Reeectance Based Object Recognition 1

Neighboring points on a smoothly curved surface have similar surface orientations and illumination conditions. Therefore, their brightness values can be used to compute the ratio of their re ectance coe cients. Based on this observation, we develop an algorithm that estimates a re ectance ratio for each region in an image with respect to its background. The algorithm is e cient as it computes ratios for all image regions in just two raster scans. The region re ectance ratio represents a physical property that is invariant to illumination and imaging parameters. Several experiments are conducted to demonstrate the accuracy and robustness of ratio invariant. The ratio invariant is used to recognize objects from a single brightness image of a scene. Object models are automatically acquired and represented using a hash table. Recognition and pose estimation algorithms are presented that use the ratio estimates of scene regions as well as their geometric properties to index the hash table. The result is a hypothesis for the existence of an object in the image. This hypothesis is veri ed using the ratios and locations of other regions in the scene. This approach to recognition is e ective for objects with printed characters and pictures. Recognition experiments are conducted on images with illumination variations, occlusions, and shadows. The paper is concluded with a discussion on the simultaneous use of re ectance and geometry for visual perception.

[1]  E. H. Linfoot.  Principles of Optics , 1961 .

[2]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[3]  K. Torrance,et al.  Theory for off-specular reflection from roughened surfaces , 1967 .

[4]  George Nagy,et al.  Feature Extraction on Binary Patterns , 1969, IEEE Trans. Syst. Sci. Cybern..

[5]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[6]  Stephen B. Gray,et al.  Local Properties of Binary Images in Two Dimensions , 1971, IEEE Transactions on Computers.

[7]  Alfred V. Aho,et al.  The Design and Analysis of Computer Algorithms , 1974 .

[8]  Berthold K. P. Horn,et al.  Determining lightness from an image , 1974, Comput. Graph. Image Process..

[9]  J. Koenderink,et al.  Photometric Invariants Related to Solid Shape , 1980 .

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

[11]  Linda G. Shapiro,et al.  A new connected components algorithm for virtual memory computers , 1983, Comput. Vis. Graph. Image Process..

[12]  J. Koenderink,et al.  Geometrical modes as a general method to treat diffuse interreflections in radiometry , 1983 .

[13]  Ramesh C. Jain,et al.  Three-dimensional object recognition , 1985, CSUR.

[14]  Andrew Blake,et al.  Boundary conditions for lightness computation in Mondrian World , 1985, Comput. Vis. Graph. Image Process..

[15]  Charles R. Dyer,et al.  Model-based recognition in robot vision , 1986, CSUR.

[16]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[17]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[18]  David A. Forsyth,et al.  Mutual illumination , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Takeo Kanade,et al.  Determining shape and reflectance of hybrid surfaces by photometric sampling , 1989, IEEE Trans. Robotics Autom..

[20]  Takeo Kanade,et al.  Surface Reflection: Physical and Geometrical Perspectives , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Rakesh Mohan,et al.  Multidimensional indexing for recognizing visual shapes , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Markus A. Stricker,et al.  Color and Geometry as Cues for Indexing , 1992 .

[23]  Graham D. Finlayson,et al.  Colour object recognition , 1992 .

[24]  Daphna Weinshall.  Model-based invariants for 3D vision , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[25]  C. J. Nicol.  A systolic architecture for labeling the connected components of multi-valued images in real time , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Edward H. Adelson,et al.  Recovering Reflectance and Illumination in a World , 1993 .

[27]  J.B. Burns,et al.  View Variation of Point-Set and Line-Segment Features , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Shree K. Nayar,et al.  Removal of specularities using color and polarization , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.