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Abstract: We develop a probability model over image spaces and demonstrate its broad utility in mammographic image analysis. The model employs a pyramid representation to factor images across scale and a tree-structured set of hidden variables to capture long-range spatial dependencies. This factoring makes the computation of the density functions local and tractable. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters are... (Update)
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...scale decompositions for learning contextual dependencies in images. In previous work, we have presented the details of these two models [2][3]. The first is a discriminative model, called the hierarchical pyramid neural network (HPNN) that utilizes a multi resolution pyramid...
.... signal and image processing applications including classification, segmentation, compression, synthesis and alenoising [15] 16] 17] [18]. The parameters of the two state zero mean HMT con sist of, 1) the probability mass function p S describing the high low variance...
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BibTeX entry: (Update)
C.D. Spence, L. Parra, and P. Sajda, "Detection, synthesis and compression in mammographic image analysis using a hierarchical image probability model," in IEEE MMBIA 2001. http://citeseer.ist.psu.edu/spence01detection.html More
@misc{ spence01detection,
author = "C. Spence and L. Parra and P. Sajda",
title = "Detection, synthesis and compression in mammographic image analysis using
a hierarchical image probability model",
text = "C.D. Spence, L. Parra, and P. Sajda, Detection, synthesis and compression
in mammographic image analysis using a hierarchical image probability model,
in IEEE MMBIA 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/spence01detection.html" }
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