We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
We present a model of a `gas of circles', the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short ran...
Peter Horvath, Ian Jermyn, Zoltan Kato, and Josian...
The Gaussian Mixture Model (GMM) is often used in conjunction with Mel-frequency cepstral coefficient (MFCC) feature vectors for speaker recognition. A great challenge is to use ...
We present a new approximation algorithm based on an exact representation of the state space S, using decision diagrams, and of the transition rate matrix R, using Kronecker algeb...
Andrew S. Miner, Gianfranco Ciardo, Susanna Donate...
First IEEE International Workshop on Biologically Motivated Computer Vision, Seoul, Korea (May 2000). There is considerable evidence that object recognition in primates is based o...