On image reconstruction, numerical stability, and invariance of orthogonal radial moments and radial harmonic transforms

Various radial moments viz. Zernike moments, pseudo Zernike moments, orthogonal Fourier Mellin moments, radial harmonic Fourier moments, Chebyshev-Fourier moments and polar harmonic transforms such as polar complex exponential transforms, polar cosine transforms and polar sine transforms satisfy orthogonal principle. By virtue of which these moments and transforms possess minimum information redundancy and thereby exhibit a good characteristic of image representation. In this paper, a complete comparative analysis is performed by considering image reconstruction capability of each individual moment and transform. The orthogonal properties of above mentioned moments along with the causes of their, reconstruction error, numerical stability and invariance are described.

[1]  Jian Zou,et al.  Character Reconstruction with Radial-Harmonic-Fourier Moments , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[2]  Y. Sheng,et al.  Orthogonal Fourier–Mellin moments for invariant pattern recognition , 1994 .

[3]  Chee-Way Chong,et al.  A comparative analysis of algorithms for fast computation of Zernike moments , 2003, Pattern Recognit..

[4]  Yongqing Xin,et al.  Geometrically robust image watermarking via pseudo-Zernike moments , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[5]  Whoi-Yul Kim,et al.  Content-based trademark retrieval system using visually salient features , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Rajiv Mehrotra,et al.  Edge detection using orthogonal moment-based operators , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[7]  Miroslaw Pawlak,et al.  Accurate Computation of Zernike Moments in Polar Coordinates , 2007, IEEE Transactions on Image Processing.

[8]  Kaamran Raahemifar,et al.  An effective feature extraction method for face recognition , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[9]  Chandan Singh,et al.  Computation of Zernike moments in improved polar configuration , 2009, IET Image Process..

[10]  Ziliang Ping,et al.  Image description with Chebyshev-Fourier moments. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Demetri Psaltis,et al.  Recognitive Aspects of Moment Invariants , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Miroslaw Pawlak,et al.  On the reconstruction aspects of moment descriptors , 1992, IEEE Trans. Inf. Theory.

[14]  M. Teague Image analysis via the general theory of moments , 1980 .

[15]  Andrew Beng Jin Teoh,et al.  Palmprint Verification with Moments , 2004, WSCG.

[16]  Raveendran Paramesran,et al.  On the computational aspects of Zernike moments , 2007, Image Vis. Comput..

[17]  Miroslaw Pawlak,et al.  On the Accuracy of Zernike Moments for Image Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  JiangXudong,et al.  Two-Dimensional Polar Harmonic Transforms for Invariant Image Representation , 2010 .

[19]  A. Bhatia,et al.  On the circle polynomials of Zernike and related orthogonal sets , 1954, Mathematical Proceedings of the Cambridge Philosophical Society.

[20]  Sugata Ghosal,et al.  Segmentation of range images: an orthogonal moment-based integrated approach , 1993, IEEE Trans. Robotics Autom..

[21]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.