Many perceptual models and theories hinge on treating objects as a collection of constituent parts. When applying these approaches to data, a fundamental problem arises: how can w...
Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-theart technique...
Risheng Liu, Zhouchen Lin, Fernando De la Torre, Z...
Abstract— This paper introduces a novel approach to representing and learning tool affordances by a robot. The tool representation described here uses a behavior-based approach t...
Today’s organisations require techniques for automated transformation of the large data volumes they collect during their operations into operational knowledge. This requirement...
Alexander Artikis, Georgios Paliouras, Franç...
We develop nonparametric Bayesian models for multiscale representations of images depicting natural scene categories. Individual features or wavelet coefficients are marginally de...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...