TY - GEN
T1 - Learning multiscale image models of 2D object classes
AU - Perrin, Benoit
AU - Ahuja, Narendra
AU - Srinivasa, Narayan
N1 - Publisher Copyright:
© 1997, Springer Verlag. All rights reserved.
PY - 1997
Y1 - 1997
N2 - This paper is concerned with learning the canonical gray scale structure of the images of a class of objects. Structure is defined in terms of the geometry and layout of salient image regions that characterize the given views of the objects. The use of such structure based learning of object appearence is motivated by the relative stability of image structure over intensity values. A multiscale segmentation tree description is antomatically extracted for all sample images which are then matched to construct a single canonical representative which serves as the model 0fthe class. Different images are selected as prototypes, and each prototype tree is refined to best match the rest of the class. The model tree for the class is that tree which is best supported over all the initializations with different prototypes. Matching is formulated as a problem of finding the best mapping from regions of example images to those of the model tree, and implemented as a problem in incremental refinement of the model tree using a learning approach. Experiments are reported on a face image database. The results demonstrate that a reasonable model of facial geometry and topology is learnt which includes prominent facial features.
AB - This paper is concerned with learning the canonical gray scale structure of the images of a class of objects. Structure is defined in terms of the geometry and layout of salient image regions that characterize the given views of the objects. The use of such structure based learning of object appearence is motivated by the relative stability of image structure over intensity values. A multiscale segmentation tree description is antomatically extracted for all sample images which are then matched to construct a single canonical representative which serves as the model 0fthe class. Different images are selected as prototypes, and each prototype tree is refined to best match the rest of the class. The model tree for the class is that tree which is best supported over all the initializations with different prototypes. Matching is formulated as a problem of finding the best mapping from regions of example images to those of the model tree, and implemented as a problem in incremental refinement of the model tree using a learning approach. Experiments are reported on a face image database. The results demonstrate that a reasonable model of facial geometry and topology is learnt which includes prominent facial features.
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U2 - 10.1007/3-540-63931-4_233
DO - 10.1007/3-540-63931-4_233
M3 - Conference contribution
AN - SCOPUS:84947557133
SN - 3540639314
SN - 9783540639312
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 323
EP - 331
BT - Computer Vision - ACCV 1998 - 3rd Asian Conference on Computer Vision, Proceedings
A2 - Chin, Roland
A2 - Pong, Ting-Chuen
PB - Springer
T2 - 3rd Asian Conference on Computer Vision, ACCV 1998
Y2 - 8 January 1998 through 10 January 1998
ER -