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16 February 2006 K-max: segmentation based on selection of Max-tree deep nodes
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Abstract
This work proposes the segmentation of grayscale image from of its hierarchical region based representation. The Maxtree structure has demonstrated to be useful for this purpose, offering a semantic vision of the image, therefore, reducing the number of elements to process in relation to the pixel based representation. In this way, a particular searching in this tree can be used to determine regions of interest with lesser computational effort. A generic application of detection of peaks is proposed through searching nodes to kup steps from leaves in the Max-tree (this operator will be called k-max), being each node corresponds to a connected component. The results are compared with the optimal thresholding and the H-maxima technique.
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Alexandre G. Silva, Siovani C. Felipussi, Roberto de Alencar Lotufo, and Gustavo L. F. Cassol "K-max: segmentation based on selection of Max-tree deep nodes", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60640M (16 February 2006); https://doi.org/10.1117/12.643462
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