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Color pixel measurements in an image provide useful information for object recognition. Unfortunately, these measurements depend on the illumination environment as well as on the intrinsic color of an object. Color constancy is the ability of a vision system to assign a color description to an object that does not depend on the illumination environment allowing the system to recognize objects under many different illumination conditions. In reference [1], we demonstrate a color constancy method which uses highlights to estimate the color of the illumination. In subsequent work (references [2]-[7]) we use linear models for spectral reflectance functions to derive methods for illumination-invariant recognition. References [2] and [4] describe methods for computing illumination invariants from global and local color distributions. References [3] and [5] develop algorithms for illumination-invariant recognition that incorporate both color and spatial information. Reference [6] uses a linear reflectance model for the illumination-invariant recognition of local image structure. In [8], we combine several of these methods for recognition in multispectral satellite images.

[1] G. Healey

``Estimating Spectral Reflectance using Highlights,'' Image and Vision Computing, 9(5):333-337, October 1991. abstract

[2] G. Healey and D. Slater

``Global Color Constancy: Recognition of objects by use of illumination invariant properties of color distributions,'' Journal of the Optical Society of America A, 11(11):3003-3010, November 1994. abstract

[3] G. Healey and L. Wang

``Illumination-Invariant Recognition of Texture in Color Images,'' Journal of the Optical Society of America A, 12(9):1877-1883, September 1995. abstract

[4] D. Slater and G. Healey

``Recognizing 3-D Objects using Local Color Invariants,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(2):206-210, February 1996. abstract

[5] G. Healey and A. Jain

``Retrieving Multispectral Satellite Images using Physics-based Invariant Representations,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8):842-848, August 1996. abstract

[6] G. Healey and D. Slater

``Computing Illumination-Invariant Descriptors of Spatially Filtered Color Image Regions,'' IEEE Transactions on Image Processing, 6(7):1002-1013, July 1997. abstract

[7] D. Slater and G. Healey

``The Illumination-Invariant Matching of Deterministic Local Structure in Color Images,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(10):1146-1152, October 1997. abstract

[8] L. Wang and G. Healey

``Using Zernike Moments for the Illumination and Geometry Invariant Classification of Multispectral Texture,'' IEEE Transactions on Image Processing, 7(2):196-203, 1998. abstract

[9] D. Slater and G. Healey

``Modeling the Sensitivity of Moment Invariants in a Recognition System,'' Journal of the Optical Society of America A, 15(5):1068-1076, May 1998. abstract

[10] L. Wang and G. Healey

``Using Multiband Filtered Energy Matrices for Recognition and Illumination Correction,'' Optical Engineering, 37(10), October, 1998. abstract

[11] B. Thai and G. Healey

``Modeling and Classifying Symmetries using a Multiscale Opponent Color Representation,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1224-1235, 1998. abstract

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Last modified: 27 November 1998