TY - GEN
T1 - Visual deconstruction
T2 - International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997
AU - Liu, Tyng Luh
AU - Geiger, Davi
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1997.
PY - 1997
Y1 - 1997
N2 - We propose a deconstruction framework to recognize and find articulated objects. In particular we are interested in human arm and leg articulations. The deconstruction view of recognition naturally decomposes the problem of finding an object in an image, into the one of (i) extracting key features in an image, (ii) detecting key points in the models, (iii) segmenting an image, and (iv) comparing shapes. All of these subproblems can not be resolved independently. Together, they reconstruct the object in the image. We briefly address (i) and (ii) to focus on solving together shape similarity and segmentation, combining top-down & bottom-up algorithms. We show that the visual deconstruction approach is derived as an optimization for a Bayesian-Information theory, and that the whole process is naturally generated by the guaranteed Dijkstra optimization algorithm.
AB - We propose a deconstruction framework to recognize and find articulated objects. In particular we are interested in human arm and leg articulations. The deconstruction view of recognition naturally decomposes the problem of finding an object in an image, into the one of (i) extracting key features in an image, (ii) detecting key points in the models, (iii) segmenting an image, and (iv) comparing shapes. All of these subproblems can not be resolved independently. Together, they reconstruct the object in the image. We briefly address (i) and (ii) to focus on solving together shape similarity and segmentation, combining top-down & bottom-up algorithms. We show that the visual deconstruction approach is derived as an optimization for a Bayesian-Information theory, and that the whole process is naturally generated by the guaranteed Dijkstra optimization algorithm.
UR - http://www.scopus.com/inward/record.url?scp=84958624964&partnerID=8YFLogxK
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U2 - 10.1007/3-540-62909-2_87
DO - 10.1007/3-540-62909-2_87
M3 - Conference contribution
AN - SCOPUS:84958624964
SN - 3540629092
SN - 9783540629092
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 295
EP - 309
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings
A2 - Hancock, Edwin R.
A2 - Pelillo, Marcello
PB - Springer Verlag
Y2 - 21 May 1997 through 23 May 1997
ER -