臺灣博碩士論文加值系統

樣板比對(template matching)是一種應用於信號與影像處理的技術。樣板比對在場景影像中移動樣板，計算它們之間的相似性以尋找所有可能的樣版位置。樣板比對存在的問題之一是在場景影像中存在旋轉的物體。因此，本論文討論旋轉不變性的樣板比對技術並提出兩種快速旋轉不變性樣板比對方法。
在第一種方法，我們提出一個快速次像素(subpixel)精度的樣板比對方法。首先，利用插值法減小場景影像和樣板影像的大小，並使用環形投影轉換 （circular projection transformation）來確定比對的候選點。環形投影轉換的優點是擁有旋轉不變性的特性，並降低了計算複雜度。接著將這些候選點使用Zernike矩執行樣板比對。環形投影轉換比Zernike矩更有效率。因此，在第一階段使用環形投影轉換可以減少計算時間。最後，利用二次多項式匹配法則(second-degree polynomial fitting formula)決定次像素匹配位置。實驗結果證明，提出的次像素樣板比對方法可以達到次像素精度且執行時間顯著減少。這表示此法適合線上的檢測系統。
在第二種方法中，我們結合環形投影轉換和有界部分相關係數 (bounded partial correlation) 方法，提出一個快速樣板比對方法。首先，利用環形投影轉換的核心濾波器求得影像特徵，以減少計算的複雜度。接著利用有界部分相關係數演算法計算相關係數的相似性量測 (normalized correlation similarity measure)以減少執行時間。在一台Pentium IV 3GHz處理器的電腦上，完成單次程序所需要的時間為0.12秒，使用的背景影像大小為 且樣板大小為 。實驗結果證明，提出的樣板比對方法所得的匹配結果與環形投影轉換是一致的，並且大幅降低執行時間。這表示此法適合即時辨識系統。

Template matching is a technique used in signal and image processing. It searches all possible positions of a template in a scene image by moving a template, and computing the similarity measures between them. One of the existing problems in template matching is when rotated objects exist in a scene image. Hence, this thesis discusses many rotation invariance techniques used in template matching and proposes two different approaches for fast template matching with rotation invariance.
In the first approach, a fast template matching method with subpixel accuracy is proposed. In the first stage of the approach, the reduced sizes of the scene image and the template image are used by the interpolation method and the circular projection transformation (CPT) process is used to determine the matching candidates. The advantages of the CPT process are that it owns the property of rotation invariance and reducing the computational complexity. In the second stage of the approach, the Zernike moments were applied to perform template matching only on the matching candidates. CPT is more efficient than the Zernike moments. Hence, using CPT in the first stage can reduce the computation time for selecting the matching candidates. Finally, the subpixel position can be computed by the second-degree polynomial fitting formula. The experimental results show that the proposed subpixel template matching method can reach subpixel accuracy and the execution time is significantly reduced. This indicates that our approach is suitable for online template matching.
In the second approach, a fast template matching method by combining CPT process and the bounded partial correlation (BPC) algorithm was proposed. In the first stage of the approach, the CPT kernel filters are used to reduce the time complexity of computing CPT values. In the second stage of the approach, the BPC algorithm was used to reduce the execution time of the normalized correlation similarity measure. The experimental results show that the matching result from our method was consistent with that of the CPT, and the execution time was considerably reduced. The computational time of the proposed method takes approximate 0.12 seconds to complete entire operation with a 3 GHz Pentium IV PC using the program Visual C++ for a scene image and a template. This indicates that our approach is suitable for online template matching.

摘 要 i
Abstract iii
誌 謝 v
Contents vi
Table Captions ix
Figure Captions xi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related Work 3
1.3 An Overview of the Thesis 9
1.4 Organization of the Thesis 10
Chapter 2 Techniques of Template Matching 12
2.1 Introduction 12
2.2 Similarity Measures 14
2.3 Projection Transformation 15
2.3.1 Circular Projection Transformation 16
2.3.2 Radial Projection Transformation 21
2.3.3 Summary 26
2.4 Image Moments 29
2.4.1 Hu Moments 30
2.4.2 Zernike Moments 35
2.4.3 Orthogonal Fourier-Mellin Moments 40
2.5 Elimination Algorithms 45
2.5.1 Successive Elimination Algorithm 46
2.5.2 Bounded Partial Correlation 47
Chapter 3 A Template Matching Algorithm Using CPT and Zernike Moments 49
3.1 Introduction 49
3.2 Procedure of Template Matching of the Proposed Method 50
3.2.1 Acquiring Images 52
3.2.2 Teaching 53
3.2.3 Coarse Search 53
3.2.4 Fine Search 58
3.2.5 Registering in Subpixel Accuracy 60
3.3 Experimental Results and Discussion 61
3.4 Summary 65
Chapter 4 A Template Matching Algorithm by Combining CPT and BPC 67
4.1 Introduction 67
4.2 The Proposed Algorithm of Template Matching by the Combination of CPT and BPC 68
4.2.1 Acquiring Images 69
4.2.2 Teaching 69
4.2.3 Circular Projection Transformation Using Kernel Filters 69
4.2.4 Bounded Partial Correlation Using CPT 70
4.3 Procedure of Template Matching of the Proposed Method 71
4.4 Experimental Results and Discussion 73
4.5 Summary 79
Chapter 5 Conclusions and Future Work 81
References 83

[1]D. I. Barnea and H. F. Silverman, “A class of algorithms for fast digital image registration, IEEE Trans. Comput., Vol. C-21, No. 2, pp. 179-186, 1972.
[2]S. O. Belkasim, M. Shridhar and M. Ahmadi, “Pattern recognition with moment invariants: a comparative study and new results, Pattern Recogn., Vol. 24, No. 12, pp. 1117-1138, 1991.
[3]Y. Bentoutou, N Taleb, K. Kpalma and J. Ronsin, “An automatic image registration for applications in remote sensing, IEEE Trans. Geoscience and Remote Sensing, Vol. 43, No. 9, pp. 2127-2137, 2005.
[4]C. A. Bernstein, L. N. Kanal, D. Lavin and E. C. Olson, “A geometric approach to subpixel registration accuracy, Comput. Vis. Graph. Image Process., Vol. 40, pp. 334-360, 1987.
[5]E. D. Castro and C. Morandi, “Registration of translated and rotated images using finite Fourier transforms, IEEE Trans. Pattern Anal. Mach. Intell., Vol. PAMI-9, No. 5, 1987.
[6]M. E. Celebi and Y. A. Aslandogan, “A comparative study of three moment-based shape descriptors, in Proc. Int. Conf. Information Technology: Coding and Computing, Vol. 1, pp. 788-793, Apr. 2005.
[7]A. Chalechale, A. Mertins and G. Naghdy, “Edge image description using angular radial partitioning, IEE Proceedings-Vision, Image ＆ Signal Processing, Vol. 151, No. 2, pp. 93-101, 2004.
[8]M. S. Choi and W. Y. Kim, “A novel two stage template matching method for rotation and illumination invariance, Pattern Recogn., Vol. 35, pp. 119-129, 2002.
[9]X. Dai and S. Khorram, “A feature-based image registration algorithm using improved chain-code representation combined with invariant moments, IEEE Trans. Geoscience and Remote Sensing, Vol. 37, No. 5, pp. 2351-2362, Sep. 1999.
[10]S. A. Dudani, “Aircraft identification by moment invariants, IEEE Trans. Comput., Vol. C-26, No. 1, pp. 39-45, 1977.
[11]V. N Dvorchenko, “Bounds on (deterministic) correlation functions with application to registration, IEEE Trans. Pattern Anal. Mach. Intell., Vol. PAMI-5, No. 2, pp. 206-213, 1983.
[12]S. Erturk, “Digital image stabilization with sub-image phase correlation based global motion estimation, IEEE Trans. Consumer Electronics, Vol. 49, No. 4, pp. 1320-1325, 2003.
[13]J. Flusser, “On the independence of rotation moment invariants, Pattern Recogn., Vol. 33, No. 9, pp. 1405-1410, 2000.
[14]J. Flusser and T. Suk, “Rotation moment invariants for recognition of symmetric objects, IEEE Trans. Image Process., Vol. 15, No. 12, pp. 3784-3790, 2006.
[15]H. Foroosh, J. B. Zerubia and M. Berthod, “Extension of phase correlation to subpixel registration, IEEE Trans. Image Process., Vol. 11, No.3, pp. 188-200, 2002.
[16]X. Q. Gao, C. J. Duanmu and C. R. Zou, “A multilevel successive elimination algorithm for block matching motion estimation, IEEE Trans. Image Process., Vol. 9, No. 3, pp. 501-504, 2000.
[17]M. Gharavi-Alkhansari, “A fast globally optimal algorithm for template matching using low-resolution pruning, IEEE Trans. Image Process., Vol. 10, No. 4, pp. 526-533, Apr. 2001.
[18]R. C. Gonzalez and R. E. Woods, Digital Image Processing, Second Edition, chapter 12, pp. 701-712.
[19]A. Goshtasby, “Template matching in rotated images, IEEE Trans. Pattern Anal. Mach. Intell., Vol. PAMI-7, No. 3, pp. 338-344, May 1985.
[20]A. Goshtasby, S. H. Gage and J. F. Bartholic, “A two-stage cross correlation approach to template matching, IEEE Trans. Pattern Anal. Mach. Intell., Vol. PAMI-6, pp. 374-378, May 1984.
[21]A. Goshtasby, G. C. Stockman and C. V. Page, “A region-based approach to digital image registration with subpixel accuracy, IEEE Trans. Geoscience and Remote Sensing, Vol. GE-24, No. 3, pp. 390-399, May 1986.
[22]L. Guohai, Z. Zhu and X. Jie, “Orientation and damage inspection of insulators based on tchebichef moment invariants, in Proc. 2008 IEEE Int. Conf. Neural Networks and Signal Processing, pp. 48-52, 2008.
[23]P. S. Heckbert, “Filtering by repeated integration, in Proc. SIG-GRAPH, pp. 315-321, 1986.
[24]H. T. Hu and Z. L. Ping, “Computation of orthogonal Fourier-Mellin moments in two coordinate systems, Journal of the Optical Society of America A, Vol. 26, No. 5, pp. 1080-1084, 2009.
[25]M. K. Hu, “Visual pattern recognition by moment invariants, IRE Transactions on Information Theory, Vol. 8, pp. 179-187, Feb. 1962.
[26]M. S. Huajun, H. Song, W. Sheng and Z. Liu, “Fast correlation tracking method based on circular projection, in Proc. IEEE 8th ACIS Int. Conf. Software Engineering, Artificial Intelligence, Networking, and Parallel/ Distributed Computing, Vol. 1, pp. 235-238, Jul. 2007.
[27]S. Y. Huang, Y. K. Lee, R. Z. Wang and Y. H. Chen, “Threshold-based successive elimination algorithm for block motion estimation, Opt. Eng., Vol. 45, No. 2, pp. 027002:1–11, 2006.
[28]A. Jaklic and F. Solina, “Moments of superellipsoids and their application to range image registration, IEEE Trans. Syst., Man. Cybern., part B: Cybernetics, Vol. 33, No. 4, pp. 648-657, Aug. 2003.
[29]C. Kan and M. D. Srinath, “Invariant character recognition with Zernike and orthogonal Fourier-Mellin moments, Pattern Recogn., Vol. 35, pp. 143-154, 2002.
[30]R. Keys, “Cubic convolution interpolation for digital image processing, IEEE Trans. Acoustics, Speech and Signal Processing, Vol. 29, No. 6, pp. 1153-1160, 1981.
[31]A. Khotanzad and Y. H. Hong, “Invariant image recognition by Zernike moments, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 12, No. 5, pp. 489-497, May 1990.
[32]H. I. Kim, S. H. Lee and N. I. Cho, “Rotation-invariant face detection using angular projections, Electronics Letters, Vol. 40 , No. 12, pp. 726-727, 2004.
[33]H. K. Kim, J. D. Kim, D. G. Sim and D. I. Oh, “A modified Zernike moment shape descriptor invariant to translation, rotation and scale for similarity-based image retrieval, in Proc. IEEE Int. Conf. Multimedia and Expo, Vol. 1, pp. 307-310, Aug. 2000.
[34]H. Y. Kim, “Rotation-discriminating template matching based on Fourier coefficients of radial projections with robustness to scaling and partial occlusion, Pattern Recogn., Vol. 43, No. 3, pp. 859-872, 2010.
[35]H. Y. Kim and S. A. Araújo, “Grayscale template-matching invariant to rotation, scale, translation, brightness and contrast, IEEE Pacific-Rim Symposium on Image and Video Technology, Lecture Notes in Computer Science, Vol. 4872, pp. 100-113, 2007.
[36]S. G. Kim and W. Y. Kim, “Hierarchical grayscale template matching for an arbitrarily oriented object, in Proc. Korea Signal Process. Conf. 96, pp. 1335-1338, 1996.
[37]L. Kotoulas and I. Andreadis, “Accurate calculation of image moments, IEEE Trans. Image Process., Vol. 16, No. 8, pp. 2028-2037, Aug. 2007.
[38]W. Krattenthaler, K. J. Mayer and M. Zeiller, “Point correlation: a reduced-cost template matching technique, in Proc. 1st IEEE Int. Conf. Image Process., Vol. 1, pp. 208-212, Nov. 1994.
[39]R. Lai, X. Liu and F. Ohkawa, “A fast template matching algorithm based on central moments of images, in Proc. IEEE Int. Conf. Information and Automation, pp. 596-600, Jun. 2008.
[40]S. X. Liao and M. Pawlak, “On image analysis by moments, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 18, No. 3, pp. 254-266, Mar. 1996.
[41]S. X. Liao and M. Pawlak, “On the accuracy of Zernike moments for image analysis, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 20, No. 12, pp. 1358-1364, Dec. 1998.
[42]C. H. Lee and L. H. Chen, “A fast motion estimation algorithm based on the block sum pyramid, IEEE Trans. Image Process., Vol. 6, No. 11, pp. 1587-1590, 1997.
[43]D. H. Lee, S. G. Lee and J. H. Choi, “Region-based Corner Detection by Radial Projection, Journal of the Optical Society of Korea, Vol. 15, No. 2, pp. 152-154, 2011.
[44]W. C. Lee and C. H. Chen, “Subpixel edge location using orthogonal Fourier-Mellin moments based edge location error compensation model, in Proc. 8th Int. Conf. Intelligent Systems Design and Applications, pp. 346-351, 2008.
[45]W. C. Lee and C. H. Chen, “A fast template matching method for rotation invariance using two stage process, in Proc. 5th Int. Conf. Intelligent Information Hiding and Multimedia Signal Processing, pp. 9-12, 2009.
[46]J. P. Lewis, “Fast template matching, in Proc. Vision Interface, pp. 120-123, 1995.
[47]W. Li and E. Salari, “Successive elimination algorithm for motion estimation, IEEE Trans. Image Process., Vol. 4, pp. 105-107, Apr. 1995.
[48]Z. Li, J. Zhang, Y. Liu and H. Li, “The curve-structure invariant moments for shape analysis and recognition, in Proc. Ninth Int. Conf. Computer Aided Design and Computer Graphics, pp. 5-9, Dec. 2005.
[49]H. Lin, J. Si and G. P. Abousleman, “Orthogonal rotation-invariant moments for digital image processing, IEEE Trans. Image Process., Vol. 17, No. 3, pp. 272-282, Mar. 2008.
[50]Y. H. Lin and C. H. Chen, “Template matching using the parametric template vector with translation, rotation and scale invariance, Pattern Recogn., Vol. 41, No. 7, pp. 2413-2421, 2008.
[51]Y. H. Lin, C. H. Chen and C. C. Wei, “A new approach to fast template matching with rotation invariance using ring code matching, in Proc. 19th IPPR Conf. Computer. Vision, Graphics and Image Processing, pp. 1297-1301, 2006.
[52]Y. H. Lin, C. H. Chen and C. C. Wei, “New method for subpixel image matching with rotation invariance by combining the PT Method and the RPT Process, Opt. Eng., Vol. 45(6), pp. 067202(1-9), 2006.
[53]A. Mahmood and S. Khan, “Early termination algorithms for correlation coefficient based block matching, in Proc. IEEE Int. Conf. Image Processing, Vol. 2, pp. II-469-II-472, Sep. 2007.
[54]A. Mahmood and S. Khan, “Exploiting transitivity of correlation for fast template matching, IEEE Trans. Image Process., Vol. 19, No. 8, pp. 2190-2010, Aug. 2010.
[55]A. Mahmood and S. Khan, “Correlation-coefficient-based fast template matching through partial elimination, IEEE Trans. Image Process., Vol. 21, No. 4, pp. 2099-2108, 2012.
[56]S. Mattoccia, F. Tombari and L. D. Stefano, “Reliable rejection of mismatching candidates for efficient ZNCC template matching, in Proc. IEEE Int. Conf. Image Processing, pp. 849-852, 2008.
[57]S. Mattoccia, F. Tombari and L. D. Stefano, “Fast full-search equivalent template matching by enhanced bounded correlation, IEEE Trans. Image Process., Vol. 17, No. 4, pp. 528-538, 2008.
[58]R. Mukundan, “Some computational aspects of discrete orthonormal moments, IEEE Trans. Image Process., Vol. 13, No. 8, pp. 1055-1059, 2004.
[59]R. Mukundan, S. H. Ong and P. A. Lee, “Image analysis by Tchebichef moments, IEEE Trans. Image Process., Vol. 10, No. 9, pp. 1357-1364, Sep. 2001.
[60]S. Omachi and M. Omachi, “Fast template matching with polynomials, IEEE Trans. Image Process., Vol. 16, No. 8, pp.2139-2149, Aug. 2007.
[61]W. Ouyang, F. Tombari, S. Mattoccia, L. D. Stefano and W. K. Cham, “Performance evaluation of full search equivalent pattern matching algorithms, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 34, No. 1, pp. 127-143, 2012.
[62]J. A. Parker, R. V. Kenyon and D. E. Droxel, “Comparison of interpolating method for image resampling, IEEE Trans. Medical Imaging, Vol. 2, No. 1, pp. 31-39, Mar. 1983.
[63]Q. Peng, X. You, L. Zhou and Y. Cheung, “Retinal blood vessels segmentation using the radial projection and supervised classification, in Proc. 20th Int. Conf. Pattern Recogn., pp. 1489-1492, 2010.
[64]E. Persoon and K. S. Fu, “Shape discrimination using Fourier descriptors, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, No. 3, pp. 388-397, 1986.
[65]Z. L. Ping and Y. L. Sheng, “Fourier-Mellin descriptor and interpolated feature space trajectories for three-dimensional object recognition, Opt. Eng., Vol. 39, No. 5, pp. 1260-1266, 2000.
[66]L. M. Po and W. C. Ma, “A novel four-step search algorithm for fast block motion estimation, IEEE Trans. Circuits and Systems for Video Technology, Vol. 6, No. 3, pp. 313-317, 1996.
[67]S. P. Prismall, M. S. Nixon and J. N. Carter, “On moving object reconstruction by moments, In Proc. British Machine Vision Conf., pp. 73-82, 2002.
[68]B. S Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration, IEEE Trans. Image Process., Vol. 5, No. 8, pp. 1266-1271, 1996.
[69]A. P. Reeves, “Three-dimensional shape analysis using moments and Fourier descriptors, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 10, No. 6, pp. 937-943, 1988.
[70]J. Revaud, G. Lavoue and A. Baskurt, “Improving Zernike Moments Comparison for Optimal Similarity and Rotation Angle Retrieval, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 31, No. 4, pp. 627-636, 2009.
[71]A. Rosenfeld and G. J. VanderBrug, “Coarse-fine template matching, IEEE Trans. Syst., Man. Cybern., Vol. 7, No. 2, pp. 104-107, Feb. 1977.
[72]H. Schweitzer, J. W. Bell and F. Wu, “Very fast template matching, in Proc. 7th Eur. Conf. Computer Vision IV, pp. 358-372, 2002.
[73]Y. L. Sheng and L. X. Shen, “Orthogonal Fourier-Mellin moments for invariant pattern recognition, Journal of the Optical Society of America A, Vol. 11, pp. 1748-1757, 1994.
[74]P. Simard, L. Bottou, P. Haffner and Y. LeCun, “Boxlets: A fast convolution algorithm for signal processing and neural networks, Adv. Neural Inf. Process. Syst., Vol. 11, pp. 571-577, 1999.
[75]M. Sonka, V. Hlavac and R. Boyle, Image Processing, Analysis, and Machine Vision, Second Edition, P.W.S, 1999.
[76]L. D. Stefano, M. Marchionni and S. Mattoccia, “A fast area-based stereo matching algorithm, Image Vis. Comput., Vol. 22, No. 12, pp. 983-1005, 2004.
[77]L. D. Stefano and S. Mattoccia, “Fast template matching using bounded partial correlation, Mach. Vis. Appl., Vol. 13, No. 4, pp. 213-221, 2003.
[78]L. D. Stefano and S. Mattoccia, “A sufficient condition based on the Cauchy-Schwarz inequality for efficient template matching, in Proc. IEEE Int. Conf. Image Processing, Vol. 1, pp. 269-272, Sep. 2003.
[79]L. D. Stefano, S. Mattoccia and M. Mola, “An efficient algorithm for exhaustive template matching based on normalized cross correlation, in Proc. 12th Int. Conf. Image Analysis and Processing, pp. 322-327, 2003.
[80]L. D. Stefano, S. Mattoccia and F. Tombari, “ZNCC-based template matching using bounded partial correlation, Pattern Recogn. Lett., Vol. 26, No. 14, pp. 2129-2134, 2005.
[81]L. Su and Z. Gao, “Radial projection Fourier transform and its application for scene matching with rotation invariance, Computer and Information Science, Vol. 2, No. 4, pp. 31-46, 2009.
[82]R. Sundaram, “Analysis and implementation of an efficient edge detection algorithm, Opt. Eng., Vol. 42, No. 3, pp. 642-650, 2003.
[83]K. Tanaka, M. Sano, S. Ohara and M. Okudaira, “A parametric template method and its application to robust matching, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Vol. 1, pp. 620-627, 2000.
[84]Y. Y. Tang, B. F. Li, H. Ma and J. Liu, “Ring-projection-wavelet fractal signatures: A novel approach to feature extraction, IEEE Trans. Circuits and Systems. Analog Digital Signal Processing, Vol. 45, No. 8, pp.1130-1134, Aug. 1998.
[85]S. L. Tanimoto, “Template matching in pyramids, Comput. Vis. Graph. Image Process., Vol. 16, No. 4, pp. 356-369, 1981.
[86]M. R. Teague, “Image analysis via the general theory of moments, Journal of the Optical Society of America, Vol. 70, pp. 920-930, Aug. 1980.
[87]C. H. Teh and R. T. Chin, “On image analysis by the methods of moments, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 10, No. 4, pp. 496-513, 1988.
[88]F. Tombari, S. Mattoccia and L. D. Stefano, “Full-search-equivalent pattern matching with incremental dissimilarity approximations, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 31, No. 1, pp. 129-141, 2009.
[89]D. M. Tsai and Y. H. Tsai, “Rotation-invariant pattern matching with color ring-projection, Pattern Recogn., Vol. 35, pp. 131-141, 2002.
[90]Y. C. Tsai, “Measure accuracy of template matching based on feature-based approach, Master Thesis, Institute of Computer and Communication Engineering, NCKU, 2009.
[91]M. Tuceryan, “Moment based texture segmentation, Pattern Recogn. Lett., Vol. 15, No. 7, pp.659-668, 1994.
[92]M. Uenohara and T. Kanade, “Use of Fourier and Karhunen-Loeve decomposition for fast pattern matching with a large set of templates, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 19, No. 8, pp. 891-898, Aug. 1997.
[93]F. Ullah and S. Kaneko, “Using orientation codes for rotation-invariant template matching, Pattern Recogn. , Vol. 37, pp. 201-209, 2004.
[94]G. J. VanderBrug and A. Rosenfeld, “Two-stage template matching, IEEE Trans. Comput., Vol. C-26, No. 4, pp. 384-393, Apr. 1977.
[95]H. S. Wang and R. M. Mersereau, “Fast algorithms for the estimation of motion vectors, IEEE Trans. Image Process., Vol. 8, No.3, pp. 435-439, 1999.
[96]L. Wang and G Healey, “Using Zernike moments for the illumination and geometry invariant classification of multispectral texture, IEEE Trans. Image Process., Vol. 7, No. 2, pp. 196-203, 1998.
[97]S. D. Wei and S. H. Lai, “Fast template matching based on normalized cross correlation with adaptive multilevel winner update, IEEE Trans. Image Process., Vol. 17, No. 11, pp. 2227-2235, Nov. 2008.
[98]R. Y. Wong and E. L. Hall, “Sequential hierarchical scene matching, IEEE Trans. Comput., Vol. C-27,No. 4, pp. 359-366, 1978.
[99]J. Wood, “Invariant pattern recognition: a review, Pattern Recogn., Vol. 29, No. 1, pp. 1-17, 1996.
[100]P. Wu and J. W. Hsieh, “Efficient image using concentric sampling features and boosting process, in Proc. IEEE Int. Conf. Image Processing, pp. 2004-2007, Oct. 2008.
[101]H. O. Yacov and H. O. Hagit, “Real-time pattern matching using projection kernels, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, No. 9, pp. 1430-1445, 2005.
[102]B. Yang, G. Li, H. Zhang and M. Dai, “Rotation and translation invariants of Gaussian-Hermite moments, Pattern Recogn. Lett., Vol. 32, No. 9, pp. 1283-1298, 2011.
[103]C. T. Zhan and R. Z. Roskies, “Fourier descriptors for plane closed curves, IEEE Trans. Comput., Vol. C-21, No. 3, pp. 269-281, Mar. 1972.
[104]J. Zhang and T. Tan, “Brief review of invariant texture analysis methods, Pattern Recogn., Vol. 35, No. 3, pp. 735-747, 2002.
[105]L. Zhang, G. B. Qian, W. W. Xiao and Z. Ji, “Geometric invariant blindimage watermarking by invariant Tchebichef moments, Opt. Express, Vol. 15, No. 20, pp. 2251-2261, 2007.
[106]R. Zhi and Q. Ruan, “A Comparative study on region-based moments for facial expression recognition, in Proc. CISP '08. Congress on Image and Signal Processing, Vol. 2, pp. 600-604, May 2008.
[107]C. Zhu, W. S. Qi and W. Ser, “Predictive fine granularity successive elimination for fast optimal block-matching motion estimation, IEEE Trans. Image Process., Vol. 14, No. 2, pp. 213-221, 2005.
[108]H. Zhu, H. Shu, T. Xia, L. Luo and J. L. Coatrieux, “Translation and scale invariants of Tchebichef moments, Pattern Recogn., Vol. 40, No. 9, pp. 2530-2542, 2007.
[109]S. Zhu and K. K. Ma, “A new diamond search algorithm for fast block-matching motion estimation, IEEE Trans. Image Process., Vol. 9, No. 2, pp. 287-290, 2000.
[110]B. Zitova and J. Flusser, “Image registration methods: a survey, Image Vis. Comput., Vol. 21, No. 11, pp. 977-1000, 2003.