Files and links (1)
SubmittedOpen Access (License Unspecified), Open
Conference proceeding
Video Analysis Algorithms for Automated Categorization of Fly Behaviors
ADVANCES IN VISUAL COMPUTING, ISVC 2012, PT II, v 7432(2), pp 229-241
01 Jan 2012
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The fruit fly, Drosophila melanogaster, is a well established model organism used to study the mechanisms of both learning and memory in vivo. This paper presents video analysis algorithms that generate data that may be used to categorize fly behaviors. The algorithms aim to replace and improve a labor-intensive, subjective evaluation process with one that is automated, consistent and reproducible; thus allowing for robust, high-throughput analysis of large quantities of video data. The method includes tracking the flies, computing geometric measures, constructing feature vectors, and grouping the specimens using clustering techniques. We also generated a Computed Courtship Index (CCI), a computational equivalent of the existing Courtship Index (CI). The results demonstrate that our automated analysis provides a numerical scoring of fly behavior that is similar to the scoring produced by human observers. They also show that we are able to automatically differentiate between normal and defective flies via analysis of their videotaped movements.
Metrics
9 Record Views
1 citations in Scopus
Details
- Title
- Video Analysis Algorithms for Automated Categorization of Fly Behaviors
- Creators
- Md Alimoor Reza - Drexel UniversityJeffrey Marker - Drexel UniversitySiddhita Mhatre - Drexel UniversityAleister Saunders - Drexel UniversityDaniel Marenda - Drexel UniversityDavid Breen - Drexel University
- Publication Details
- ADVANCES IN VISUAL COMPUTING, ISVC 2012, PT II, v 7432(2), pp 229-241
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 13
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Biology; Computer Science
- Web of Science ID
- WOS:000363265800023
- Scopus ID
- 2-s2.0-84866673242
- Other Identifier
- 991019170404204721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods
- Mathematical & Computational Biology