Object Localization Using Texture Motifs and Markov Random Fields

S. Newsam, S. Bhagavathy, and B.S. Manjunath

Dept. of Electrical and Computer Engineering
University of California at Santa Barbara
Santa Barbara, CA 93106
Email: snewsam,sitaram,manj [at] ece.ucsb.edu


This work presents a novel approach to object localization in complex imagery. In particular, the spatial extents of objects characterized by distinct spatial signatures at multiple scales are estimated by using statistical models to control a simple region growing process. Texture motifs are used to model the spatial signatures at the smallest, or pixel, scale. Markov random fields are used to model the spatial signatures at the larger, or motif, scale. These models are used to iteratively expand a bounding box to approximate the spatial extent of an object. The approach is applied to localizing geo-spatial objects in high-resolution panchromatic aerial imagery.
[PDF] [BibTex]
S. Newsam, S. Bhagavathy, and B. S. Manjunath,
IEEE International Conference on Image Processing (ICIP), vol. 2, pp. 1049-1052, Barcelona, Spain, Sep. 2003.
Node ID: 350 , DB ID: 148 , VRLID: 118 , Lab: VRL , Target: Conference
Subject: [Image Texture] « Look up more