DocumentCode
3547392
Title
A dorsal pathway guided visual attention model
Author
Lingxiang Zheng ; Xianchao Zheng ; Zhanjian Lin ; Weiwei Tang ; Changle Zhou
Author_Institution
Sch. of Inf. Sci. & Eng., Xiamen Univ., Xiamen, China
fYear
2013
fDate
2-4 Nov. 2013
Firstpage
638
Lastpage
644
Abstract
Attention computational model is widely used in the embedded intelligent vision system to help it offload the processing effort. In this paper, we proposed a visual attention computational model based on the biological mechanism that the dorsal pathway will guide the ventral visual information process. The model involves two feature processing subsystems, one is the dorsal pathway feature processing subsystem and the other is the ventral pathway feature processing subsystem. Moreover, the dorsal pathway feature processing subsystem will generate a signal based on its processing result to modulate the information processing of the ventral pathway feature processing subsystem. The experiment results show that the proposed model outperforms the comparison models in four different test scenarios, which indicates that the proposed model may be more biologically plausible and can help the embedded intelligent vision system to find out the interested objects more accurately.
Keywords
computer vision; knowledge based systems; biological mechanism; dorsal pathway feature processing subsystem; dorsal pathway guided visual attention model; embedded intelligent vision system; information processing modulation; ventral pathway feature processing subsystem; ventral visual information process; visual attention computational model; Artificial intelligence; Biological system modeling; Computational modeling; Image color analysis; Modulation; Visualization; dorsal pathway; saliency map; ventral pathway; visual attention;
fLanguage
English
Publisher
ieee
Conference_Titel
Awareness Science and Technology and Ubi-Media Computing (iCAST-UMEDIA), 2013 International Joint Conference on
Conference_Location
Aizu-Wakamatsu
Type
conf
DOI
10.1109/ICAwST.2013.6765517
Filename
6765517
Link To Document