2026-09-23T02:38:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/85762022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
W. Eric L. Grimson.
Felzenszwalb, Pedro F., 1976-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-08-23T21:24:35Z
2005-08-23T21:24:35Z
2001
2001
http://hdl.handle.net/1721.1/8576
49223516
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 51-53).
This thesis presents a statistical framework for object recognition. The framework is motivated by the pictorial structure models introduced by Fischler and Elschlager nearly 30 years ago. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. The problem of detecting an object in an image and the problem of learning an object model using training examples are naturally formulated under a statistical approach. We present efficient algorithms to solve these problems in our framework. We demonstrate our techniques by training models to represent faces and human bodies. The models are then used to locate the corresponding objects in novel images.
by Pedro F. Felzenszwalb.
S.M.
53 p.
8399082 bytes
8398838 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Object recognition with pictorial structures
Thesis
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