Using chromatic information in edge-based stereo correspondence

Abstract One approach to developing a faster, more robust solution to the stereo correspondence problem is to seek a more complete and efficient use of available image information. Motivated by the observation that chromatic (color) information is a salient, regional property of surfaces in many natural scenes, the goal of this research has been to gain a fundamental understanding of the use of chromatic information in edge-based stereo correspondence. In particular, the use of chromatic information to characterize intensity edges is analyzed, and the chromatic gradient matching constraint , which specifies disambiguation criteria for edge-based stereo correspondence, is developed. The result is a theoretical construction for developing edge-based stereo correspondence algorithms which use chromatic information. The efficacy of using chromatic information via this construction is demonstrated by implementing a disparity-gradient-based algorithm and comparing algorithm performance with and without the chromatic gradient matching constraint. The results demonstrate that the use of chromatic information can significantly reduce the ambiguity between potential matches, resulting in increased algorithm accuracy as well as increased algorithm speed.

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

[2]  B. Julesz,et al.  A disparity gradient limit for binocular fusion. , 1980, Science.

[3]  J. Kender Saturation, Heu, And Normalized Color: Calculation, Digitization Effects, and Use. , 1976 .

[4]  Kim L. Boyer,et al.  Robotic Manipulation Experiments Using Structural Stereopsis for 3D Vision , 1986, IEEE Expert.

[5]  Marsha Jo Hannah,et al.  Computer matching of areas in stereo images. , 1974 .

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

[7]  L. Quam Hierarchical warp stereo , 1987 .

[8]  Leonie S. Dreschler-Fischer,et al.  Feature Selection in Colour Images for Token Matching , 1987, IJCAI.

[9]  Alan C. Bovik,et al.  Computation of shape from stereo images with application to biological shape analysis , 1989 .

[10]  A. Bovik,et al.  Computational stereo vision using color , 1988, IEEE Control Systems Magazine.

[11]  T. Kanade,et al.  USING A COLOR REFLECTION MODEL TO SEPARATE HIGHLIGHTS FROM OBJECT COLOR , 1987 .

[12]  Liang-Hua Chen,et al.  Synergistic Smooth Surface Stereo , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[13]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[14]  J P Frisby,et al.  PMF: A Stereo Correspondence Algorithm Using a Disparity Gradient Limit , 1985, Perception.

[15]  Alan C. Bovik,et al.  A contour-based stereo matching algorithm using disparity continuity , 1988, Pattern Recognit..

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

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

[18]  Jake K. Aggarwal,et al.  Segmentation of chromatic images , 1981, Pattern Recognit..

[19]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[20]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[21]  Guner S. Robinson Color Edge Detection , 1977 .

[22]  William B. Thompson,et al.  Disparity Analysis of Images , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Shoji Tominaga,et al.  Expansion of color images using three perceptual attributes , 1987, Pattern Recognit. Lett..

[24]  Grahame B. Smith,et al.  A Stereo Integral Equation , 1986, AAAI.

[25]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Tomaso A. Poggio,et al.  On parallel stereo , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[27]  Thomas O. Binford,et al.  Depth from Edge and Intensity Based Stereo , 1981, IJCAI.

[28]  Wilson S. Geisler,et al.  Color as a source of information in the stereo correspondence process , 1990, Vision Research.

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

[30]  A. Peter Blicher The Stereo Matching Problem From the Topological Viewpoint , 1983, IJCAI.

[31]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  W E Grimson,et al.  A computer implementation of a theory of human stereo vision. , 1981, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[33]  T. O. Binford,et al.  Geometric Constraints In Stereo Vision , 1980, Optics & Photonics.