Color recognition in outdoor images

The color associated with an object in machine vision images is not constant; under varying illuminating and viewing conditions (such as in outdoor images), the perceived color of an object can vary significantly, thus making color-based recognition difficult. Existing methods in color-based recognition have been applied mostly to indoor and/or constrained imagery, but not to realistic outdoor data. This work analyzes the variation of object color in outdoor images with respect to existing models of daylight illumination and surface reflectance. Two approaches for color recognition are then proposed: the first develops context-based models of daylight illumination and hybrid surface reflectance, and predicts the color of objects based on scene context. The second method shows that object color can be nonparametrically "learned" through classification methods such as Neural Networks and Multivariate Decision Trees. The methods have been successfully tested in domains such as road/highway scenes, off-road navigation and military target detection.

[1]  Bui Tuong Phong.  Illuminat~on for computer generated images , 1973 .

[2]  Dean A. Pomerleau,et al.  Neural Network Perception for Mobile Robot Guidance , 1993 .

[3]  Yasuhiro Hayashi,et al.  Recovery of Illuminant and Surface Colors from Images Based on the CIE Daylight , 1994, ECCV.

[4]  Charles E. Thorpe,et al.  Color Vision For Road Following , 1989, Other Conferences.

[5]  D. B. Judd,et al.  Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature , 1964 .

[6]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[7]  K. Ikeuchi,et al.  Reflectance analysis under solar illumination , 1994, Proceedings of the Workshop on Physics-Based Modeling in Computer Vision.

[8]  Brian V. Funt,et al.  The State of Computational Color Constancy , 1995 .

[9]  Graham D. Finlayson,et al.  Color constancy in diagonal chromaticity space , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

[11]  Bui Tuong Phong.  Illumination for computer generated pictures , 1975, Commun. ACM.

[12]  Terrance E. Boult,et al.  Correcting chromatic aberrations using image warping , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[14]  Steven A. Shafer,et al.  Obtaining accurate color images for machine-vision research , 1990, Electronic Imaging.

[15]  Geoffrey E. Hinton,et al.  A general framework for parallel distributed processing , 1986 .

[16]  Takeo Kanade,et al.  Color Image Analysis With An Intrinsic Reflection Model , 1988, [1988 Proceedings] Second International Conference on Computer Vision.