In this paper, we analyze the formation of blocking artifacts as a result of quantization of the discrete cosine transform (DCT) coefficients. These artifacts are known to be the ...
Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text analysis. Posterior inference in such models is intractable, and practitioners rely on...
One of the major problems in modeling images for vision tasks is that images with very similar structure may locally have completely different appearance, e.g., images taken under...
A new approach to the recognition of temporal behaviors and activities is presented. The fundamental idea, inspired by work in speech recognition, is to divide the inference probl...
We present an objective approach for evaluating probability elicitation methods in probabilistic models. Our method draws on ideas from research on learning Bayesian networks: if ...