Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Digital image processing
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Computer Vision
Multiresolution Analysis of Ridges and Valleys in Grey-Scale Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
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This paper presents a novel segmentation algorithm for metallographic images, especially those objects without regular boundaries and homogeneous intensities. In metallographic quantification, the complex microstructures make conventional approaches hard to achieve a satisfactory partition. We formulate the segmentation procedure as a new framework of iterative watershed region growing constrained by the ridge information. The seeds are selected by an effective double-threshold approach, and the ridges are superimposed as the highest waterlines in the watershed transform. To tackle the over-segmentation problem, the blobs are merged iteratively with the utilization of Bayes classification rule. Experimental results show that the algorithm is effective in performing segmentation without too much parameter tuning.