#90: Fast graph partitioning active contours for image segmentation using histograms


S. K. Nath and K. Palaniappan

EURASIP Journal on Image and Video Processing, pgs. 9p, 2009

detection, segmentation, active contours, motion, classification

PlainText, Bibtex, PDF, URL, DOI, Google Scholar

Abstract

We present a method to improve the accuracy and speed, as well as significantly reduce the memory requirements, for the recently proposed Graph Partitioning Active Contours (GPACs) algorithm for image segmentation in the work of Sumengen and Manjunath (2006). Instead of computing an approximate but still expensive dissimilarity matrix of quadratic size, (Ns2Ms2)/(nsms), for a 2D image of size Ns ×Ms and regular image tiles of size ns ×ms , we use fixed length histograms and an intensity-based symmetric- centrosymmetric extensor matrix to jointly compute terms associated with the complete NsMs × NsMs dissimilarity matrix. This computationally efficient reformulation of GPAC using a very small memory footprint offers two distinct advantages over the original implementation. It speeds up convergence of the evolving active contour and seamlessly extends performance of GPAC to multidimensional images.