Colour object recognition

Since colour characterizes local surface properties and is largely viewpoint insensitive it is a useful cue for object recognition. Indeed, Swain and Ballard have developed a simple scheme, called colour-indexing, which identi es objects by matching colourspace histograms. Their approach is remarkably robust in that variations such as a shift in viewing position, a change in the scene background or even object deformation degrade recognition only slightly. Colour-indexing fails, however, if the intensity or spectral characteristics of the incident illuminant varies. This thesis examines two di erent strategies for rectifying this failure. Firstly we consider applying a colour constancy transform to each image prior to colour-indexing (colours are mapped to their appearance under canonical lighting conditions). To solve for the colour constancy transform assumptions must be made about the world. These assumptions dictate the types of objects which can be recognized by colour-indexing + colour constancy preprocessing. We review several colour constancy algorithms and in almost all cases conclude that their assumptions are too limiting. The exception, a discrete implementation of Forsyth's CRULE, successfully solves the colour constancy problem for sets of simple objects viewed under constant illumination. To circumvent the need for colour constancy preprocessing and to recognize more complex object sets we consider indexing on illuminant invariants. Three illuminant invariants|volumetric, opponent and ratio|are examined. Each characterizes local surface properties, is largely viewpoint insensitive and is independent of both the intensity and spectral characteristics of the incident illuminant. We develop an algorithm, called Colour constant colour-indexing, which identi es objects by matching colour ratio-space histograms. In general our algorithm performs comparably iii with colour-indexing under xed illumination, but substantially better than colourindexing under varying illumination. iv Acknowledgements My sincerest thanks go to my supervisor Dr. Brian V. Funt. He has been exceptionally supportive throughout the duration of this thesis. In particular I greatly appreciate Dr. Funt's approach to research; the free exchange of ideas forms the foundation of a stimulating working environment. Regarding research, I am also indebted to Dr. Mark Drew. His research skills and tireless enthusiasm are an example to all. Special thanks go to my fellow graduate students in the Computing Science department. They contribute greatly to the general research environment and more importantly supply support and friendship. In particular I'd like to mention computing science stalwarts|Allan Bennet-Brown, Gilles Dionne and Mark Mezofenyi. In a category by himself is Sanjeev Mahajan|as well as being a close friend, he provided valuable insights concerning the probability results of this thesis. My nal thanks goes to the departmental advisor|Elma Krbavac. I met Elma whilst studying in Glasgow in May 1989. She was my introduction to the computing science department at Simon Fraser University. This meeting led to an adventure which is still continuing. v