MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • Artificial Intelligence Lab Publications
  • AI Memos (1959 - 2004)
  • View Item
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • Artificial Intelligence Lab Publications
  • AI Memos (1959 - 2004)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Biologically Plausible Neural Model for the Recognition of Biological Motion and Actions

Author(s)
Giese, Martin Alexander; Poggio, Tomaso
Thumbnail
DownloadAIM-2002-012.ps (3.397Mb)
Additional downloads
AIM-2002-012.pdf (2.423Mb)
Metadata
Show full item record
Abstract
The visual recognition of complex movements and actions is crucial for communication and survival in many species. Remarkable sensitivity and robustness of biological motion perception have been demonstrated in psychophysical experiments. In recent years, neurons and cortical areas involved in action recognition have been identified in neurophysiological and imaging studies. However, the detailed neural mechanisms that underlie the recognition of such complex movement patterns remain largely unknown. This paper reviews the experimental results and summarizes them in terms of a biologically plausible neural model. The model is based on the key assumption that action recognition is based on learned prototypical patterns and exploits information from the ventral and the dorsal pathway. The model makes specific predictions that motivate new experiments.
Date issued
2002-08-01
URI
http://hdl.handle.net/1721.1/7272
Other identifiers
AIM-2002-012
CBCL-219
Series/Report no.
AIM-2002-012CBCL-219
Keywords
AI, biological motion, action recognition, visual pathways, hierarchical processing

Collections
  • AI Memos (1959 - 2004)
  • CBCL Memos (1993 - 2004)

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.