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Available in both: print and electronic (printable pdf) formats. Copyright © 2013 by International Frequency Sensor Association. All rights reserved. http://www.sensorsportal.com/ SSeennssoorrss && TTrraannssdduucceerrss JJoouurrnnaall CCoonntteennttss Volume 159 Issue 11 November 2013 www.sensorsportal.com ISSN 2306-8515 e-ISSN 1726-5479 Research Articles A Method of Using Digital Image Processing for Edge Detection of Red Blood Cells Jinping Li, Hongshan Mu, Wei Xu.............................................................................................. 1 Research and Implementation of Pattern Recognition Based on Adaboost Algorithm Luqun Chang, Zhengfu Bian ...................................................................................................... 7 Image Based Computer-Aided Manufacturing Technology Zhanqi Hu, Xiaoqin Zhang, Jinze Li, Wei Li ............................................................................... 13 A Detection Algorithm for Image Copy-move Forgery Based on Improved Circular Projection Matching and PCA Yanfen Gan, Jing Cang.............................................................................................................. 19 Lossless Compression of Grayscale Digital Image Based on Two-Dimensional Differential Prediction Algorithm Wei Liang, Guangxian Zhang, Liang Tao .................................................................................. 26 Unsupervised Segmentation Method for Diseases of Soybean Color Image Based on Fuzzy Clustering Jiangsheng Gui, Li Hao, Shusen Sen, Wenshu Li, Yanfei Liu................................................... 32 Level Set Based Shape Model for Automatic Linear Feature Extraction from Satellite Imagery Yi Liu, Fansi Kong, Fei Yan........................................................................................................ 39 The Study of Remote Sensing Image Classification Based on Support Vector Machine Zhang Jian-Hua.......................................................................................................................... 46 Monitoring Data Cleaning of Urban Tunnels by Fusing PCA and CLARA Algorithms Kun Hao Tang, Luo Zhong, Lin Li, Guang Yang........................................................................ 54 A Framework for Detecting the Self-heating Source in Oil Tank Hui Liu, Yunfei Hu ...................................................................................................................... 60 Numerical Simulation of Flame Temperature Field in Rotary Kiln Gongfa Li, Jia Liu, Hegen Xiong, Jianyi Kong, Zhen Gao,Yikun Zhang, Wentao Xiao, Fuwei Cheng .............................................................................................................................. 66 Research on Temperature Field and Stress Field of Prefabricate Block Electric Furnace Roof Wentao Xiao, Gongfa Li, Guozhang Jiang, Jianyi Kong, Jia Liu, Shaoyang Shi, Yikun Zhang, Fuwei Cheng, Tao He.................................................................................................... 74 http://www.sensorsportal.com/ Influence Factors on Stress Distribution of Electric Furnace Roof Jia Liu, Gongfa Li, Guozhang Jiang, Jianyi Kong, Shao Yang Shi, Yikun Zhang, Wentao Xiao, Fu Wei Cheng, Tao He........................................................................................ 80 Measurement and Control System of Self-propelled Levelling Machine Based on Inclination Sensor and Laser Liu Jiangtao, Cui Baojian, Jiang Haiyong, Yi Jinggang ............................................................. 87 Research and Key Bearing Part Simulation of Finite Element Analysis Platform of Gantry Crane Based on ANSYS He Bin-Hui .................................................................................................................................. 92 Research on the Rock Fragmentation Under Static and Dynamic Loads Peng Qing .................................................................................................................................. 100 Dynamic Crack Propagating Mechanism of Rock Materials Based on Different Weighted Functions Huijun Wu, Jing Zhao, Zhongchang Wang, Xunguo Zhu, Deshen Zhao................................... 107 Study on Multi-environment Factor Monitoring System of the Livestock Breeding Xiao Yu, Hai-Ye Yu .................................................................................................................... 113 Multichannel Seismic Deconvolution Using Bayesian Method Li Yanqin, Peng Hongwei, Yu Ruihong...................................................................................... 120 Towards a Confidence-based Routing Algorithm in Delay Tolerant Network Li-Xia Liu, Xiao-Hua Qiu............................................................................................................. 126 ANN RBF Based Approach of Risk Assessment for Aviation ATM Network Lan Ma, Deng Pan, Zhijun Wu................................................................................................... 132 RBF Neural Network Combined with Knowledge Mining Based on Environment Simulation Applied for Photovoltaic Generation Forecasting Dongxiao Niu, Ling Ji, Xiaomin Xu, Peng Wang........................................................................ 138 An Efficient Optimization Algorithm for Super High Dimensional Numerical Function Inspired by Cellular Differentiation Yanjiang Wang, Chengna Yuan, Hui Li, Yujuan Qi ................................................................... 143 Method of Optimal Scheduling of Cascade Reservoirs based on Improved Chaotic Ant Colony Algorithm Hongmin Gao, Baohua Xu, Zhenli Ma, Lin Zhang, Chenming Li............................................... 149 An Achievable Rate Region for Relay Multiple Access Channel Based on Decode-and-Forward Xiaoxia Song, Yong Li................................................................................................................ 155 Method of Reservoir Optimal Operation Based on Improved Simulated Annealing Genetic Algorithm Chenming Li, Baohua Xu, Hongmin Gao, Xueying Yin, Lizhong Xu ......................................... 160 Conceptual Analysis of Node Application Program of Semantic Reasoning Network Shi Yun Ping .............................................................................................................................. 167 Solution to Degree Diameter-2 Graph Problem in Parallel Machine Tools Control Network Based on Genetic Algorithm Xiang Chen, Jun-Yong Tang, Yong Zhang................................................................................ 174 Water Inrush Source Identification of Mine Based on D-S Evidence Theory Jianyu Xiao, Aili Yang ................................................................................................................ 179 Design and Application of Counter's Interface IP Core Based on Avalon Bus Huazhu Wu, Chunguang Zhang, Naihao Luo............................................................................ 185 Research on a Novel Deadbeat Hybrid Flux Observer Guifeng Wang, Jianguo Jiang, Shutong Qiao, Fangtian Zhu .................................................... 190 A Multilevel SVPWM Algorithm for Linear Modulation and Over Modulation Operation Wei Wu, Jianguo Jiang, Guifeng Wang, Shutong Qiao, He Liu................................................. 198 The Intelligent Fiber Knitted Fabrics Development and Function Test Chen Guofen, Yang Lefang ....................................................................................................... 206 Loading and Unloading Manipulator Controlled by Built-in PLC in CNC System Hu Fuwen................................................................................................................................... 212 Blind Separation of Noisy Mixed Speech Based on Wiener Filtering and Independent Component Analysis Hongyan Li, Xueying Zhang....................................................................................................... 218 Support Vector Machine Based Intrusion Detection Method Combined with Nonlinear Dimensionality Reduction Algorithm Xiaoping Li ................................................................................................................................. 226 A Kind of Network Intrusion Detection Algorithm Based on Quantum-behaved Particle Swarm Optimization Qiang Song, Lingxia Liu ............................................................................................................. 230 A Mechanism of Initiative Transmission to Send Message on WebGIS Luo Xiangang, Xie Zhong, Luo Jin............................................................................................. 236 Parallel Computing of Polymer Chains Based on Monte Carlo Method Hong Li, Bin Gong, Chang-Ji Qian, He-Bei Gao........................................................................ 242 Research on the Evaluation of Low Carbon Economic Development by Fuzzy Algorithm Wang Fan................................................................................................................................... 249 XML Data Retrieval Model Based On Two-dimensional Table Datasets Lichuan Gu, Qingyan Guo, Youhua Zhang................................................................................ 255 The Reasoning Mining of Inner-outer Unknown Information Base on Dynamic Packet Sets Xiaojuan Wang, Yang Wang...................................................................................................... 263 New Exponential Strengthening Buffer Operators and Numerical Simulation Cuifeng Li, Huajie Ye, Zhengguo Weng..................................................................................... 271 An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model Zhehuang Huang ....................................................................................................................... 277 Study Horizontal Screw Conveyors Efficiency Flat Bottomed Bins EDEM Simulation Yanping Yao, Wenjun Meng, Ziming Kou.................................................................................. 282 Decoupling Research of a Three-dimensional Force Tactile Sensor Based on Radical Basis Function Neural Network Feilu Wang, Xin Sun, Yubing Wang, Junxiang Ding, Hongqing Pan, Quanjun Song, Yong Yu, Feng Shuang............................................................................................................. 289 Optimization of Power Allocation for a Hybrid Wind-Hydro Power System Xuankun Song, Hui Zhou, Zhi-Juan Shang, Rong Cong ........................................................... 299 Dynamical Modeling and Optimization of the Roll Forming Machine based on the Particle Swarm Optimization with Negative Gradient Na Risu, Li Qiang ....................................................................................................................... 307 Optimal Design Based on Rough Set and Implementation of Worm Gear in Valve Actuator Fan Chang Xing, Wu Qiang ....................................................................................................... 313 Symmetrical Structure Strong Drive Capability Optocoupler Sensor Lei Tian, Xinquan Lai ................................................................................................................. 319 Research and Calibration Experiment of Characteristic Parameters of High Temperature Resistance Strain Gauges Wang Wen-Rui, Zhang Jia-Ming, Ren Xin, Nie Shuai ............................................................... 324 Research in Algorithm of Image Processing Used in Collision Avoidance Systems Hu Bin......................................................................................................................................... 330 A Study and Analysis on a Perceptual Image Hash Algorithm Based on Invariant Moments Hu Bin......................................................................................................................................... 337 Application of a Force Sensor in Wire Bonding Process Lei Zhou, Jiangang Li, Lanhui Fu, Zexiang Li ............................................................................ 345 Automatic Measurement and Monitoring Technology for Oil Well Qibin Yang, Wei Sun, Dazhong Ren.......................................................................................... 351 Design and Practical Application of the Solar Radiation Simulator Changwu Xu, Kaicheng Huo, Zhigang Ren ............................................................................... 358 Electrical Power System Harmonic Analysis Using Adaptive BSS Algorithm Chen Yu, Liu Yueliang ............................................................................................................... 364 Nanomanipulators with Reduced Hysteresis and Interferometers Build in NanoFabs Petr Luskinovich, Vladimir Zhabotinskiy .................................................................................... 369 The study of Sensors Market Trends Analysis Based on Social Media Shianghau Wu, Jiannjong Guo .................................................................................................. 374 Refractive Index Sensing by Using Nano Fiber Coupler Liu Tiedong ................................................................................................................................ 379 A Multiple Factors Safety Prediction Algorithm Based on Genetic Neural Networks in Coal Mine Safe-state Qi Li-Xia...................................................................................................................................... 385 The Radar Tomography Detection for the Abnormal Moisture Regions of Huge Grain Pile Su Yanping, Lian Feiyu .............................................................................................................. 391 Physiological State Monitoring System of the Home Elderly Based on Multi-frequency Narrowband Power Line Communications Xujia Wang, Liang Dong ............................................................................................................ 397 Strength Study of Spiral Flexure Spring of Stirling Cryocooler Wang Wen-Rui, Nie Shuai, Zhang Jia-Ming .............................................................................. 404 Design for Crack Detection System of Wall in Houses Based on SCM Liu Tianye................................................................................................................................... 409 Vector Quantization Codebook Design and Application Based on the Clonal Selection Algorithm Mengling Zhao, Hongwei Liu ..................................................................................................... 415 Study of 3D Wireless Sensor Network Based on Overlap Method Wu Mingxin ................................................................................................................................ 422 Experimental Study on the Engineering Characteristics of Lime Soil with Different Lime Content Sun Xiao, Zhao Mingjie, Wang Kui, Lin Junzhi .......................................................................... 431 Feasibility Research on the System of Real-time Traffic Information Between Taxis and Passengers Chao Wang, Yun Cai, Yina Zhang, Wei Jie Sun ....................................................................... 437 Collision Energy Dissipation Calculation and Experiment for Impact Damper with Particles Xiao Wang-Qiang, Li Wei........................................................................................................... 442 Longitudinal Ultrasonic Guided Waves for Monitoring the Minor Crack of Rotating Shaft with Galfenol Transducer Xiaoyu Wang, Jun Zou, Fuji Wang, Ronghua Li ........................................................................ 450 A Method Based on the Improved Matrix Pencil Algorithm Designed for Voltage Flicker Detection Chen Shi, Li Xing-Yuan, Zhu Rui-Ke, Luo Xiao-Yi..................................................................... 457 Research on an Approach to Ultrasound Diffraction Tomography Based on Wave Theory Hao-Quan Wang, Le-Nan Cao, Hui-Zhen Chai ......................................................................... 464 Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail: editor@sensorsportal.com. Please visit journal’s webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm International Frequency Sensor Association (IFSA). Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 1-6 1 SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsss © 2013 by IFSA http://www.sensorsportal.com A Method of Using Digital Image Processing for Edge Detection of Red Blood Cells 1 Jinping LI, 2 Hongshan MU, 2 Wei XU 1 Software School, East China Institute of Technology, 330013, China 2 Economic Development Zone Guanglan Avenue 418, Nanchang330013, China 2 Tel.: 13699532208 2 E-mail: muhongshan@126.com Received: 22 July 2013 /Accepted: 25 October 2013 /Published: 30 November 2013 Abstract: Using a series of digital image processing methods, such as gray stretch, median filter, threshold segmentation, edge extraction and detection, detect the variations of red blood cells, realize the goal of identifying the shapes of variable red blood cells, and good results have been achieved. In conclusion, the average detection rate of abnormal red blood cells is above 80 %. This inspiring and conductive method is a tentative/experimental research which will play a good demonstration role in further application of image processing and detection in medical field. Copyright © 2013 IFSA. Keywords: Image processing, Morphological, Red blood cells, Detection. 1. Introduction With the development of information technology, image processing technology is becoming an essential and effective tool in scientific research. It is especially widely used and effective in the field of biomedical engineering. Besides CT technique of digital image processing, it is also widely used in medical diagnosis, such as chromosome analysis, cancer cell detection, etc [1-4]. According to geometric features obtained of the red blood cells, we can detect and research the pathological red blood cells. The method will play a good demonstration role for further application in the field of image processing technology in medicine. 2. Experimental Methods 2.1. Experimental Material The image samples of medical red blood cells (provided by people’s hospital in Nanfeng County, Jiangxi Province). 2.2. Experimentation 2.2.1. The Grey Image Stretching of the Red Blood Cells While being a way of image linear transformation, the grey image stretching can greatly improve the visual effect for us. The gray level of all Article number P_1529 http://www.sensorsportal.com/ Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 1-6 2 points in the image is transformed according to linear transformation function, which is one dimensional linear function [1]. BA fxfxf += *)( (1) For gray level transform equations: BAAAB fDfDfD +== *)( (2) The parameters fA is the slope of the linear function, fB is the y-axis intercept, DA shows the grayscale of the input image, and DB shows the grayscale of output image. While fA>1, the contrast of the output image will be increased; While fA<1, the contrast of the output image will be reduced; while fA =1 and fB≠0, the gray value of all the pixels will go up or down, and its effect is to make the image darker or brighter; If fA<0, dark areas will be brighten, and bright areas will be darken, complementary operations of the images are completed by the point operation. In a particular case, while fA=1, fB=0, the output image is the same as the input figure; While fA=-1, fB=255, the grayscale of the input image and the output image is precisely reversed [5]. The Original Red Blood is shown in Fig. 1. Fig. 1. The original red blood. The Enhanced Image by the Gray Stretch is shown in Fig. 2. Fig. 2. Red blood cell image by gray stretch. 2.2.2. The Mean Filter of the Red Blood Cell Image Median filter of image is a kind of enhancement technique of image spatial domain filtering [1], which can reflect the texture characteristics of the spatial image, such as physical location, shape, size, and so on. The mean value of all pixels in the field is assigned to the output corresponding pixels so as to achieve the purpose of smoothing. 3×3 templates are adopted in this paper, and average filtering process is shown in Fig. 3. Fig. 3(a) shows a small part of an image, with a total of 9 pixels. Pi (i= 0, 1... 8) shows the grey value of pixels; Fig. 3(b) shows a 3×3 template, and Ki (i = 0, 1... 8) is called template coefficient; Odd numbers (such as 3×3, 5×5) are generally taken for the consideration of template size, and the median filter can be divided into the following steps: 1) Make Ki (i= 0, 1... 8); 2) Make the template roam in the image, and make pixels of k0 and p0 overlap in Fig. 3. Gray value r0 can be calculated by the next type of output image which is corresponding to pixel p0 (as shown in Fig. 3(c); 3) All grey values of the pixels in the enhanced image can be obtained by calculating each pixel according to the type of Fig. 3(c). The process of the median filter can be applied to all the spatial filtering methods, that is to say, the function of the spatial filter is realized in the process of each pixel area through applying template convolution method. Fig. 3. Average filtering process. In order to remove noises, the image with a 3×3 templates has used the smooth processing operation. Results are shown in Fig. 4. Fig. 4 (a). Red blood cell image median filter smoothing, the image before smoothing. Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 1-6 3 Fig. 4 (b). Red blood cell image median filter smoothing, the image after smoothing. 2.2.3. Threshold Segmentation of Red Blood Cell Image Threshold segmentation is a kind of regional segmentation technology [2], which can make the image gray level split into two or more gray intervals according to the user specified. Then using the differences in the gray level between extraction of target objects and the background, we choose an appropriate threshold value. By judging whether or not each pixel in the image meets the requirements of threshold value, we determine which area the pixels in the image belong to, the target area or background region. One of the commonly used threshold processing method is binarization processing of the image. Select a threshold then convert it to black and white binary image, which is pretreated by image segmentation and edge tracing, etc. Using the threshold value method of human-computer interaction and windows applications [6], we got the following red blood cells threshold segmentation image, see Fig. 5. 2.2.4. Image Edge Detection and Extraction Edge usually refers to the collection of those surrounding pixels which have a step change or roof change, and it is also an important characteristic on which image segmentation depends. The method of Laplace operator and Sobel operator are respectively used to sharpen the red blood cells [1, 7], and the following respective images can be got as in Fig. 6. Fig. 5(a). Threshold segmentation of red blood cell image before threshold segmentation. Fig. 5 (b). Threshold segmentation of red blood cell image after threshold segmentation. (a) Image before sharpening. (b) Image after sharpening. Fig. 6. Laplace sharpening processing of the red blood cell image. 2.2.5. Red Blood Cell Image Processing 2.2.5.1. The Geometrical Characteristics of the Red Blood Cell Image Normal mature red blood cells are reddish or orange, with the shape of a disc, the characteristics of concentric undertint and pale center, the diameter of its light coloured area is about 1/3 of the diameter of the red blood cells. Red blood cell image samples chosen for test are shown in Fig. 7, the labeled cells are to be detected, which are random sampling of the red blood cells. Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 1-6 4 (a) Image before sharpening. (b) Image after sharpening. Fig. 7. Sobel sharpening processing of the red blood cell image. First, the software interface of image as shown in Fig. 9 is processed by gray level stretch, median filter, threshold segmentation and prepared for the following extraction of the single red blood cells. After getting the red blood cell images with greater contrast which have been removed noises, the Windows XP system with a drawing software is used to extract the selected red blood cells images [6]. Number and arrange the selected red blood cells images, then a new arrangement of red blood cells images appears as shown in Fig. 10. Fig. 8. The original red blood cells. Fig. 9. Software interface of image processing. Fig. 10. Red blood cells of image selected to be detected. 3. Results and Analysis Detect the edges of the red blood cell images according to the images as shown in Fig. 11, we get detection results of the first level (as shown in Fig. 12). In tests one, according to Fig. 12, we can see that red cells No. 15 and No. 17 are rectangular, not like a disc as normal red blood cells in medical science, therefore, we can conclude that the two red blood cells are abnormal. In tests two, through binarization process the single red blood cells are extracted, as shown in Fig. 13, and are prepared for the next calculation of geometrical characteristics of red blood cells. Fig. 11. Edge detection of red blood cell Image to be detected. Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 1-6 5 Fig. 12. Edge detection of the red blood cell image chosen. Fig. 13. Binarization Processing of images before red blood cells Detection. According to the binarization images in Fig. 13, observe the blood cells erythrocyte shallow areas, the cells No. 1, 3, 4, 5, 7, 8, 9, 11, 12, 13 can be observed with no shallow areas, or their light colored areas are smaller than 1/3 of the diameter of the red blood cells, so we can conclude that these red blood cells are abnormal. In tests three, respectively calculate the geometrical characteristics of the red blood cells after binarization processing in Fig. 14. Data aggregation of the red blood cell geometric characteristics is shown in Fig. 15. Fig. 14. Calculation of the red blood cell geometrical characteristics. Fig. 15. Data aggregation of red blood cells to geometric features. With the software used in this experiment, we get the result that the average area of the normal red blood cells is 830 or so, but average error range of red blood cells No. 20 and No. 23 is more than 100, so they can be regarded as abnormal red blood cells. Finally, the normal red blood cells detected are shown in Fig. 16. Fig. 16. the normal red blood cells image. 4. Conclusions As we can see, the abnormal rate of the medical red blood cell image samples was 70 %, which was provided by the hospital in Fig. 8. However, the abnormal rate of red blood cells in the image we get in this experiment was 62.5 %. Therefore, we can basically conclude that the average detection rate of abnormal red blood cells in this study is more than 80 %. In short, through the image processing and detection process, using a variety of image processing technologies, we completed the extraction of single red blood cells, realized the detection of abnormal red blood cells, and achieved good results. But for red blood cell image detection there are still some problems to be solved: 1) Some of the discriminate error rates are still high, because only geometric features are used for analysis, while color, texture, the proportion of the internal structure factors were not considered; 2) Errors existing in the detection process certainly have some effect on the experimental results; Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 1-6 6 3) To facilitate testing and ensure higher detection rate, red blood cell images without overlapping are selected in this study, tests for overlapping cells will be explored with new treatment methods in the future. Acknowledgements We would like to thank Jiangxi Department of Education of Science and Technology Plan Projects (GJJ11490). 4. References [1]. Fu Desheng, Graphic image processing, Southeast University Press, Nanjing, 2001. [2]. Nie Bin, Medical image segmentation technology and its progress, Mount Taishan Medical School Journal, Vol. 23, No. 4, 2002, p. 422-426. [3]. Tian Ya, Rao Nini, Pu Li Xin, The latest dynamic of domestic medical image processing technology, Journal of University of Electronic Science and Technology, Vol. 3, No. 2, 2001, pp. 3-9. [4]. Jia Minyi, Diagnostics, People's Medical Publishing House, Beijing, 1981. [5]. M. Christgan, K. A. Hiller, G. Schmalz et al., Accuracy of quantitative digital subtraction radiography for determining changes in calcium mass in mandibular bone, Journal of Periodontal Researches, Vol. 33, Issue 3, 1998, pp. 138-149. [6]. Cheng Wenbin, Jin Xiangfeng, Visual C++ utility, Beijing University of Aeronautics and Astronautics Press, Beijing, 1995. [7]. Xiao Yi, Long Mei, Ni I, Li Hongyang, Computer application in medical image processing, Medical Education and Technology of China, Vol. 15, No. 4, 2001, pp. 203-204. ___________________ 2013 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com) Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 7-12 7 Sensors & Transducers © 2013 by IFSA http://www.sensorsportal.com Research and Implementation of Pattern Recognition Based on Adaboost Algorithm 1 Luqun CHANG, 2 Zhengfu BIAN 1 Ji’nan Research Institute of Geotechnical Investigation & Surveying, 59 Lishan Road, 250013, Ji’nan, Shandong Province, China 2 Institute of Land Resources, China University of Mining and Technology, Xuzhou, Jiangsu Province, China Received: 28 August 2013 /Accepted: 25 October 2013 /Published: 30 November 2013 Abstract: Pattern recognition and computer vision technology as a long-term subject of concern, which has high academic value and commercial value. Adaboost is an iterative algorithm, and its core idea is to obtain some weak classifier with a training set of training. Finally, a much stronger classifier is obtained by combining weak classifiers. In this paper, we firstly introduce the basic theory of Adaboost algorithm, and then take face recognition as an application example, the training process and the detection process were achieved respectively and independently. Experimental results show the detector based on Adaboost algorithm can accurately detect the location of the face, regardless of their positions, scale, orientation, lighting conditions, expressions, etc., and it has a smaller detection error. Specifically, the detector can effectively detect multiple faces, and it also has much higher detection accuracy. Copyright © 2013 IFSA. Keywords: Pattern recognition, Computer vision, Face detection, Adaboost algorithm. 1. Introduction Pattern detection algorithm has been developed over the past decades. Each detection algorithm is developed in a particular application context, and so we can analysis these few of detection methods into two main types: image based methods and feature based methods. The first method always uses classifiers trained statically with a given sample set. Then each classifier is scanned through the samples set. The other method locates by detecting particular features. Pattern detection algorithm used is both image based and feature based. It is image based in the sense that the method uses a learning algorithm to train the classifier with some chosen trained positive and negative samples. And it is also feature based because the lots of features chosen by the learning algorithm are directly related to the particular features. The boosting techniques improve the performances of base classifiers by re-weighting the training examples. Learning using Boosting is the main contribution of Pattern detection. The important issue of face information processing technology has always been the pattern recognition cut the field of machine vision research concern, is one of the important components of this stage based on biometric identification technology. What’s more, face as images and video of the most important visual objects, one in computer vision, pattern recognition, multimedia technology research occupies an important position. Face detection and retrieval of information processing of human face and retrieval based on content. In recent years, a very active direction in intelligent man-machine interface, it has a very wide range of applications, content- based retrieval, the digital word video processing, security and other fields [1-2]. Article number P_1530 http://www.sensorsportal.com/ Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 7-12 8 In recent years, face detection has made considerable development. Amit et al. had presented a method for shape detection, and then this method was applied to detect frontal-view faces in still intensity images [3]. Viola et al. proposed a new detection method based on the integral image features and Adaboost algorithm [4]. Speed and performance of this cascade classifier is equivalent of ANN method proposed by Rowley [5]. Later, Li's research group developed this method for multi-view face detection [6]. Kauth et al. proposed a blob representation to extract a compact, structurally meaningful description of multispectral satellite imagery. Craw et al. proposed a located method based on a shape template of a frontal-view face, and a Sobel filter is first used to extract edges. These edges features are grouped together to search for the template of a face based on several constraints. These above methods all have better face detection performance. Recently, many researchers started to use Adaboost algorithm in Pattern detection. Adaboost is an iterative algorithm, and its core idea is to obtain some weak classifier with a training set of training. Finally, a much stronger classifier is obtained by combining weak classifiers. In this paper, we firstly introduce the basic theory of Adaboost algorithm, and then the training process and the detection process were achieved respectively and independently. Experimental results show the detector based on Adaboost algorithm can accurately detect the location of the face, and it has a smaller detection error. Specifically, the detector can effectively detect multiple faces, and it also has much higher detection accuracy. 2.Adaboost Algorithm 2.1. Adaboost Review Adaboost algorithm is based on gray-scale distribution of target features, which chooses to use the Haar characteristics [4-7]. Haar feature is based on the characteristics of the integral image, and this feature is mainly used in the gray scale image. Its advantages consist of calculation simpler and faster extraction. Adaboost algorithm first extracts image Haar characteristics, and then through the training process to obtain Haar feature is converted into many weak classifiers, and finally these weak classifiers are optimized combination to use for face detection. Fig. 1 shows the flow chart of detection based on Adaboost algorithm. Integral image snapped original any point in the upper left pixel in the image obtained by adding the pixel value as the current point image. The accumulation of all the pixel values of the upper left portion of the integral image for each point (x,y) of the midpoint of the value of the original image (x,y): ∑ ≤≤ = 00 , ),(),( yyxx yxiyxii (1) where i(x,y) is the original image, ii(x,y) is the integral image. Fig. 2 shows the process of computing integral image. Fig. 3 shows an example of integral image. Fig. 1. The flow chart of face detection based on Adaboost algorithm. (a) image data (b) integral image data Fig. 2. The data of the integral image. (a) original image (b) integral image Fig. 3. A specific example of integral image. According to the characteristics of the integral image, the sum value of the arbitrary rectangular region of pixels can be calculated by using the formula (1) with a quick computing process and the computation time is fixed. The advantage of this feature to design Haar feature extraction and the machine calculation time fixed. It is because of Haar feature extraction speed is fast enough, it making the Adaboost detection algorithms has become one of the fastest detection algorithms. Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 7-12 9 As we all know, common Haar feature is designed according to the characteristics of regional gray contrast. Fig. 4 shows four types of classical Haar features. Haar features of this reflects the characteristics of the image in grayscale distribution characteristics bow l into the face detection problem which, the problem is converted into how to find a better Haar feature to describe the characteristics of the image gray distribution. Adaboost algorithm selects from a large number of Haar characterized the optimal characteristics, and convert it into the corresponding weak classifier classification used, so as to achieve the purpose of the target classification. Adaboost algorithm training process is to select the process of the weak classifiers. (a) feature A (b) feature B (c) feature C (d) feature D Fig. 4. Four types of classical Haar features. 2.2. Training Process Each Haar feature is corresponding with one of the weak classifiers, but not any Haar feature can be better described gradation distribution of certain characteristics. There is a key research object to be solved how to select optimal Haar characteristics and then produce into a classifier for detection from a large number of Haar features in Adaboost algorithm training process. The requirements of the training sample face close-up image, but vastly different face shape, so the training sample selection process to take into account the diversity of the sample. Training samples need to preprocess before using to train. Generally speaking, training samples pre-treatment does not require special algorithm, but the sample human face gesture to try to be consistent. Fig. 5 shows a set of training samples. Firstly, we can extract Haar feature for face image from training set. Then weak classifiers are generated based on features. Each Haar characteristic is corresponding to a weak classifier, and each weak classifier is based on the parameters of its corresponding Haar characteristics defined. By using the position information of the above Haar characterized statistical training samples can be obtained corresponding to the characteristic parameters. The weak classifiers definition formula is as follows: ⎩ ⎨ ⎧ ≤ = otherwise pfpif xh jjjj j 0 1 )( θ , (2) where the characteristic parameters pj represent inequality direction, θj is threshold. Weak classifiers can be divided into different ways according to statistics, the value of single-domain weak classifiers and dual-domain values of weak classifiers. Fig. 5. A set of ORL face training samples. As defined in the current Haar characterized by the statistics of the training sample average of positive samples and negative samples: 1+pjθ , 1−pjθ . So we can obtain 2/)( 11 −+ += pp jjp θθθ . Assume 11 −+ > pp jj θθ , so 1+=jp , otherwise 1−=jp . The output result of the weak classifiers to 1 or 0, and outputs 1 represents a judgment is true, that is a face image; on the contrary, this is false, i.e. a non-face image. Single weak classifier limited capacity, and does not handle objects, so would its group and into a strong classifier. Next, we will describe the training process. Adaboost algorithm training process is the selection of optimal weak classifiers, and given the weight of the process [8-9]. Fig.6 shows the training process of Adaboost algorithm. The specific training algorithm steps are as follows: 1) Label n training samples, where m samples are labeled 1+=jy , and n – m non-face sample are labeled 1−=jy ; Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 7-12 10 2) Initialize the weights. The original weights of each face sample are set as:  m w p ∗ =+ 2 1 10 , non-face samples are set as: )(2 1 10 mn w p −∗ =− ; Fig. 6. The training process of Adaboost algorithm. 3) Select T weak classifiers (T iterations) a) In the t-th iteration, compute the iteration error sum of the j-th weak classifier as: ∑= −= n i jijijj yxhw 1 )(ε , and choose the minimum iterative error of weak classifiers )( it xh . Compute t t t ε εβ − = 1 , and set t t β α 1log= , tα is the weight of weak classifier; b) Use )( it xh and tβ to update all weights: i pp titit ww εβ − + = 1 1 . If i-th sample is correct classification, so 0=iε ; otherwise 1=iε ; c) Normalize weights: ∑ + + + = it it it p p p w w w 1 1 1 ; d) Set 1+= tt . 4) A strong classifier is obtained by linear combination of a number of weak classifiers: ⎪ ⎩ ⎪ ⎨ ⎧ > = ∑ ∑= = otherwise xh xh t t 0 2 1)( 1 )( T 1t T 1 t t α α (1) Single weak classifier has poor classification results, and the training initial error is 0.15, followed by a gradual rise. So we need the combine the weak classifiers into a strong classifier to make better performance. 2.3. Cascade Classifier A strong classifier can be obtained through using a combination of some of the weak classifiers by equation (3), and each strong classifier will have more performance to detect face. If a plurality of strong classifiers are cascaded together, then by the strong classifier at all levels of the detected object is the possibility of the human face is also the largest. According to this principle, Adaboost algorithm introduces a waterfall-type classification is an associated classifier. The flow of detection algorithm based on cascade classifiers as Fig. 7. Fig. 7. The flow of detection algorithm based on cascade classifiers. The cascade classifier combines several strong classifiers to grade series together, and strong classifier level is complex than other and strict than others. Detection of non-face images in the front is ruled out to only face images detected by the strong classifier at all levels. In addition, because the non- face images are eliminated in the first few levels of the cascade classifier, it can speed up the detection speed of detection algorithm. 3. Face Detection Based on Adaboost Algorithm The cascade classifier performance, in order to be able to use the image detection records cascade classifier needs to design a detection mechanism, and its design processing interface. In Order to be able to detect the size of various scales the human face, the need to introduce here the detection mechanism of the multi-scale. There are several commonly used scale variation methods, but in order to ensure the detection speed, there are two methods available: One method to implement scale transformation to the classification, and also it needs to change the field values of the weak classifiers. Another method is to sample to the image at different scales, and this method is simple to implement, but a little time- consuming than the former method. A flow of the detection process is shown in Fig. 8. Adaboost algorithm based face detection processing Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 7-12 11 is a gradation data, so the detection of the first step is to be detected image is converted into a grayscale image. Fig. 8. A flow of the detection process. As see from Fig. 8, the second step is grayscale images integral image. The third step of the integral image detection at different scales, in different scales, the detection result of the merger. The fourth step is output after the detection results under different scales. On the same scale, when the overlapping part of the two sub-windows detected face, you need to consider whether there is the need to merge. According to the experimental results, we need to combine overlapping sub-window when the overlapping part of the face sub-window is over the current window size of 0.5. This combined method is to obtain averaging values. In addition, we also need to combine when the detected windows are overlapped in different scales. Generally in the vicinity of the location adjacent scales duplicate detection, this will not only lead to repeat testing, but also may cause unnecessary error detection results. 4. Experimental Verification In this section, many experimental results are showed to verify the effectiveness of Adaboost detection algorithm. Firstly, we implement the detection on IMM face database. Fig. 9 shows some detection results on IMM face database. From Fig. 9, we can see that the detector can accurately detect the location of the face, and it has a smaller detection error. Then the detector is implemented on some network picture. Fig. 10 shows some detection results on faces set from network. We can also find the detector can accurately detect the location of the face. Then we verify the effectiveness of Adaboost detection algorithm to detect face on some images with multiple faces. Fig. 11 shows some detection results on images with multiple faces. From experimental results, we can easily find the detector can effectively detect multiple faces, and it also has much higher detection accuracy. Fig. 9. Detection results on IMM face database. Fig. 10. Detection results on faces set from network. Fig. 11. A few of faces in an image. In addition, we also need to combine when the detected windows are overlapped in different scales. Generally in the vicinity of the location adjacent scales duplicate detection, this will not only lead to repeat testing, but also may cause unnecessary error detection results. Then some experimental results show the combining process for different windows. Fig. 12 shows an example of how to merge detection result: Fig. 12 (a) is the result of a plurality of windows overlapped in the same scale; Fig. 12 (b) is a plurality of windows overlapped with each other at different scales, and Fig. 12 (c) is the final combining result. Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 7-12 12 (a) Overlap in same scale (b) Overlap in different scales (c) Combine results Fig. 12. Results of combining windows. 5. Conclusions Face detection in pattern recognition and machine vision technology as a long-term subject of concern, which has high academic value and commercial value. The rapid development of related face technologies, face detection as a key step, has causing more and more attention of researchers and research. Adaboost is an iterative algorithm, and its core idea is to obtain some weak classifier with a training set of training. Finally, a much stronger classifier is obtained by combining weak classifiers. In this paper, we firstly introduce the basic theory of Adaboost algorithm, and then the training process and the detection process were achieved respectively and independently. Experimental results show the detector based on Adaboost algorithm can accurately detect the location of the face, and it has a smaller detection error. Specifically, the detector can effectively detect multiple faces, and it also has much higher detection accuracy. References [1]. Ming-Hsuan Yang, D. J. Kriegman, N. Ahuja, Detecting faces in images: a survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, Issue 1, 2002, pp. 34-58. [2]. Hyobin Lee, Seongwan Kim, Sooyeon Kim, Sangyoun Lee, Face detection using multi-modal features, in Proceedings of the International Conference on Control, Automation and Systems, 2008, pp. 2152-2155. [3]. Y. Amit, D. Geman, B. Jedynak, Efficient focusing and face detection, Face Recognition: From Theory to Applications, Vol. 163, 1998, pp. 124-156. [4]. P. Vioia, M. Jones, Rapid object detection using a boosted cascade of simple features, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, 2001. [5]. H. A. Rowiey, S. Baiuja, T. Kanade, Neural network- based human face detection, IEEE Transactions on Pattern Anaiysis and Machine Intelligence, Vol. 20, Issue 1, 1998, pp. 23-38. [6]. Z. Zhang, S. Z. Li, H. Zhang, Real-time multi-view face detection, in Proceedings of the Conference on Automatic Face and Gesture Recognition, Washington DC, USA, 2002, pp. 149-154. [7]. A. Treptow, A. Zell, Combining Adaboost learning and evolutionary search to select features for real- time object detection, in Proceedings of the International Conference on Evolutionary Computation (CEC’2004), Vol. 2,2004, pp. 2107-2113. [8]. Zhen Qiu Zhang, Mingling Li, S. Z. Li, Hongliang Zhang, Multi-view face detection with FloatBoost, in Proceedings of the 6th IEEE Workshop on Applications of Computer Vision (WACV’ 02), 2002, pp. 184-188. [9]. Yong Ma, Xiaoqing Ding, Real-time rotation invariant face detection based on cost-sensitive Adaboost, in Proceedings of the International Conference on Image Processing (ICIP’2003), Vol. 2, 2003, pp. 921-924. ___________________ 2013 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com) Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 13-18 13 SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsss © 2013 by IFSA http://www.sensorsportal.com Image Based Computer-Aided Manufacturing Technology 1 Zhanqi HU, 2 Xiaoqin ZHANG, 2 Jinze LI, 1 Wei LI 1 College of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China 2 Mechanical and Electrical Engineering College, Hebei Normal University of Science and technology, Qinhuangdao, Hebei, 066004, China 1 Tel.: 0335-8057031, fax: 0335-8074783 E-mail: ronghu118@163.com Received: 19 August 2013 /Accepted: 25 October 2013 /Published: 30 November 2013 Abstract: Image based manufacturing technique is a novel manufacturing method, which is combine of machining technique and machine vision technique. By using the technique, machine tools can perform cutting process according to what they see, which is very like that the machine tool is equipped with “eyes”. In this paper, some researches of author about the subject are proposed, and key techniques are included. Construction of image based manufacturing system is introduced briefly. The geometrical model is then built from the image information, in which process shape from shading with adaptive pro-processing method is used. After the model is built, cutting path is planed, and two cutting paths, line cutting and contour cutting, are conducted. NC programs are generated automatically, and machining process is then performed. Finally a prototype system named ImageCAM is introduced. Algorithms developed in our research are verified in the system. Copyright © 2013 IFSA. Keywords: Image processing, Bitmap, Line cutting method, Contour cutting method, Layer cutting method, Shape from shading (SFS). 1. Introduction Although geometrical modeling techniques is widely used in product developing and manufacturing process, some product surface can not still be described with CAD model. In this situation, the method is required to transform the surface information into CAD model, which can be processed with CAD/CAM software. Reverse engineering (RE) can get surface date and transform the data into CAD model, but some special measurement machines are required in RE process. The measurement machines are generally expensive, and measurement process will be time consuming. When only a picture of the product can be obtained, general RE technique can not perform copping process. This is the time, when machine vision based RE system is required which can extract three dimension information from one or more pictures of a product, and transform the information into geometrical model that CAD system can accept which is subject researched in the paper. The objective of machine vision is to make computer percept three dimension environment information through two dimension picture, which can detect not only geometric information of object, but also shape, position, motion of the object. Basis of machine vision comes from image processing, model identification, and artificial intelligence [1]. Machine vision based manufacturing technique is Article number P_1531 http://www.sensorsportal.com/ Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 13-18 14 combination of machine vision technique and manufacturing technique. By using machine vision, three dimension information of object is extracted, geometric model of the object is built, CNC program is generated and machining is conducted. How to get three dimension of object and built its three dimension model are important points of machine vision based manufacturing technique. Prospect of machine vision based manufacturing technique is realizing integration of measurement, modeling, and manufacturing process, which is very like equipping “eyes” to machine tools, and making machine tools to cope the object they see. Development of machine vision technology gives solid basis to our research. SFS method [2, 3] is an important method in machine vision research. References [4-6] develop and improve SFS method, make machine vision more precision. References [7, 8] discus the boundaries between surfaces, making the curve surface smoother. But there will be much work to do for machining a work-piece depending only image of an object. Machine vision based manufacturing technique is one of advanced manufacturing techniques, which is integration of image processing, CNC technique, machining technique, and can be used in such areas as reverse engineering, fast prototype of product, manufacturing of work of art. 2. Machine Vision Based Manufacturing System Construction of machine vision based manufacturing system is shown in Fig.1, which consists of camera, image processing card, computer, and a machine tool. The camera is used to take the picture of the object which would be coped. The image processing card is used to do some pre- processing of the picture. The task of computer is to build the geometrical model of the surface, get tool path and generate CNC program. The program is transferred to CNC machine tool, with which work- piece is machined. The process is also called three dimension copying. The number of cameras depends on the machine vision method used in the system. For multiple eyes vision system, more than one camera may be used. One eye machine vision method is used in author’s research, so that there is only one camera in the system. Fig. 1. Construction of machine vision based manufacturing system. Single eye machine vision is a simple one of machine vision techniques, and also main method with which three-dimension information can be extracted from picture. Recovering three-dimension shape from simple picture is called shape from shading (SFS). SFS method is used to get three- dimension information from picture in this research. SFS method is first proposed by Horn [9], which is main algorithm in machine vision getting three dimensions from camera and is based on the fact that the change of direction of surface leads to the change of gray degree in the picture of the surface. SFS method is an algorithm which can extract three dimensions information from a few pictures of the object, especially from one picture of the object. SFS method develops very fast in recent years, and is being applied in many fields including industry. Main algorithms of SFS consists of recovering three dimensions information using sheltered boundary [10], recovering three dimensions information from normal direction of surfaces [11], and recovering three dimensions information from orthogonal polynomials [12]. Based on above researches, a new algorithm is proposed in the paper, by which three dimensions information can be recovered more precision than previous method. 3. Modeling of Work-Piece 3.1. Extracting Three-Dimension Information from a Picture In desired condition, grey of image will meet reflection map equation: ( ) ( ) ( ) 2222 11 1 ,, qpqp qqpp qpRyxI ss ss ++++ ++ == ρ , (1) where ( ) ⎟ ⎠ ⎞⎜ ⎝ ⎛ ∂ ∂ ∂ ∂= y z x zqp ,, is the normal direction of surface, ( )yxzz ,= is the equation of surface, ( )ss qp , is the direction of light source, ( )yxI , is the grey of image. Shape from shading is then calculating normal direction of surface ( )qp, from grey of image ( )yxI , . In order to solve the ill-character of reflection map equation, regulation method of depth continue is used. Global optional function is constructed: ( ) ( )( ) ( )∑∑ +−= yxyx yxFqpRyxIE ,, 2 ,,, λ (2) First item of the equation comes from Eq. (1), which means difference between actual grey value ),( yxI and the one calculated from normal parameter p and q. Regulation condition can be Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 13-18 15 expressed with continue constrain condition ),( qpF as: ( ) ( ) ( )( ) ( ) ( )( ) ( ) ( )( ) ( ) ( )( )22 22 ,1,,1, ,,1,,1, yxqyxqyxpyxp yxqyxqyxpyxpyxF −++−++ −++−+= (3) Because ( )Tqp 1,, is the normal direction of object surface, the value of Eq. (3) is the change rate of normal line direction. Therefore the smaller the value of second item in Eq. (2), the smoother of object surface is. The (x, y) in Eq. (2) is discrete coordinate of image, and summation region is a part of the image, in which all points are corresponding to the continue surface on same object. Our target is to get p(x,y) and q(x,y) which make the value of Eq. (2) the minimum. Making derivation of E in Eq. (2) to each ( )yxp , and ( )yxq , , and letting the derivation be zero, recursive formula of p and q will be derived: ( ) ( ) ( ) ( ) ( )( )( ) tt qptttt p RyxqyxpRyxIyxpyxp ,1 ,,,,,, ∂ ∂ −+=+ η (4) ( ) ( ) ( ) ( ) ( )( )( ) tt qptttt q RyxqyxpRyxIyxqyxq ,1 ,,,,,, ∂ ∂ −+=+ η (5) where 11 , ++ tt qp are the values of p and q in t+1th time recursion, and ( )yxpt , , ( )yxqt , are the average values of p and q in the neighbor region of (x,y) in tth time recursion. ( ) ( ) ( ) ( ) ( )( )1,1,,1,1, 4 1 −+++−++= yxpyxpyxpyxpyxp ttttt (6) ( ) ( ) ( ) ( ) ( )( )1,1,,1,1, 4 1 −+++−++= yxqyxqyxqyxqyxq ttttt (7) By using the recursive formula, three-dimension coordinate of an object can be calculated. This algorithm is based on continue surface, therefore recovered result is accurate only for single continue surface, for example, in Fig. 2. But for non-continue part on an object as the connecting part between two surface patch, recovered result will be very poor [13], for example, in Fig. 3. The reason is that total surface consists of several patches, although each patch is continuing, but boundary between the patches is not continued. It dose not meet the condition of above algorithm. At the boundary, normal direction of surface patches change greatly. Refraction of light makes grey values on neighbor patch are very close, and shape distortion of recovered surface takes place on the part. In order to solve the problem, SFS with adaptive pre-processing algorithm is developed in author’s research. (a) bitmap of hemisphere (b) geometrical model Fig. 2. Modeling of half sphere surface. (a) bitmap of chili pepper (b) geometrical model Fig. 3. Modeling of chili pepper. Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 13-18 16 3.2. SFS Algorithm with Adaptive Pre-processing If the attenuation character of light intensity is strengthened, the change of grey degree will increase, and recovered surface will be more like the sample surface. The main points of the algorithm is that the image is divided into some patches firstly, which is continue inside the patch, and non-continue between the patches. Then grey degree of each patch is reduced according to some rules. And last, sample surface is recovered from pre-processed patches. It is proved that surface recovered from pre-processed patches is more accurate than that recovered from original image. In order to simplify the problem, pre-processing of the image is turned into one dimension problem, and calculating process is performed line by line. Following is pre-processing procedure: 1) Parameter initialization including threshold value of grey degree ( GREY), windows threshold value (FLAG), and counter(N). 2) Reading in a line of the image, comparing grey degree of each point at the line with the GREY. If it is greater than GREY, counter N plus one, until it is smaller than GREY. 3) If the grey degree of a point is smaller than GREY, counter N is compared with windows threshold FLAG. If it is smaller than FLAG, N pixels are processed, otherwise turning to step (2) until the end of the line. 4) Scanning next line, until total image is processed. (a) chili pepper (b) vase Fig. 4. Modeling by using SFS algorithm with adaptive pre-processing. If the GREY and FLAG are selected properly, process result will be perfect after one scan. If some light green are there at edge of the image, middle value wave filtering may be needed. After the processing, higher quality geometrical model can be built, as shown in Fig. 4. High quality geometrical model is the bases if high quality CNC code. 3.3. CNC Programming Using Approximation Method of Polygon Cutting path can be derived through train code tracing to object image contour, then using adaptive including box (AIB) algorithm or approximation method of polygon. AIB method can eliminate effect of noise and get correct contour of work piece, which has been elaborated in reference [14]. Approximation method of polygon has higher precision, is the method used in this paper to generate CNC code. Information in image is of redundancy, approximation method of polygon can be used to descript a contour, which descriptions a curve contour with a polygon. In order to get proper approximating effect, an error index Emax is used to measure degree of approximation. Fig. 5. Error of approximation method of polygon. As shown in Fig. 5, assuming curve from A to B is approximated with line segment AB, letting ( ) ( ) ( )NN yxyxyx ,,,,,, 2211 be coordinate of points on curve, ( )1,,2 −= Nidi be distance from ( )ii yx , to line segment AB, we have: iNi dE 12max max −≤≤ = (8) Procedure of using approximation method of polygon is as following: 1) Determining start point of cutting path 1P , finding point maxP that is furthest point to 1P on the path. The two points divide the path into two segments. Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 13-18 17 2) Letting initial position of mP is maxP , starting from 1P , traversing curve from 1P to mP , find point iP that has maximum error maxE . 3) If maxE is greater than permitting error, let iP be new mP and turn to step (2). 4) Otherwise, iP is a vertex of polygon, 1P iP is a line segment, line CNC code (G01) is generated. Letting iP be new 1P , and maxP be new mP , turn to step (2). 5) When iP is of coincidence to maxP , the contour from 1P to maxP is processed. The contour from maxP to 1P can be processed in same way. 4. Machining Example Machine vision based manufacturing technique can be used to machine 2-D curve or 3-D curved surface. For 3-D curved surface, uncut chip is cut out layer after layer. Each layer is a plane curve with same z coordinate. In layer cutting method, 3-D machine can be turned into 2-D machining, therefore only plane cutting is discussed. This method is often called 2.5 axes machining in engineering. After geometrical model is built, CNC Programming process of layer cutting is as following: 1) Determining cutting depth, which is distance between layers. Value of cutting depth is relevant with material of work piece, cutting tool material, and machining requirement. 2) Determining height of cutting layer, which is z coordinate of cutting path of a cutting layer. 3) Calculating the intersection region of the cutting plane and the curve surface which may be a single region or several islands, each of which has their lose boundary. 4) Determining cutting path in 2-D region. Machining can be performed with line cutting or circle cutting algorithm. There are some existing algorithm can be referenced. After tool path is generated, NC code for special machine tool can also be generated with post proposing program for the machine tool. A prototype system ImageCAM has been developed in order to prove the algorithm of the paper, which can perform total process of machine vision based manufacturing including pre-processing of image, modeling of work piece, cutting path planning, and NC program generating. CNC system used in the research is SKY2000-I, developed by SKY Co., which is based on Windows platform. The system has 32 bits CPU, supports normal netware, and Chinese operating interface, which is widely used in medium and small machine shops. Fig. 6 is the machining parameters input interface of ImageCAM. Fig. 7 show the four main stages of machining a water pot from a picture. Fig. 7 (a) is the orignal bitmap of the water pot. Fig. 7 (b) is the three dimensions model built from Fig. 7 (a), this is the most important step of machine vision based manufacturing process. Precision of machining depends greatly on the quality of the model. Fig. 7 (c) is the tool path of the work-piece, line cutting method is adapted in the research. Fig. 7 (d) is the work-piece machined in wax material. Fig. 6. Machining parameters input interface. Fig. 7 (a). Machining example with machine vision based manufacturing system – bitmap of sample Fig. 7 (b). Machining example with machine vision based manufacturing system – geometrical model. Fig. 7 (c). Machining example with machine vision based manufacturing system – tool path from CNC code/ Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 13-18 18 Fig. 7 (d). Machining example with machine vision based manufacturing system – wax work piece. 5. Conclusions Machine vision based manufacturing technique is a part of intelligent manufacturing technology, which can find its application in many engineering regions, for examples, in fast prototype manufacturing, reverse engineering, handcraft manufacturing, etc. Some key techniques are researched in the paper. Pre-proposing method is proposed, by which high quality image can be gotten, combining with middle value filter, therefore machine quality can be improved. Approximation method of polygon can be used to generate tool path very fast. Prototype system ImageCAM can perform total process of machine vision based manufacturing including pre-processing of image, modeling of work piece, cutting path planning, and NC program generating. Which proves the algorithms of the paper is feasible. Some problems are still to be researched for the technique to be used in engineering. Most important of the problems is precision of the geometrical model of work piece. Simple and precision modeling algorithm are still looked for ward to. References [1]. Ambarish G. Mohapatra, Computer vision based smart lane departure warning system for vehicle dynamics control, Sensors & Transducers, Vol. 132, Issue 9, September 2011, pp. 122-135. [2]. Q. Zheng, R. Chellappa, Estimation of illumination direction, albedo, and shape from shading, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, Issue 7, 1991, pp. 680-702. [3]. J. Oliensis, Shape from shading as a partially well- constrained problem, CVGIP: Image Understanding, Vol. 54, Issue 2, 1991, pp. 163-183. [4]. Takayuki Okatani, Koichiro Deguchi, Shape reconstruction from an endoscope image by shape from shading technique for a point light source at the projection center, Computer Vision and Image Understanding, Vol. 66, Issue 2, 1997, pp. 119-131. [5]. P. Dupuis, J. Oliensis, Shape from shading: provably convergent algorithms and uniqueness results, in Proceedings of the European Conference on Computer Vision (ECCV’94), Stockholm, Sweden, 1994, pp. 259-268. [6]. K. M. Lee, C.-C. J. Kuo, Shape from shading with a linear triangular element surface model, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, Issue 8, 1993, pp. 815-822. [7]. R. Kimmel, A. M. Bruckstein, Global shape from shading, Computer Vision and Image Understanding, Vol. 62, Issue 3, 1995, pp. 360-369. [8]. I. Shimshoni, R. Kimmel, A. M. Bruckstein, DIALOGUE, Global shape from shading, Computer Vision and Image Understanding, Vol. 64, Issue 1, 1996, pp. 188-189. [9]. B. K. P. Horn, Obtaining shape from shading information, in shape from shading, MIT Press, Cambridge, MA, 1989, pp. 123-171. [10]. Jin-Hua Sheng, A three-dimensional image processing system shape from shading, Pattern Recognition & Artificial Intelligence, Vol. 4, Issue 4, 1991, pp. 46-52. [11] Ma Songde, Zhang Zhengyou, Computer Vision, Science press, Beijing, 1998, pp. 194-208. [12]. Bang-Hwan Kim, Rae-Hong Park, Shape from shading and photometric stereo using surface approximation by legend polynomials, Computer Vision and Image Understanding, Vol. 66, Issue 33, 1997, pp. 255-270. [13]. He Bin, Ma Tianyu, Wang Yunjian et al., Visual C++ data image processing, People Post Press, Beijing, 2001, pp. 5-8. [14]. Jia Xicun, 3-D copy technique based on machine vision, Master Thesis, Yahanshan University, 2003. ___________________ 2013 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com) Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 19-25 19 SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsss © 2013 by IFSA http://www.sensorsportal.com A Detection Algorithm for Image Copy-move Forgery Based on Improved Circular Projection Matching and PCA Yanfen GAN, Jing CANG Information science and technology department Guangdong University of Foreign Studies South China Business College, Guangzhou, 510545, China Tel.: +8613751800978 E-mail: 315139752@qq.com Received: 19 August 2013 /Accepted: 25 October 2013 /Published: 25 November 2013 Abstract: Because general algorithms seldom detect copy-move forgery with angle rotation and block matching algorithms are very time-consuming, this paper proposes a detection algorithm which is able to detect copy- move forgery with rotation of certain angles by using the direction invariance of the circular projection vector. At the same time, considering the influence of random noise and brightness changes on the circular projection vector, circular projection vector is improved and becomes robust. In order to avoid the time-consuming, this algorithm also constructs a data matrix by using the circular projection vector of each image block to significantly reduce the dimensionality of the data requiring during principal component analysis. Its detection speed is obviously faster than general block matching algorithms. The experimental results show that the improved circular projection matching algorithm is less time consuming, able to resist a certain degree of angle rotation in copy-move operations, and relatively robust to the influence of random noise and illumination. Copyright © 2013 IFSA. Keywords: Circular projection matching, Rotation invariance, PCA, Image matching, Copy-move forgery. 1. Introduction Generally, a picture is used as the strong evidence for describing the occurrence of a thing. With the rapid development of image editing software, digital images are easily modified and it is becoming increasingly easier to generate vivid images. Forged images are more and more frequently found in tabloids, magazines and mainstream media or used as evidence submitted to courts. Some are even used in scientific frauds. Therefore, it is urgent to study image forgery detection algorithms so as to identify the authenticity and integrity of images. Detection algorithms for digital image forgery fall into two types. One includes the image authentication based on fragile watermarking and the image authentication based on digital signatures, both of which belong to the active algorithm. Watermark or auxiliary information is required to be added in advance. In the real world, however, this information cannot be added to all images in advance. Another type is the blind detection technique, a kind of passive authentication. The blind detection technique relies on the characteristics of images to authenticate them. However, due to the complex authentication method, it has become a more challenging subject and also has wider potential applications. Article number P_1532 http://www.sensorsportal.com/ Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 19-25 20 There are a variety of methods for image forgery [9]. The copy-move forgery in the same image is common which copies and moves a portion of the image into another position so as to cover up persons or objects in the image. The detection algorithm for this kind of image forgery is a blind detection technique and has been proposed. Most of them judge the authenticity based on the characteristic that copy-move in images will result in large similar areas. Fridrich [1] first analyzed the exhaustive search algorithm and proposed a block matching detection method based on discrete cosine transform (DCT) which significantly improves the efficiency of the exhaustive search algorithm. Popescu [2] proposed a similar method which reduces the dimensionality of feature vectors by using principal component analysis (PCA) instead of discrete cosine transform. Experiments have shown that his algorithm is more efficient. Subsequently, many scholars carried out further studies on the basis of block matching algorithms. Wu Qiong [3] transformed the original image into a 1/4 similar image using discrete wavelets before partitioning, used singular value decomposition to reduce dimensionality and finally located according to the lexicographic order. Jing Li et al. [4, 10] proposed detection and location algorithm for image region duplication forgery based on phase correlation in order to overcome the low efficiency of block matching algorithms. In general, this class of algorithms does not resist rotation and noise and is time consuming. This paper mainly studies how to improve the anti-rotation and anti-noise of the algorithm and how to improve detection efficiency using grayscale information. This algorithm first divides the detected blocks by row and column, then calculates the improved circular projection vectors of the image blocks, constructs the projection data matrix of the image through the circular projection vectors of all image blocks, uses PCA to reduce the dimensionality of the matrix, sorts that matrix in the lexicographic order and finally judges whether the sorted adjacent image block is the image block that has been copied and moved by confidence distance. The flow chart of the algorithm is shown as Fig. 1 below. The reminder of this paper is organized as follows: section 2 introduces the improved circular projection matching algorithm. Application of PCA in the detection algorithm for image copy-move forgery is presented in Section 3. Experimental results and conclusion are described in Section 4 and 5 respectively. 2. The Improved Circular Projection Matching Algorithm 2.1. Conventional Pixel Matching Rotation Sensitivity Analysis Assume that f1 and f2 with the same size of m×n are two moved image blocks in the forged image S shown as Fig. 2, where f1 is the copied image block and f2 is the moved and pasted image block. Conventional matching algorithms compare the copied image block f1 and the pasted image block f2 in the similarity in the gray values of the corresponding pixels, and take similarity as the criterion for relevance. It is obvious that when relative rotation exists between f1 and f2, corresponding pixels change. When the rotation angle is small, the algorithms can find correct matching positions due to the similarity between adjacent pixels. However, when the rotation angle is big, the difference of gray value increases and conventional matching algorithms do not work. Fig. 1. Flow chart of the algorithm. Fig. 2. The forged image S. The key to solving the angle matching problem that random angle rotation exists between the copied image f1 and the pasted image f2 is to find a rotation 1f 2f Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 19-25 21 invariant. The circular projection matching algorithm [5, 6] was proposed based on the isotropy and projection features of a circle. With this algorithm, the sum of the pixels in the concentric circles with different radiuses in the circle is calculated and taken as the projection data at the radius. P(r)=∑ = π θ θ 2 0 ),(rT Rr ≤≤0 , (1) where ( ) ( )22 ϕφ −+−= nmr and R is the radius of the maximum inscribed circle of the image, shown as Fig. 3. Fig. 3. Circular projection vector. When the image rotates, the pixels on the circle with any radius also rotate along with the radiuses of the concentric circles, so P(r) remains unchanged, which means that it is theoretically possible to realize matching of random angle rotation. 2.2. The Improved Circular Projection Matching Algorithm As a matter of fact, some problems will arise when the circular projection matching algorithm is used to detect the copy-move in the image, including changing the brightness of copied image and adding noise, which requires us to improve the circular projection matching algorithm. 2.2.1. Noise Impact Analysis and Improvement Images transmitted in channels are interfered by different kinds of noise, including the most common Gaussian impulsive noise. Gaussian impulsive noise refers to a noise signal whose frequency spectrum components are uniformly distributed (white noise) and whose amplitudes obey the Gaussian distribution. It is also addible and regarded as one kind of white noise. Literature [8] has proved that Gaussian impulsive noise does not change image gray values and it only affects the alternating current component of an image. At the same time, in analyzing each projection vector, it is found that the bigger the radius of the projection vector is, the greater the cumulative change is. In other words, the influence of the alternating current component on the image increases with the radius. Literatures [4, 5] improve the algorithm by replacing the projection value with the grayscale average of each concentric circle. The component in the projection is P1(r)= P(r)/ S(r) , (2) where S(r) is the number of pixels included in the concentric circle whose radius is r. 2.2.2. Brightness Impact Analysis and Improvement The gray value of each pixel of an image is mainly decided by the brightness of the light reflected by the surface of a scene, so light will brighten or darken the overall image, which is equivalent to adding a direct current component. Similarly, the grayscale and contrast changes can also be attributed to the influence of the direct current component or the alternating current component of an image. How to reduce the influence of the alternating current component has been discussed above. As to how to reduce the influence of the direct current component, it is required to reconstruct the circular projection vector and enable it to have grayscale translation invariance instead of averaging. The improvement made in literatures [5, 6] is: ( ) ( ) ( ) R R r 0 r 0 2 ( P( r ) / SP r P r ( rS ) )r = = = ∑ ∑ , (3) According to Formula (3), changes in noise and light intensity will result in the changes in the alternating current component of a circular projection and as a result, the gray values P(r) at different radiuses increase as the radius increases. In this case, Literatures [5, 6] adopted the idea of normalization for processing. ( ) ( ) ( ) ( ) ( ) ( ) R R r 0 r 0 3 2 ( ( P( r ) / S( r P r P r / S r P r S r / S) )) r = = = = = ∑ ∑ , (4) At the same time, considering that the main characteristics of a scene are concentrated on the center, the variable weight is used. ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) R R r 0 r 0 R R r 0 r 0 R R r 0 r 0 R R r 4 1 0 r 3 0 ( ( P( r ) / S( r ) )) ( ( P( r P r W r P r W r P r S r / S r W r P r / S r W r ) / S( r ) )) ( ( ) ( P( r ) / S( r ) )) ( ( P( r ) / S P r P r S r / S r ,( r ) )) = = = = = = = = = = = − = − = − = = = = ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ (5) Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 19-25 22 where W(r) is the variable weight vector. This paper uses the inconsistency correction method to reduce the influence of brightness based on variable weights. ( ) ( ) ( )5 1 R 1 r 0 P ( r P r W r P r ) ( ) R 1 == + − ∑ , (6) Equations (5) and (6) subtract an estimated direct current component. The difference is that Equation (5) directly estimates the projection of the original component while Equation (6) uses the projection data in which noise has been filtered out. 2.2.3. Analysis of the Experimental Results of the Improved Circular Projection Matching Algorithm The improved circular projection matching algorithm above is verified. A point whose coordinate is (148, 70) is located. After the image is rotated by 60 degrees, the coordinate of the point becomes (182, 118). For the point (148, 70), a circular projection is conducted according to Formula (1). The radius of the circular projection is 7. The projection vector of the original image is P1(r) = [43 24 18.634 16.012 14.357 13.975 13.994 13.943]. Gaussian noise (σ =0.01) is added to the point (182, 118). After brightness γ is adjusted, a circular projection is conducted according to Formula (5) to obtain the new projection vector P4(r)= [0.2181 0.30401 0.32234 0.3046 0.27542 0.26652 0.26472 0.261870]. Similarly, Gaussian noise (σ =0.01) is added to the point (182,118). After brightness γ is adjusted, a circular projection is conducted according to Formula (6) to obtain the new projection vector P5(r)= [0.14850 0.09486 0.01871 -0.00154 0.014067 - 0.00076 -0.00656 -0.00651]. After calculations, the correlation coefficient between P1(r) and P4(r) is 0.9487 and the correlation coefficient between P1(r) and P5(r) is 0.9701. The results show that the new projection vector obtained according to Equation (6) has better performance in rotation, noise and brightness change resistance. 3. Application of PCA in the Detection Algorithm for Image Copy-Move Forgery PCA is a linear dimensionality reduction method based on one-dimensional vectors. It is used to find the best low-dimensional representation of the original high-dimensional data based on least mean- square error. It can transform multiple indicators into a few comprehensive indicators and select a few important variables from multiple variables through linear variation so as to reduce dimensionality. Therefore, the first step is to move the pixel one by one to divide the image whose size is m × n (512 × 512) into L image blocks whose sizes are 2k×2k. A circular projection is conducted for each image block. The grayscale function of the 2k× 2k image blocks is f (x, y). The circular projection of a pixel is the one-dimensional vector PT=[P(0),P(1)…P(K)]. L one-dimensional vectors are written into the projection feature matrix of the image U=[PT(1) PT(2)…PT(L)]T , where PT(i) (i=1,2…L) is the row vector constituted by the circular projection of the image block. It can be seen that the size of U is L×K. Then PCA is used to reduce the dimensionality of the matrix U. The steps are as follows: Step 1 :Normalize the projection feature matrix U. The data in the matrix have different natures and different orders of magnitude. If no normalization is applied, the impact of high-value indicators will reduce the impact of low-value indicators in the analysis, so that small data are ignored. As a result, various data will participate in the operation at unequal weights. There are a great many methods for normalization. Here the standard normal distribution normalization is used: ( ) ( ) ( )T T 2’ T T T 1( P i ) ( P (i )-PP i P i / ) L (i ) = − ∑ , (7) where PT(i)’ is the normalized row vector and Unew is the matrix composed of the normalized row vectors. Step 2: Obtain the covariance matrix V of Unew. V= ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎡ npnn p p vvv vvv vvv 21 22221 11211 , (8) where Vij (i=1, n, j=1, p) is the correlation coefficient between variables PT(i)’ and PT(j)’ and is calculated according to the following formula. Vij= ∑ ∑ ∑ = = = −− −− n k n k jTkjTiTkiT n k jTkjTiTkiT pppp pppp 1 1 22 1 )()( ))(( , (9) Step 3: Obtain the feature valueλ i of V and the feature vector ei(i=1, p) of the corresponding feature value. Step 4: Calculate the contribution and cumulative contribution of the principal component: The contribution of the principal component Wi is Wi= ∑ = n k k i 1 λ λ (i=1, p) , (10) Sensors & Transducers, Vol. 159, Issue 11, November 2013, pp. 19-25 23 and the cumulative contribution Qi is Qi= ∑ ∑ = = n k k i k i 1 1 λ λ , (11) Get the 1st, 2nd, …, hth principal components corresponding to the feature values λ 1, λ 2 λ h (h≤ p ) whose cumulative contribution is 85 %. Step 5: Construct the dimensionality reduction transformation matrix S with the feature vectors corresponding to the feature values λ 1, λ 2 λ h sorted in the descending order. Calculate U’=Unew S⋅ and complete the transformation from the high-dimensional U into the low-dimensional U’. Finally sort U’ according to the lexicographic order and seek out the copy-move area in combination with the offset confidence distance according to the sorted image. 4. Experimental Results and Analysis In order to verify the validity of this algorithm, this paper selects 150 natural images and use Photoshop for copy-move. The methods for copy- move include rotation of 0°-30°(150 images), rotation of 30°-60°(150 images) and rotation of above 60° (150 images). For the above images, Gaussian noise ( 01.0=σ ) is added and brightness γ =0.6 is adjusted. The computer configuration on which all experiments are conducted is CPU dual core (TM) 2.4 GHz, a memory of 2GB and the Windows XP operating system. The size of the image block is 16×16. Matlab 7 is used to program the algorithm in this paper and other relevant algorithms. First, the above forged image sample is used to test the resistance of the algorithm proposed in this paper against rotation angles. The test results are shown in Table 1. The data in the table show that the detection rate can exceed 93 % for copy-move forgery after rotation of 0°-60°. Fig. 4 and Fig. 5 are the detection results of a test image called “sunflower” which is copied and pasted after rotation of 30°and 60° respectively. It is shown that both can be detected correctly. However, when the rotation angle exceeds 60°, the detection rate of the algorithm proposed in this paper decreases sharply. The circular projection matching algorithm is theoretically rotation invariant. However, during its application on the computer, if the interpolation of the image block, whose rotation angle becomes bigger after copy, is greater when pasted, the projection difference between the original image block and that of the pasted image block will be greater and the detection will fail. Table 1. Test of copy-move forgery detection after rotation of different angles. Type of image Number of images Detected Detection rate (%) Rotate 0°-60° 300 280 93.3 Rotate >60° 150 12 8.0 (a) Original image (b) Copied and pasted image after rotation of 30° (c) Detected image Fig. 4. Detection result of copy-move forgery after rotation of 30°. Second, comparison is made with algorithms proposed in literatures [2-4] in timeliness. Because the number of blocks is a crucial factor that affects the