أعرض تسجيلة المادة بشكل مبسط

dc.creator Jones, Michael J.
dc.creator Poggio, Tomaso
dc.date 2004-10-20T20:49:06Z
dc.date 2004-10-20T20:49:06Z
dc.date 1996-12-01
dc.date.accessioned 2013-10-09T02:48:29Z
dc.date.available 2013-10-09T02:48:29Z
dc.date.issued 2013-10-09
dc.identifier AIM-1583
dc.identifier CBCL-139
dc.identifier http://hdl.handle.net/1721.1/7183
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.
dc.format 33 p.
dc.format 13000011 bytes
dc.format 1999501 bytes
dc.format application/postscript
dc.format application/pdf
dc.language en_US
dc.relation AIM-1583
dc.relation CBCL-139
dc.subject AI
dc.subject MIT
dc.subject Artificial Intelligence
dc.subject Computer Vision
dc.subject Image Correspondence
dc.subject Deformable Templates
dc.subject Object Recognition
dc.title Model-Based Matching by Linear Combinations of Prototypes


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أعرض تسجيلة المادة بشكل مبسط