efficient edge detection and object segmentation using gabor filters
Gabor filter is a widely used feature extraction method, especially in image texture analysis. The selection of optimal filter parameters is usually problematic and unclear. This study analyzes the filter design essentials and proposes two different methods to segment the Gabor filtered multi-channel images. The first method integrates Gabor filters with labeling algorithm for edge detection and object segmentation. The second method uses the K-means clustering with simulated annealing for image segmentation of a stack of Gabor filtered multi-channel images. Various experiments with real images demonstrate the effectiveness of these approaches.
segmentation gabor filter labeling k-means simulated annealing