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A. Califano, R.M. Bolle, "The Multiple Window Parameter Transform," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 12, pp. 11571170, December, 1992.  
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@article{ 10.1109/34.177381, author = {A. Califano and R.M. Bolle}, title = {The Multiple Window Parameter Transform}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {12}, issn = {01628828}, year = {1992}, pages = {11571170}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.177381}, 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 Multiple Window Parameter Transform IS  12 SN  01628828 SP1157 EP1170 EPD  11571170 A1  A. Califano, A1  R.M. Bolle, PY  1992 KW  computational requirements; feature dimensionality; feature extraction; parameter spaces; noisy feature hypothesis suppression; image segmentation; multiple window parameter transform; highdimensional features; space requirements; image size; correlated information; evidence integration techniques; nonexistent feature hypotheses; constraint satisfaction networks; iterative relaxation phase; disjoint support; computational complexity; feature extraction; image processing; image segmentation; transforms VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
The multiwindow transform, an extension of parameter transform techniques that increase performance and scope by exploiting the longrange correlated information contained in multiple portions of an image, is presented. Multiplewindow transforms allow the extraction of highdimensional features with improvement in accuracy over conventional techniques while keeping linear to loworderpolynomial computational and space requirements with respect to image size and dimensionality of the features. Using correlated information provides a direct link between extracted features and supporting regions in the image. This, coupled with evidence integration techniques, is used to suppress noisy or nonexistent feature hypotheses. Parameter spaces are implemented as constraint satisfaction networks, where feature hypotheses with overlapping support in the image compete. After an iterative relaxation phase, surviving hypotheses have disjoint support, forming a segmentation of the image. Examples show the performance and provide insight about the behavior.
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