BMVC 2004, Kingston, 7th-9th Sept, 2004

A Hybrid Object-Level/Pixel-Level Framework For Shape-based Recognition
O. Carmichael and M. Hebert (Carnegie Mellon University, USA)

This paper presents a technique for shape-based recognition that fuses pixellevel
and object-level approaches into a unified framework. A pixel-level
algorithm classifies individual pixels as belonging to a target object or clutter
based on automatically-selected shape features computed in a spatial arrangement
around them; an object-level algorithm classifies object-sized rectangular
image regions as objects or clutter by aggregating pixel classifier scores
in the regions. We train a cascade of interleaved pixel-level and objectlevel
modules to quickly localize complex-shaped objects in highly cluttered
scenes under arbitrary out-of-image-plane rotation. Experimental results on
a large set of real, highly-cluttered images of a common object under arbitrary
out of image plane rotation demonstrate improvements over cascades of
strictly pixel-level modules.
(pdf article)