K-max: segmentation based on selection of max-tree deep nodes

Alexandre G. Silva; Siovani C. Felipussi; Roberto de Alencar Lotufo; Gustavo L. F. Cassol

doi:10.1117/12.643462

16 February 2006 K-max: segmentation based on selection of Max-tree deep nodes

Alexandre G. Silva, Siovani C. Felipussi, Roberto de Alencar Lotufo, Gustavo L. F. Cassol

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 60640M (2006) https://doi.org/10.1117/12.643462
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

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 k_up 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.

Citation Download Citation

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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KEYWORDS

Image segmentation

Expectation maximization algorithms

Image processing

Image processing algorithms and systems

Image retrieval

Chemical elements

Image filtering

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