DocumentCode :
691113
Title :
The Multi-orientation Target Recognition Method Based on Visual Attention
Author :
Du Yaling ; Lin Beiqing ; Lu Jing
Author_Institution :
Nat. Key Lab. of Sci. & Technol. on Aerosp. Intell. Control, Beijing Aerosp. Autom. Control Inst., Beijing, China
fYear :
2013
fDate :
21-23 Sept. 2013
Firstpage :
776
Lastpage :
780
Abstract :
Synthetically utilizing image visual attention and Support Vector Machine (SVM) classification method, a multi-orientation target recognition algorithm was proposed to detect multi-orientation targets in images. Firstly, according to human visual system, the saliency image was get rapidly using visual attention to improve the efficiency. Secondly, the Histogram of Oriented Gradients (HOG) features described the shape features of target. Then, the angular field of view to targets was divided into several parts for solving the samples variety according to the pose angle. In every divided field SVM classifier was used to recognize the multi-orientation targets. Experimental results show that the multi-view target recognition method proposed by this paper is effective and reliable.
Keywords :
feature extraction; image classification; object detection; object recognition; support vector machines; HOG; SVM classifier; histogram-of-oriented gradients features; human visual system; multiorientation target detection; multiorientation target recognition method; shape features; support vector machine classification method; synthetic image visual attention utilization; Aerospace control; Algorithm design and analysis; Computational modeling; Histograms; Support vector machines; Target recognition; Visualization; Histograms of oriented gradient; Multi-orientation Target recognition; Support Vector Machine; Visual Attention;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Instrumentation, Measurement, Computer, Communication and Control (IMCCC), 2013 Third International Conference on
Conference_Location :
Shenyang
Type :
conf
DOI :
10.1109/IMCCC.2013.173
Filename :
6840563
Link To Document :
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