This paper presents a comparative study of several well-known and thoroughly tested techniques for the segmentation of textured images, including two algorithms belonging to the adaptive Bayesian family of restoration and segmentation methods, a probabilistic relaxation process, and a novel approach based on the recently introduced concept of the frequency histogram of connected elements. The application domain chosen for comparison purposes is the problem of detecting very thin cracks-around 1 mm width- in the wooden boards of used pallets, where a tricky balance between the crack detection and false alarm ratios must be guaranteed. After a brief description of each segmentation method and their respective application to the problem at hand, the paper discusses the comparative results, showing the excellent performance achieved with the frequency histogram of connected elements, which can be considered an attractive and versatile novel instrument for the analysis and recognition of textured images.
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