Object segmentation using feature based conditional morphology

This paper presents a new technique to segment objects of interest from cluttered background with varying edge densities and illumination conditions from gray scale imagery. An optimal background model is generated and an index of disparity of the objects from this model is computed. This index estimates the disparity, both in terms of edge densities and edge orientation. We introduce feature based conditional morphology to process the representations that are most likely to belong to the object of interest and obtain a distilled edge map. These edges are linked using N^th order interpolation to get the final outline of the object. We compare our approach with 9 contemporary background subtraction algorithms (Toyama et al. (1999)). Our approach shows significant performance advantages and uses only the gray scale images, while the other approaches also need the color images for their algorithms. A comparison with the conventional morphological techniques is also made to highlight the advantages of our algorithms.