Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model
IEEE Transactions on Image Processing
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
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This paper proposed a Gabor Filter and MRF Model for image segmentation. First, extract color features through transformation of color space; second, get image texture through Gabor filtering and Gaussian smoothing of the original color texture image; and then set up MRF image segmentation model, combining the color and texture features, to calculate the maximum posterior probability(MAP), and using ICM algorithm to optimize the computing complexity. We also proposed parameters estimation method using EM algorithm. Experiment shows that this mixture feature model is efficient than using only color or texture features.