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Multimedia signal processing for behavioral quantification in neuroscience
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Proceedings of the 14th annual ACM international conference on Multimedia table of contents
Santa Barbara, CA, USA
SESSION: Brave new topics session 2 - multimedia signal processing and systems in healthcare and life science table of contents
Pages: 1007 - 1016  
Year of Publication: 2006
ISBN:1-59593-447-2
Authors
Peter Andrews  Cold Spring Harbor Laboratory
Haibin Wang  Cold Spring Harbor Laboratory
Dan Valente  Cold Spring Harbor Laboratory
Jihène Serkhane  Cold Spring Harbor Laboratory
Partha P. Mitra  Cold Spring Harbor Laboratory
Sigal Saar  City College of New York
Ofer Tchernichovski  City College of New York
Ilan Golani  Tel Aviv University
Sponsors
ACM: Association for Computing Machinery
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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ABSTRACT

While there have been great advances in quantification of the genotype of organisms, including full genomes for many species, the quantification of phenotype is at a comparatively primitive stage. Part of the reason is technical difficulty: the phenotype covers a wide range of characteristics, ranging from static morphological features, to dynamic behavior. The latter poses challenges that are in the area of multimedia signal processing. Automated analysis of video and audio recordings of animal and human behavior is a growing area of research, ranging from the behavioral phenotyping of genetically modified mice or drosophila to the study of song learning in birds and speech acquisition in human infants. This paper reviews recent advances and identifies key problems for a range of behavior experiments that use audio and video recording. This research area offers both research challenges and an application domain for advanced multimedia signal processing. There are a number of MMSP tools that now exist which are directly relevant for behavioral quantification, such as speech recognition, video analysis and more recently, wired and wireless sensor networks for surveillance. The research challenge is to adapt these tools and to develop new ones required for studying human and animal behavior in a high throughput manner while minimizing human intervention. In contrast with consumer applications, in the research arena there is less of a penalty for computational complexity, so that algorithmic quality can be maximized through the utilization of larger computational resources that are available to the biomedical researcher.


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Collaborative Colleagues:
Peter Andrews: colleagues
Haibin Wang: colleagues
Dan Valente: colleagues
Jihène Serkhane: colleagues
Partha P. Mitra: colleagues
Sigal Saar: colleagues
Ofer Tchernichovski: colleagues
Ilan Golani: colleagues