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Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004.
Traditional generative Markov random fields for segmenting images model the image data and corresponding labels jointly, which requires extensive independence assumptions for tractability. We present the conditional random field for an application in sign detection, using typical scale and orientation selective texture filters and a nonlinear texture operator based on the grating cell. The resulting model captures dependencies between neighboring image region labels in a data-dependent way thatdoi:10.1109/mlsp.2004.1423018 fatcat:gpzq7dwlevhwblabzhoagsbjp4