The image foresting transform: theory, algorithms, and applications Export

@article{citeulike:2841385, abstract = {The image foresting transform (IFT) is a graph-based approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definition of the IFT, and a procedure to compute it-a generalization of Dijkstra's algorithm-with a proof of correctness. We also discuss implementation issues and illustrate the use of the IFT in a few applications.}, author = {Falcao, A. X. and Stolfi, J. and de Alencar}, booktitle = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, citeulike-article-id = {2841385}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TPAMI.2004.1261076}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1261076}, doi = {10.1109/TPAMI.2004.1261076}, journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, keywords = {distance, foresting, growing, image, path, region, segmentation, shortest, transform}, number = {1}, pages = {19--29}, posted-at = {2008-05-28 12:43:44}, priority = {2}, title = {The image foresting transform: theory, algorithms, and applications}, url = {http://dx.doi.org/10.1109/TPAMI.2004.1261076}, volume = {26}, year = {2004} }

TY - JOUR ID - citeulike:2841385 L3 - citeulike-article-id:2841385 N2 - The image foresting transform (IFT) is a graph-based approach to the design of image processing operators based on connectivity. It naturally leads to correct and efficient implementations and to a better understanding of how different operators relate to each other. We give here a precise definition of the IFT, and a procedure to compute it-a generalization of Dijkstra's algorithm-with a proof of correctness. We also discuss implementation issues and illustrate the use of the IFT in a few applications. IS - 1 JF - Pattern Analysis and Machine Intelligence, IEEE Transactions on EP - 29 TI - The image foresting transform: theory, algorithms, and applications VL - 26 T2 - Pattern Analysis and Machine Intelligence, IEEE Transactions on SP - 19 KW - distance KW - foresting KW - growing KW - image KW - path KW - region KW - segmentation KW - shortest KW - transform AU - Falcao, AX AU - Stolfi, J AU - de Alencar PY - 2004/// UR - http://dx.doi.org/10.1109/TPAMI.2004.1261076 ER -