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A Parallel Search Strategy Based on Sparse Representation for Infrared Target Tracking
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Algorithms 2015, 8(3), 541-551; doi:10.3390/a8030541

Target Detection Algorithm Based on Two Layers Human Visual System

School of Electrical Engineering and Automation, Harbin Institute of Technology, No.2 Yi-Kuang Street Nan Gang District, Harbin 150001, China
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Academic Editor: Jun-Bao Li
Received: 27 April 2015 / Revised: 17 July 2015 / Accepted: 17 July 2015 / Published: 29 July 2015
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data)
View Full-Text   |   Download PDF [590 KB, uploaded 29 July 2015]   |  

Abstract

Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications. View Full-Text
Keywords: small target detection; human visual system; phase spectrum Fourier transform; support vector machine small target detection; human visual system; phase spectrum Fourier transform; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cui, Z.; Yang, J.; Jiang, S.; Wei, C. Target Detection Algorithm Based on Two Layers Human Visual System. Algorithms 2015, 8, 541-551.

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