Clump splitting through concavity analysis
Pattern Recognition Letters
Digital Image Processing
Shape-Guided Split and Merge of Image Regions
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
A New Plant Cell Image Segmentation Algorithm
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
A rule-based approach for robust clump splitting
Pattern Recognition
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Cell Cluster Image Segmentation on Form Analysis
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Ore image segmentation by learning image and shape features
Pattern Recognition Letters
Splitting touching cells based on concave points and ellipse fitting
Pattern Recognition
A Delaunay triangulation approach for segmenting clumps of nuclei
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Segmentation of clustered nuclei based on curvature weighting
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
| Hi-index | 0.01 |
Under-segmentation of an image with multiple objects is a common problem in image segmentation algorithms. This paper presents a novel approach for splitting clumps formed by multiple objects due to under-segmentation. The proposed algorithm includes three steps: (1) decide whether to split a candidate connected component by application-specific shape classification; (2) find a pair of points for clump splitting and (3) join the pair of selected points. In the first step, a shape classifier is applied to determine whether a connected component should be split. In the second step, a pair of points for splitting is detected using a bottleneck rule, under the assumption that the desired objects have roughly a convex shape. In the third step, the selected splitting points from step two are joined by finding the optimal splitting line between them, based on minimizing an image energy. The shape classifier is built offline via various shape features and a support vector machine. Steps two and three are application-independent. The performance of this method is evaluated using images from various applications. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms for the clump splitting problem.