Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 5,
  • Issue 7,
  • pp. 400-402
  • (2007)

Multisensor long distance target detection using support vector machine

Not Accessible

Your library or personal account may give you access

Abstract

Multisensor image fusion could improve system performances such as detection, tracking, and identification greatly. In this paper, a long distance target detection approach is presented based on multisensor image features fusion. This method extracts two different features from visual and infrared (IR) image sequences respectively to detect regions of motion information content. Temporal change feature is extracted from the visual image sequence using temporal decomposition based on wavelet, which reflects the dynamical content variation at a pixel at any time. And correlation features between local regions are extracted from IR image sequence to distinguish regions with potential moving targets. All these features are merged into a multi-dimensional space and the support vector machine is trained to select regions that have the potential target at each pixel location. The method is robust and feasible to detect long distance targets in clutter background scene.

© 2007 Chinese Optics Letters

PDF Article
More Like This
Target detection and recognition improvements by use of spatiotemporal fusion

Hai-Wen Chen, Surachai Sutha, and Teresa Olson
Appl. Opt. 43(2) 403-415 (2004)

Directional support value of Gaussian transformation for infrared small target detection

Changcai Yang, Jiayi Ma, Shengxiang Qi, Jinwen Tian, Sheng Zheng, and Xin Tian
Appl. Opt. 54(9) 2255-2265 (2015)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved