Wavelets/multiwavelets bases and correspondence estimation problem: An analytic study

Correspondence estimation in one of the most active research areas in the field of computer vision and number of techniques has been proposed, possessing both advantages and shortcomings. Among the techniques reported, multiresolution analysis based stereo correspondence estimation has gained lot of research focus in recent years. Although, the most widely employed medium for multiresolution analysis is wavelets and multiwavelets bases, however, relatively little work has been reported in this context. In this work we have tried to address some of the issues regarding the work done in this domain and the inherited shortcomings. In the light of these shortcomings, we propose a new technique to overcome some of the flaws that could have significantly impact on the algorithm performance and has not been addressed in the earlier propositions. Proposed algorithm uses multiresolution analysis enforced with wavelets/multiwavelts transform modulus maxima to establish correspondences between the stereo pair of images. Variety of wavelets and multiwavelets bases, possessing distinct properties such as orthogonality, approximation order, short support and shape are employed to analyse their effect on the performance of correspondence estimation. The idea is to provide knowledge base to understand and establish relationships between wavelets and multiwavelets properties and their effect on the quality of stereo correspondence estimation.

[1]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[2]  Ruigang Yang,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation and Occlusion Handling , 2006, CVPR.

[3]  Jean-Christophe Olivo-Marin,et al.  Automatic registration of images by a wavelet-based multiresolution approach , 1995, Optics + Photonics.

[4]  Julian Magarey,et al.  Motion estimation using a complex-valued wavelet transform , 1998, IEEE Trans. Signal Process..

[5]  Miao Liao,et al.  High-Quality Real-Time Stereo Using Adaptive Cost Aggregation and Dynamic Programming , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[6]  Stéphane Mallat,et al.  Zero-crossings of a wavelet transform , 1991, IEEE Trans. Inf. Theory.

[7]  I. Daubechies,et al.  Two-scale difference equations I: existence and global regularity of solutions , 1991 .

[8]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[9]  I. Daubechies Orthonormal bases of compactly supported wavelets , 1988 .

[10]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[11]  Demetri Terzopoulos,et al.  Signal matching through scale space , 1986, International Journal of Computer Vision.

[12]  Egon Dorrer,et al.  Automatic image-matching algorithm based on wavelet decomposition , 1994, Other Conferences.

[13]  He-Ping Pan,et al.  General stereo image matching using symmetric complex wavelets , 1996, Optics & Photonics.

[14]  A. Haar Zur Theorie der orthogonalen Funktionensysteme , 1910 .

[15]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[16]  G. Strang,et al.  Orthogonal multiwavelets with vanishing moments , 1994 .

[17]  J. Lagarias,et al.  EXISTENCE AND GLOBAL REGULARITY OF SOLUTIONS * , 2022 .

[18]  Luigi di Stefano,et al.  A fast area-based stereo matching algorithm , 2004, Image Vis. Comput..

[19]  I. Cohen,et al.  Adaptive time-frequency distributions via the shift-invariant wavelet packet decomposition , 1998, Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380).

[20]  Fangmin Shi,et al.  SSD Matching Using Shift-Invariant Wavelet Transform , 2001, BMVC.

[21]  DaubechiesIngrid Orthonormal bases of compactly supported wavelets II , 1993 .

[22]  V. Strela Multiwavelets--theory and applications , 1996 .

[23]  C. Chui,et al.  A study of orthonormal multi-wavelets , 1996 .

[24]  Saeid Nahavandi,et al.  Accurate 3D modelling for automated inspection : a stereo vision approach , 2005 .

[25]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[26]  D. Nistér,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Thomas S. Huang,et al.  Motion and structure from feature correspondences: a review , 1994, Proc. IEEE.

[28]  P. Laguna,et al.  Signal Processing , 2002, Yearbook of Medical Informatics.

[29]  Bülent Bilgehan,et al.  Multi-wavelets from B-spline super-functions with approximation order , 2002, Signal Process..

[30]  Emanuele Trucco,et al.  Symmetric Stereo with Multiple Windowing , 2000, Int. J. Pattern Recognit. Artif. Intell..

[31]  Julian Magarey,et al.  Multiresolution stereo image matching using complex wavelets , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[32]  S. Mallat A wavelet tour of signal processing , 1998 .

[33]  Gilbert Strang,et al.  Short wavelets and matrix dilation equations , 1995, IEEE Trans. Signal Process..

[34]  Takeo Kanade,et al.  A Cooperative Algorithm for Stereo Matching and Occlusion Detection , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Asim M. Bhatti 3D depth estimation using wavelet analysis based stereo vision approach , 2005 .

[36]  Laurent Moll,et al.  Real time correlation-based stereo: algorithm, implementations and applications , 1993 .

[37]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[38]  Michael Unser,et al.  Multiresolution image registration procedure using spline pyramids , 1993, Optics & Photonics.

[39]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[40]  He-Ping Pan,et al.  Uniform Full-Information Image Matching Using Complex Conjugate Wavelet Pyramids , 1996 .

[41]  Hairong Qi,et al.  M-band multi-wavelets from spline super functions with approximation order , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[42]  Richard Szeliski,et al.  Stereo Matching with Nonlinear Diffusion , 1998, International Journal of Computer Vision.