The Color Adjacency GraphRepresentation of Multi-Coloured

A new representation for objects with multiple colours-the colour adjacency graph (CAG)-is proposed. Each node of the CAG represents a single chromatic component of the image deened as a set of pixels forming a unimodal cluster in the chromatic scattergram. Edges encode information about adjacency of colour components and their reeectance ratio. The CAG is related to both the histogram and region adjacency graph representations. It is shown to be preserving and combining the best features of these two approaches while avoiding their drawbacks. The proposed approach is tested on a range of diicult object recognition and localisation problems involving complex imagery of non rigid 3D objects under varied viewing conditions with excellent results.

[1]  Glenn Healey,et al.  Using color for geometry-insensitive segmentation , 1989 .

[2]  Michael J. Swain,et al.  Indexing via color histograms , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[3]  L. Maloney,et al.  Color constancy: a method for recovering surface spectral reflectance. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[4]  Michael J. Swain,et al.  The capacity of color histogram indexing , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Josef Kittler,et al.  A locally sensitive method for cluster analysis , 1976, Pattern Recognit..

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

[7]  Glenn Healey.  A parallel color algorithm for segmenting images of 3-D scenes , 1989, Sixth Multidimensional Signal Processing Workshop,.

[8]  Shree K. Nayar,et al.  Reflectance ratio: A photometric invariant for object recognition , 1993, 1993 (4th) International Conference on Computer Vision.

[9]  S. Nayar,et al.  Computing Reeectance Ratios from an Image , 1993 .

[10]  Tanveer F. Syeda-Mahmood.  Data and Model-Driven Selection using Color Regions , 1992, ECCV.