Some research topics for future work are proposed.
(1) Symmetric information extraction
Since symmetry exists widely in the real world, the symmetry detection and localization of symmetry axes is significance for understanding and interpreting the images. The Zernike moments are very suitable for the symmetry detection due to their symmetric and periodical properties. We are currently develop a novel approach which transforms the 2D symmetric image into a 1D periodic curve based on the symmetric properties of the ZMs function. In this way, the symmetric type (rotational or reflection symmetry), fold number and the fold axes can be plainly determined by finding the periodic information from the transformed 1D curve. Furthermore, a unique solution of the rotation angle for a given gray-level or binary image can also be determined.
(2) Content-based image retrieval (CBIR)
We plan to extend the proposed ZM phase-based descriptor for the application of content-based image retrieval. To do this, the interests of regions for a set of labeled training images are first detected and their descriptors are constructed by the proposed ZM phase approach. The collected descriptors are grouped via clustering, and the set of cluster
centers. To organize a codebook, the visual words are constructed from the set of vocabularies. The codebook is stored as an inverted file or a hash table, called the gallery image database. During the image retrieval stage, the corresponding visual vocabularies, visual words are generated for a query image, and then the images in the gallery database are ranked with respect to the query visual word. The most similar gallery images are then output as the query result.
[1] L. Van Gool, T. Moons, and D. Ungureanu, “Affine/photometric invariants for planar intensity patterns,” in Proc. Fourth European Conference on Computer Vision, Vol. II, pp.
642-651, 1996.
[2] C. Schmid and R. Mohr, “Local grayvalue invariants for image retrieval,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 19, no. 5, pp. 530-535, 1997.
[3] S. Lazebnik, C. Schmid, and J. Ponce, “A sparse texture representation using local affine regions,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp.1265–1278, 2005.
[4] T. Ueshiba and F. Tomita, “Plane-based calibration algorithm for multi-camera systems via factorization of homography matrices,” Proc. Int’l Conf. Computer Vision, vol. 2, pp.
966-973, 2003.
[5] J. Matas, O. Chum, M. Urban, T. Pajdla, “Robust wide-baseline stereo from maximally stable extremal regions,” Image and Vision Computing , vol.22, pp.761–767, 2004.
[6] C. Harris and M. Stephens, “A combined corner and edge detector,” Alvey Vision Conf., pp. 147-151, 1988.
[7] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int’l J.
Computer Vision, vol. 60, no.2, pp. 91–110, 2004.
[8] T. Tuytelaars and L. Van Gool, “Matching widely separated views based on affine invariant regions,” Int’l J. Computer Vision, vol. 59, no. 1, pp. 61-85, 2004.
[9] K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int’l J.
Computer Vision , vol. 60, no. 1, pp. 63–86, 2004.
[10] T. Lindeberg, and J. Garding, “Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure,” Image and Vision Computing, vol.
15, no. 6, pp. 415–434, 1997.
[11] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, and J. Matas, “A comparison of affine region detectors,” Int’l J. Computer Vision, vol. 65, no. 1/2, pp. 43–72, 2005.
[12] K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1615–1630, 2005.
[13] Jing Li, Nigel M. Allinson, “A comprehensive review of current local features for computer vision,” Neurocomputing, vol. 71, pp.1771– 1787, 2008.
[14] W. Freeman, E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell. Vol.13 no.9 pp. 891–906, 1991.
[15] T. S. Lee, “Image representation using Gabor wavelets,” IEEE Trans. Pattern Anal.
Mach. Intell., vol.18, no. 10, pp. 959-97, 1996.
[16] Janne Heikkilä, “Pattern matching with affine moment descriptors,” Pattern Recognition, vol.37, pp.1825 – 1834, 2004.
[17] Dengsheng Zhang, Guojun Lu, “Review of shape representation and description techniques,” Pattern Recognition, vol. 27, pp. 1–19, 2004.
[18] S. Paschalakis and P. Lee, “Pattern recognition in grey level images using moment based invariant features,” Proc. Seventh International Conference on Image Processing and Its Applications, vol. 1, pp.245-249, 1999.
[19] F. Schaffalitzky and A. Zisserman, “Multi-view matching for unordered image sets,” in Proc. Seventh European Conf. Computer Vision, pp. 414-431, 2002.
[20] C.-H. Teh, 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.
[21] B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada, “Color and Texture Descriptors”, IEEE Trans. Circ. Syst. Video Technol., vol.11, no.6, pp.703–715, 2001.
[22] Y. Ke and R. Sukthankar, “PCA-SIFT: A more distinctive representation for local image descriptors,” in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, vol. 2, pp.
506-513, 2004.
[23] L. M. J. Florack, J. Koenderink, B. M, Ter Haar Romeny, J. J. Koenderink and M. A.
Viergever, “General intensity transformations and differential invariants,” J. Mathematical Imaging and Vision, vol. 4, no. 2, pp. 171-187, 1994.
[24] 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, 1990.
[25] W.Y. Kim, and Y.S. Kim, “A region-based shape descriptor using Zernike moments,”
Signal Processing: Image Communication, vol. 16, pp. 95-102, 2000.
[26] S. K. Hwang, M. Billinghurst and W. Y. Kim, “Local descriptor by Zernike moments for real-time keypoint matching,” IEEE Congress on Image and Signal Processing, pp. 781–785, 2008.
[27] Y. Xin, M. Pawlak, and S. Liao, “Accurate computation of Zernike moments in polar coordinates,” IEEE Trans. Image Process., vol. 16, no. 2, pp. 581–587, 2007.
[28] 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, 2007.
[29] W.Y. Kim, and Y.S. Kim, “Robust rotation angle estimator,” IEEE Trans. Pattern Anal.
Mach. Intell., vol. 21, no. 8, pp. 768–773, 1999.
[30] http://www.robots.ox.ac.uk/~vgg/research/affine/.
[31] Z. Chen and H.L. Chou, “A novel 3D planar object reconstruction from multiple uncalibrated images using the plane-induced homographies,” Pattern Recognition Letters, vol.
25, no. 12, pp. 1399-1410, 2004.
[32] M. K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. Inf. Theory, pp.
179-187, 1962.
[33] A. Baumberg, “Reliable feature matching across widely separated views,” in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 774-781, 2000.
[34] A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” in Proc. Of the IEEE, Vol. 69, No. 5, pp. 529-550, 1981.
[35] D. J. Fleet and A. D. Jepson, “Stability of phase information,” IEEE Trans. Pattern Anal.
Mach. Intell., vol.15, no.12, pp.1253–1268, 1993.
[36] D. J. Fleet and A. D. Jepson, “Computation of component image velocity from local phase information,” International Journal of Computer Vision, vol.5, no.1, pp. 77- 104, 1990.
[37] G. Carneiro and A. D. Jepson. “Multi-scale phase-based local features,” in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. I/736– I/743, 2003.
[38] S. Winder and M. Brown, “Learning local image descriptors,” in Proc. IEEE Int’l Conf.
Computer Vision and Pattern Recognition, pp.1–8, 2007.
[39] M. R Teague, “Image analysis via the general theory of moments,” J. Opt. Soc. Am., vol.
70, no. 8, 1980, pp. 1468-1478.
[40] G. Amayeh, A. Erol, G. Bebis, and M. Nicolescu. “Accurate and efficient computation of high order Zernike moments,” First Int. Sym. on Vision and Computation, NV, USA, pages 462–469, 2005.
[41] S. K. Hwang and W. Y. Kim, “A novel approach to the fast computation of Zernike moments,” Pattern Recognition, vol. 39, no. 11, pp. 2065-2076, Nov. 2006.
[42] L. Kotoulas and I. Andreadis. ”Real-time computation of Zernike moments”. IEEE Trans.
on Circuits and Sys. for Video Tech., 15:801–809, 2005.
[43] A. A. Salah, E. Alpaydin and L. Akarun, “A selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp.420–425, 2002.
[44] J Himberg, K Korpiaho, H Mannila, J Tikanmaki, H, “Time series segmentation for context recognition in mobile devices”, In Proc. Int’l Conf. Data Mining, pp. 203–210, 2001.
[45] G Fritz, C Seifert, L Paletta, “A mobile vision system for urban detection with informative local descriptors” In Proc. Int’l Conf. Computer Vision Systems, pp. 30–38, 2006.
[46] E Bruns, B Brombach, T Zeidler, O Bimber, “Enabling mobile phones to support large-scale museum guidance”, IEEE multimedia, pp. 16–25, 2007.
[47] E. Baratis, E. G.M. Petrakis and E. Milios, “Automatic website summarization by image content: a case study with logo and trademark images,” IEEE Trans. Knowledge and Data Engineering, vol. 20, no. 9, pp. 1195-1204, 2008.
[48] G. Zhu and D. Doermann, “Automatic document logo detection,” In Proc. Int’l Conf.
Document Analysis and Recognition, vol.2, pp. 864 – 868, 2007.
[49] G. Cui, L. Chen, and J. Li, “Billboard advertising detection in sport tv,” In Proc. Int’l Conf. Signal Processing and Its Applications, pp.537-540, 2003.
[50] P. Nieto, J.R. Cózar, J.M. González-Linares, N. Guil, “A TV-logo classification and learning system,” In Proc. Int’l Conf. Image Processing, pp. 2548 - 2551, 2008.
[51] Wang Jinqiao, Liu Qingshan, Duan Lingyu, Lu Hanqing, and Xu Changsheng,
“Automatic TV logo detection, tracking and removal in broadcast video,” Multimedia
Modeling (2), pp. 63–72, 2007.
[52] W. Yunqiong, L. Zhifang, and X. Fei, “A fast coarse-to-fine vehicle logo detection and recognition method,” In Proc. Int’l Conf. Robotics and Biometrics, pp. 691-696, 2007.
[53] L. Xia, F. Qi, and Q. Zhou, “A learning-based logo recognition algorithm using SIFT and efficient correspondence matching,” In Proc. Int’l Conf. Information and Automation, pp.
1767-1772, 2008.
[54] M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. Comput. Vis., vol. 7, pp. 11–32, 1991.
[55] B. S. Manjunath and W. Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Trans. Pattern Anal. Machine Intell., vol.18, pp. 837–841, Aug. 1996.
[56] A. Hesson and D. Androutsos, “Logo classification using Haar wavelet co-occurrence histograms,” In Proc. Canadian Conference on Electrical and Computer Engineering, pp.927-930, 2008.
[57] S. Li, M.-C. Lee, and C.-M. Pun, “Complex Zernike moments features for shape-based image retrieval,” IEEE Tran. Systems, Man, and Cybernetics—Part A: Systems and Humans, vol. 39, no. 1, pp.227 - 237, 2009.
[58] C. S. Won, D. K. Park, and S.-J. Park, “Efficient use of MPEG-7 edge histogram descriptor,” ETRI Journal, vol.24, no.1, pp. 23-30, 2002.
[59] D.-M. Tsai and C.-H. Chiang, “Rotation-invariant pattern matching using wavelet decomposition”, Pattern Recognition Letters, vol.23 pp.191-201, 2002.
[60] L. G. Brown, “A survey of view registration techniques,” ACM Comput. Surv. vol.24, no.4, pp.335-376, 1992.
[61] J. B. A. Maintz and M. A. Viergever, “A survey of medical view registration,” Med.
Image Anal., vol.2, no.1, pp.1–37, 1998.
[62] T. Mäkelä, P. Clarysse, O. Sipilä, N. Pauna, Q. C. Pham, T. Katila, and I. E. Magnin, “A review of cardiac image registration methods,” IEEE Trans. Med. Imaging, vol.21, no.9,
pp.1011-1021, 2002.
[63] B Zitová and J Flusser, “Image registration methods: a survey,” Image Vision Comput., vol.21, pp. 977-1000, 2003.
[64] J. Ton and A. K. Jain, “Registering Landsat images by point matching,” IEEE Tran.
Geosci. and Remote Sensing, vol.27, no.5, pp.642-651, 1989.
[65] L.M.G. Fonseca and M.H.M. Costa, “Automatic registration of satellite images”, In Proceedings of Brazilian Symposium on Computer Graphics and Image Processing X, pp.
219 –226, 1997.
[66] Z. Yang and F. S. Cohen, “Image registration and object recognition using affine invariants and convex hulls,” IEEE Trans. Image Process, vol.8, no.7, pp. 934-946, 1999.
[67] G. Lei, “Recognition of planar objects in 3-D space from single perspective views using cross ratio,” IEEE Trans. Robot. Automat., vol.6, no.4, pp. 432-437, 1990.
[68] H. Lamdan, J. T. Schwartz and H. J. Wolfson, “Affine invariant model-based object recognition,” IEEE Trans. Robot. Automat., vol.6 no.5, pp. 578-589, 1990.
[69] D. F. Huber and M. Hebert, “Fully automatic registration of multiple 3D data sets,”
Image Vision Comput., vol.21, pp.637-650, 2003.
[70] Q. Zheng and R. Chellappa, “Automatic feature point extraction and tracking in image sequences for arbitrary camera motion,” Int. J. Comput. Vision, vol.15, pp. 31–76, 1995.
[71] C. Shekhar, V. Govindu, and R. Chellappa, “Multi-sensor view registration by feature consensus”, Pattern Recognit., vol.32, pp.39–52, 1999.
[72] F. Ola and J. A. Marchant, “Matching feature points in image sequences through a region-based method,” Comput. Vis. Image Und., vol.66, no.3, pp.271-285, 1997.
[73] P. Bao and D. Xu, “Complex wavelet-based image mosaics using edge-preserving visual perception modeling,” Computer & Graphics, vol.23, pp.309-321, 1999.
[74] Martin A. Fischler and Robert C. Bolles, “Random sample consensus: a paradigm for
model fitting with applications to image analysis and automated cartography,” ACM Commun., vol.24, no.6, pp.381-395, 1981.
[75] Z. Zhang, R. Deriche, O. Faugeras, Q. T. Luong, “A robust technique for matching two uncalibrated images through the recovery of the unknown epiploar geometry,” Artif. Intell., vol.78, pp.87-119, 1995.
[76] B.D. Lucas and T. Kanade, “An iterative view registration technique with an application to stereo vision,” In Proc. Image Understanding Workshop, pp.121-130, 1981.
[77] S. Christy and R. Horaud, “Euclidean shape and motion from multiple perspective views by affine iterations,” IEEE Trans. Pattern Anal. Mach. Intell., vol.18, no.11, pp. 1098-1104, 1996.
[78] S. K. Sun, Z. Chen and T. L. Chia, “Invariant feature extraction and object shape matching using Gabor filtering,” Recent Advances in Visual Information Systems, Lecture notes in computer science, vol. 2314, Springer Berlin Heidelberg, pp.95-104, 2002.
[79] F. Isgrò, and M. Pilu, “A fast and robust image registration method based on an early consensus paradigm,” Pattern Recognit. Lett., Vol.25, pp.943–954, 2004.
[80] W. Wang and Y. C. Chen, “Image registration by control points pairing using the invariant properties of line segments,” Pattern Recognit. Lett., vol.18, pp. 269-281, 1997.
[81] Y. Dufournaud, C. Schmid and Radu Horaud, “Image matching with scale adjustment,
“ Computer Vision and Image Understanding, vol.93, no.2, pp.175-194, 2004.
[82] J. Flusser and T. Suk, “A moment-based approach to registration of images with affine geometric distortion,” IEEE Tran. Geosci. Remote Sensing, vol.32, no.2, pp.382-387, 1994.
[83] Y. Bentoutou, N. Taleb, K. Kpalma, J. Ronsin, “An automatic image registration for applications in remote sensing”, IEEE Tran. Geosci. Remote Sensing, vol.43, pp. 2127-2137, 2005.
[84] T. Suk and J. Flusser, “Point-based projective invariants,” Pattern Recognit., vol.33, pp.
251-261, 2000.
[85] G. Stockman, S. Kopstein, S. Benett, “Matching images to models for registration and object detection via clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol.4, pp. 229–241, 1982.
VITA
NAME: Shu-Kuo Sun
BIRTH: Jan. 23th, 1967, Kaohsiung, R.O.C.
EDUCATION:
B.Sc., Department of Survey Engineering, Chung Cheng Institute of Technology, Sep. 1985 – Jun. 1989.
M.Sc., Department of Electronics Engineering, Chung Cheng Institute of Technology, Sep. 1991 – Jun. 1993.
Ph.D., Department of Computer Science, National Chiao Tung University, Sep. 1999 – present.
AWARDS:
Excellent paper of 2008 IPPR Conference on Computer Vision, Graphics and Image processing (with Prof. Zen Chen).
RESEARCH INTERESTS:
Image Processing Pattern Recognition Computer Vision Remote Sensing