Gross Segmentation Of Color Images Of Natural Scenes For Computer Vision Systems

Abstract

This paper describes a new systematic method for gross segmentation of color images of natural scenes. It is developed within the context of the human visual system and mathematical pattern recognition theory. The eventual goal of the research is to integrate these two concepts to obtain visually distinct image segments which are more reliable and tractable for higher level analysis or interpretation process involved in a computer vision system. This new computational technique is proposed in accordance with the human color perception to detect the image clusters efficiently using only one-dimensional (1-D) histograms of the L*,H°,C cylindrical coordinates of the (L*,a*,b*)-uniform color system selected as the feature space. The method is employed together with the Fisher linear discriminant function to isolate and extract the detected image clusters correctly. In order to obtain the features most useful for a given image, a new feature extraction technique is proposed. It is a statistical-structural method which makes use of the spatial and spectral information contained in the local areas of the image domain. A set of smoothing and line templates are developed and used to refine the extracted image regions in the spatial domain. They can also be applied to the binary images for smoothing purposes.