Adaptive quantization and filtering using Gauss-Markov measure field models

Jose Luis Marroquin Zaleta; Salvador Botello; Mariano Rivera

doi:10.1117/12.323803

22 September 1998 Adaptive quantization and filtering using Gauss-Markov measure field models

Jose Luis Marroquin Zaleta, Salvador Botello, Mariano Rivera

Proceedings Volume 3459, Bayesian Inference for Inverse Problems; (1998) https://doi.org/10.1117/12.323803
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

We present a new class of models, derived form classical Markov Random Fields, that may be used for the solution of ill-posed problems in image processing and computational vision. They lead to reconstruction algorithms that are flexible, computationally efficient and biological plausible. To illustrate their use, we present their application to the reconstruction of the dominant orientation field and to the adaptive quantization and filtering of images in a variety of situations.

Citation Download Citation

Jose Luis Marroquin Zaleta, Salvador Botello, and Mariano Rivera "Adaptive quantization and filtering using Gauss-Markov measure field models", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); https://doi.org/10.1117/12.323803

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