Segmentation of small objects in color images
Programming and Computing Software
Saliency model-based face segmentation and tracking in head-and-shoulder video sequences
Journal of Visual Communication and Image Representation
Heterogeneous stacking for classification-driven watershed segmentation
EURASIP Journal on Advances in Signal Processing
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification
Proceedings of the 30th DAGM symposium on Pattern Recognition
Graph-based tools for microscopic cellular image segmentation
Pattern Recognition
Ore image segmentation by learning image and shape features
Pattern Recognition Letters
Pattern Recognition Letters
Size distribution estimation of stone fragments via digital image processing
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
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This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. Typically, marker-driven watershed segmentation extracts seeds indicating the presence of objects or background at specific image locations. The marker locations are then set to be regional minima within the topological surface (typically, the gradient of the original input image), and the watershed algorithm is applied. In contrast, our approach uses two classifiers, one trained to produce markers, the other trained to produce object boundaries. As a result of using machine-learned pixel classification, the proposed algorithm is directly applicable to both single channel and multichannel image data. Additionally, rather than flooding the gradient image, we use the inverted probability map produced by the second aforementioned classifier as input to the watershed algorithm. Experimental results demonstrate the superior performance of the classification-driven watershed segmentation algorithm for the tasks of 1) image-based granulometry and 2) remote sensing