This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of ...
We present a method to classify and localize human actions in video using a Hough transform voting framework. Random trees are trained to learn a mapping between densely-sampled f...
Using a lexicon can often improve character recognition under challenging conditions, such as poor image quality or unusual fonts. We propose a flexible probabilistic model for c...
Jerod J. Weinman, Erik G. Learned-Miller, Allen R....
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
A novel approach to computer vision is outlined, involving the use of imprecise probabilities to connect a deep learning based hierarchical vision system with both local feature de...