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A. Montanvert, P. Meer, A. Rosenfeld, "Hierarchical Image Analysis Using Irregular Tessellations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 4, pp. 307316, April, 1991.  
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@article{ 10.1109/34.88566, author = {A. Montanvert and P. Meer and A. Rosenfeld}, title = {Hierarchical Image Analysis Using Irregular Tessellations}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {13}, number = {4}, issn = {01628828}, year = {1991}, pages = {307316}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.88566}, 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  Hierarchical Image Analysis Using Irregular Tessellations IS  4 SN  01628828 SP307 EP316 EPD  307316 A1  A. Montanvert, A1  P. Meer, A1  A. Rosenfeld, PY  1991 KW  hierarchical structure; picture processing; irregular tessellations; multiresolution image analysis; stochastic processes; segmentation; gray level images; picture processing; stochastic processes VL  13 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. Like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(imagesize) steps. However, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. Two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. In labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. In gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes.
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