We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarch...
Wei Zhang 0002, Gregory J. Zelinsky, Dimitris Sama...
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
The problem of recognizing classes of objects as opposed to special instances requires methods of comparing images that capture the variation within the class while they discrimina...
This paper investigates the use of the Bayesian inference for devising an unsupervised sketch rendering procedure. As likelihood model of this inference, we exploit the recent sta...
In this paper, a hierarchical genetic algorithm for disparity estimation is presented. The goal, to estimate reliable disparity fields with low computational cost, is reached usin...
L. J. Luo, D. R. Clewer, David R. Bull, Cedric Nis...