OBJECT LOCALIZATION USING TEXTURE MOTIFS AND MARKOV RANDOM FIELDS

 

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

Abstract

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.