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| Jose L. Marroquin, Fernando A. Velasco, Mariano Rivera, Miguel Nakamura, "Gauss-Markov Measure Field Models for Low-Level Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 337-348, April, 2001. | |||
| BibTex | x | ||
| @article{ 10.1109/34.917570, author = {Jose L. Marroquin and Fernando A. Velasco and Mariano Rivera and Miguel Nakamura}, title = {Gauss-Markov Measure Field Models for Low-Level Vision}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {23}, number = {4}, issn = {0162-8828}, year = {2001}, pages = {337-348}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.917570}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Gauss-Markov Measure Field Models for Low-Level Vision IS - 4 SN - 0162-8828 SP337 EP348 EPD - 337-348 A1 - Jose L. Marroquin, A1 - Fernando A. Velasco, A1 - Mariano Rivera, A1 - Miguel Nakamura, PY - 2001 KW - Bayes methods KW - estimation theory KW - Gaussian distributions KW - image classification KW - image segmentation KW - Markov processes KW - probability KW - simulated annealing. VL - 23 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—We present a class of models, derived from classical discrete Markov random fields, that may be used for the solution of ill-posed problems in image processing and in computational vision. They lead to reconstrucion algorithms that are flexible, computationally efficient, and biologically plausible. To illustrate their use, we present their application to the reconstruction of the dominant orientation and direction fields, to the classification of multiband images, and to image quantization and filtering.
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