We present a class of statistical models for part-based object recognition that are explicitly parameterized according to the degree of spatial structure they can represent. These...
David J. Crandall, Pedro F. Felzenszwalb, Daniel P...
This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure...
We address the problem of object detection and segmentation using holistic properties of object shape. Global shape representations are highly susceptible to clutter inevitably pr...
A method is introduced to learn and represent similarity with linear operators in kernel induced Hilbert spaces. Transferring error bounds for vector valued large-margin classifie...
We present a system for recognising human behaviour given a symbolic representation of surveillance videos. The input of our system is a set of timestamped short-term behaviours, t...