Kernel Designs for Efficient Multiresolution Edge Detection and Orientation Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A new approach to image segmentation is presented that integrates region and boundary information within a great framework of Maximum a Posteriori (MAP) estimation and decision theory. The algorithm employs iterative, decision-directed estimation performed on a spatially localised basis but within a multiresolution representation. The use of a multiresolution technique ensures both robustness in noise and efficiency of computation, while the model-based estimation and decision process is both flexible and spatially local, thus avoiding assumptions about global homogeneity or size and number of regions. The method gives accurate segmentations at low signal-to-noise ratios and is shown to be more effective than previous methods in capturing complex region shapes.