= PDF Reprint, = BibTeX entry, = Online Abstract
T. N. Mundhenk, V. Navalpakkam, H. Makaliwe, S. Vasudevan, L. Itti, Biologically inspired feature based categorization of objects, In: Proc. SPIE Human Vision and Electronic Imaging IX (HVEI04), San Jose, CA, (B. Rogowitz, T. N. Pappas Ed.), Vol. 5292, Bellingham, WA:SPIE Press, Jan 2004. (Cited by 7)
Abstract: We have developed a method for clustering features into objects by taking those features which include intensity, orientations and colors from the most salient points in an image as determined by our biologically motivated saliency program. We can train a program to cluster these features by only supplying as training input the number of objects that should appear in an image. We do this by clustering from a technique that involves linking nodes in a minimum spanning tree by not only distance, but by a density metric as well. We can then form classes over objects or object segmentation in a novel validation set by training over a set of seven soft and hard parameters. We discus as well the uses of such a flexible method in landmark based navigation since a robot using such a method may have a better ability to generalize over the features and objects. .
Themes: Computational Modeling, Model of Bottom-Up Saliency-Based Visual Attention, Computer Vision
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
This page generated by bibTOhtml on Fri Jan 26 09:25:23 PST 2018