Enhancing target detection using a hybrid human-computer system

Kenneth E. Hild; Misha Pavel; Deniz Erdogmus; Santosh Mathan

doi:10.1109/ACSSC.2009.5470175

Enhancing target detection using a hybrid human-computer system

Kenneth E. Hild, Misha Pavel, Deniz Erdogmus, Santosh Mathan

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Our goal is to develop an assistive technology for image analysts, which helps them to extract information (e.g., detect targets) efficiently. The images are displayed using the rapid serial visual presentation (RSVP) paradigm, in which a large image is broken into numerous smaller chips which are then displayed serially in the center of the monitor at a high rate. Detection performance is affected by several factors, one of which is the target eccentricity. We show how computer vision (CV) can be used to reduce the mean eccentricity in a manner that is expected to improve detection performance, while maintaining overall throughput.

Original language	English (US)
Title of host publication	Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Pages	51-54
Number of pages	4
DOIs	https://doi.org/10.1109/ACSSC.2009.5470175
State	Published - 2009
Event	43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Nov 1 2009 → Nov 4 2009

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	43rd Asilomar Conference on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove, CA
Period	11/1/09 → 11/4/09

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2009.5470175

Cite this

Enhancing target detection using a hybrid human-computer system. / Hild, Kenneth E.; Pavel, Misha; Erdogmus, Deniz et al.
Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers. 2009. p. 51-54 5470175 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hild, KE, Pavel, M, Erdogmus, D & Mathan, S 2009, Enhancing target detection using a hybrid human-computer system. in Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers., 5470175, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 51-54, 43rd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States, 11/1/09. https://doi.org/10.1109/ACSSC.2009.5470175

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abstract = "Our goal is to develop an assistive technology for image analysts, which helps them to extract information (e.g., detect targets) efficiently. The images are displayed using the rapid serial visual presentation (RSVP) paradigm, in which a large image is broken into numerous smaller chips which are then displayed serially in the center of the monitor at a high rate. Detection performance is affected by several factors, one of which is the target eccentricity. We show how computer vision (CV) can be used to reduce the mean eccentricity in a manner that is expected to improve detection performance, while maintaining overall throughput.",

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AB - Our goal is to develop an assistive technology for image analysts, which helps them to extract information (e.g., detect targets) efficiently. The images are displayed using the rapid serial visual presentation (RSVP) paradigm, in which a large image is broken into numerous smaller chips which are then displayed serially in the center of the monitor at a high rate. Detection performance is affected by several factors, one of which is the target eccentricity. We show how computer vision (CV) can be used to reduce the mean eccentricity in a manner that is expected to improve detection performance, while maintaining overall throughput.

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Enhancing target detection using a hybrid human-computer system

Abstract

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ASJC Scopus subject areas

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