Color and scale: The spatial structure of color images

Abstract

For grey-value images, it is well accepted that the neighborhood rather than the pixel carries the geometrical interpretation. Interestingly the spatial configuration of the neighborhood is the basis for the perception of humans. Common practise in color image processing, is to use the color information without considering the spatial structure. We aim at a physical basis for the local interpretation of color images. We propose a framework for spatial color measurement, based on the Gaussian scale-space theory. We consider a Gaussian color model, which inherently uses the spatial and color information in an integrated model. The framework is well-founded in physics as well as in measurement science. The framework delivers sound and robust spatial color invariant features. The usefulness of the proposed measurement framework is illustrated by edge detection, where edges are discriminated as shadow, highlight, or object boundary. Other applications of the framework include color invariant image retrieval and color constant edge detection.