2026-09-18T21:25:13Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/770012022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomaso Poggio.
Braun, Stav
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-02-14T15:36:58Z
2013-02-14T15:36:58Z
2012
2012
http://hdl.handle.net/1721.1/77001
825558388
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 59-62).
Monitoring mouse social behaviors over long periods of time is essential for neurobehavioral analysis of social mouse phenotypes. Currently, the primary method of social behavioral plienotyping utilizes human labelers, which is slow and costly. In order to achieve the high throughput desired for scientific studies, social behavioral phenotyping must be automated. The problem of automation can be divided into two tasks; tracking and phenotyping. First, individual body parts of mice must be accurately tracked. This is achieved using shape context descriptors to obtain precise point to point correspondences between templates and mice in any frame of a video. This method provides for greater precision and accuracy than current state of the art techniques. We propose a means by which this tracking information can be used to classify social behaviors between mice.
by Stav Braun.
M.Eng.
62 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Tracking multiple mice
Thesis
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Publication
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<Abstract>Monitoring mouse social behaviors over long periods of time is essential for neurobehavioral analysis of social mouse phenotypes. Currently, the primary method of social behavioral plienotyping utilizes human labelers, which is slow and costly. In order to achieve the high throughput desired for scientific studies, social behavioral phenotyping must be automated. The problem of automation can be divided into two tasks; tracking and phenotyping. First, individual body parts of mice must be accurately tracked. This is achieved using shape context descriptors to obtain precise point to point correspondences between templates and mice in any frame of a video. This method provides for greater precision and accuracy than current state of the art techniques. We propose a means by which this tracking information can be used to classify social behaviors between mice.</Abstract>
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