Intensity- and Gradient-Based Stereo Matching Using Hierarchical Gaussian Basis Functions

We propose a stereo correspondence method by minimizing intensity and gradient errors simultaneously. In contrast to conventional use of image gradients, the gradients are applied in the deformed image space. Although a uniform smoothness constraint is imposed, it is applied only to nonfeature regions. To avoid local minima in the function minimization, we propose to parameterize the disparity function by hierarchical Gaussians. Both the uniqueness and the ordering constraints can be easily imposed in our minimization framework. Besides, we propose a method to estimate the disparity map and the camera response difference parameters simultaneously. Experiments with various real stereo images show robust performances of our algorithm.

[1]  Mohan M. Trivedi,et al.  Multi-Primitive Hierarchical (MPH) Stereo Analysis , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[3]  Olga Veksler,et al.  Disparity component matching for visual correspondence , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  William B. Thompson,et al.  TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2009 .

[5]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..

[6]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[7]  Yair Weiss,et al.  Smoothness in layers: Motion segmentation using nonparametric mixture estimation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Alan L. Yuille,et al.  Multilevel Enhancement and Detection of Stereo Disparity Surfaces , 1995, Artif. Intell..

[9]  A. Ardeshir Goshtasby,et al.  Curve Fitting by a Sum of Gaussians , 1994, CVGIP Graph. Model. Image Process..

[10]  Ingemar J. Cox,et al.  A Maximum Likelihood Stereo Algorithm , 1996, Comput. Vis. Image Underst..

[11]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[12]  Charles Hansen,et al.  Rectification of images for binocular and trinocular stereovision , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[13]  Berthold K. P. Horn Robot vision , 1986, MIT electrical engineering and computer science series.

[14]  Richard Szeliski,et al.  Fast shape from shading , 1990, CVGIP Image Underst..

[15]  Kim L. Boyer,et al.  Structural Stereopsis for 3-D Vision , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Gérard G. Medioni,et al.  3-D Surface Description from Binocular Stereo , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Daniel Scharstein,et al.  Matching images by comparing their gradient fields , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[18]  Narendra Ahuja,et al.  Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Demetri Terzopoulos,et al.  Image Analysis Using Multigrid Relaxation Methods , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Ingemar J. Cox,et al.  A maximum-flow formulation of the N-camera stereo correspondence problem , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[21]  D. Burr A dynamic model for image registration , 1981 .

[22]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[23]  Narendra Ahuja,et al.  Matching Two Perspective Views , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Ramakant Nevatia,et al.  Segment-based stereo matching , 1985, Comput. Vis. Graph. Image Process..

[25]  Songde Ma,et al.  Implicit and Explicit Camera Calibration: Theory and Experiments , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Richard Szeliski,et al.  Stereo Matching with Transparency and Matting , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[27]  Takeo Kanade,et al.  A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  John E. W. Mayhew,et al.  Psychophysical and Computational Studies Towards a Theory of Human Stereopsis , 1981, Artif. Intell..

[29]  W. Eric L. Grimson,et al.  Computational Experiments with a Feature Based Stereo Algorithm , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Heinrich H. Bülthoff,et al.  Stereo Integration, Mean Field Theory and Psychophysics , 1990, ECCV.

[31]  Michael A. Gennert,et al.  Brightness-based Stereo Matching , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[32]  Takeo Kanade,et al.  Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Gerd Hirzinger,et al.  Parametric Shape-from-Shading by Radial Basis Functions , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[35]  Richard Szeliski,et al.  Motion Estimation with Quadtree Splines , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Martin D. Levine,et al.  Computer determination of depth maps , 1973, Comput. Graph. Image Process..