Intelligent splitting in the chromosome domain
Pattern Recognition
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clump splitting through concavity analysis
Pattern Recognition Letters
Watershed-based segmentation and region merging
Computer Vision and Image Understanding
A New Plant Cell Image Segmentation Algorithm
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
Shape identification and particles size distribution from basic shape parameters using ImageJ
Computers and Electronics in Agriculture
Measuring the sizes of concavities
Pattern Recognition Letters
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A novel algorithm based on watershed and concavities is proposed to segment the clustered slender-particles, such as the clustered rice kernels. First, the distance and watershed transform is used to the binary image of clustered slender-particles. Secondly, the watershed post-processing of over-segmentation is dealt with by utilizing concavity features of related shapes. Thirdly, the candidate splitting lines of touching clusters is found by matching the concavities to the un-segmentations left. Finally, the supplementary criterions are applied, such as the shortest distance, the opposite orientation, the splitting path orientation, etc., to determine whether a candidate splitting line can be accepted or not. Experimental results show that the algorithm can segment the large-scale clustered slender-particles efficiently, where such a quantitative analysis was previously infeasible.