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Alexandre X. Falc?, Jorge Stolfi, Roberto de Alencar Lotufo, "The Image Foresting Transform: Theory, Algorithms, and Applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 1929, January, 2004.  
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@article{ 10.1109/TPAMI.2004.10012, author = {Alexandre X. Falc? and Jorge Stolfi and Roberto de Alencar Lotufo}, title = {The Image Foresting Transform: Theory, Algorithms, and Applications}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {26}, number = {1}, issn = {01628828}, year = {2004}, pages = {1929}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.10012}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  The Image Foresting Transform: Theory, Algorithms, and Applications IS  1 SN  01628828 SP19 EP29 EPD  1929 A1  Alexandre X. Falc?, A1  Jorge Stolfi, A1  Roberto de Alencar Lotufo, PY  2004 KW  Dijkstra's algorithm KW  shortestpath problems KW  image segmentation KW  image analysis KW  regional minima KW  watershed transform KW  morphological reconstruction KW  boundary tracking KW  distance transforms KW  and multiscale skeletonization. VL  26 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—The image foresting transform (IFT) is a graphbased 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.
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