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http://dx.doi.org/10.9717/kmms.2011.14.3.378

Visual Information Selection Mechanism Based on Human Visual Attention  

Cheoi, Kyung-Joo (충북대학교 전자정보대학 소프트웨어학과)
Park, Min-Chul (한국과학기술연구원 포토닉스센서 시스템센터)
Publication Information
Journal of Korea Multimedia Society / v.14, no.3, 2011 , pp. 378-391 More about this Journal
Abstract
In this paper, we suggest a novel method of selecting visual information based on bottom-up visual attention of human. We propose a new model that improve accuracy of detecting attention region by using depth information in addition to low-level spatial features such as color, lightness, orientation, form and temporal feature such as motion. Motion is important cue when we derive temporal saliency. But noise obtained during the input and computation process deteriorates accuracy of temporal saliency Our system exploited the result of psychological studies in order to remove the noise from motion information. Although typical systems get problems in determining the saliency if several salient regions are partially occluded and/or have almost equal saliency, our system is able to separate the regions with high accuracy. Spatiotemporally separated prominent regions in the first stage are prioritized using depth value one by one in the second stage. Experiment result shows that our system can describe the salient regions with higher accuracy than the previous approaches do.
Keywords
Visual Information Selection; Attention Region; Saliency; Depth; Motion;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 A. Hiroshi, "Visual Psychophysics (8) : Visual Motion Perception and Motion Pictures," Journal of Image Information and Television Engineers, Vol.58, No.8, pp.1151-1156, 2004.   DOI
2 E. Niebur and C. Koch. "Computational architectures for attention," The Attentive Brain, MIT Press, 1997.
3 S. Kwak, B. Ko and H. Byun "Automatic salient-object extraction using the contrast map and salient point," LNCS, Vol.3332, pp.138-145, 2004
4 S. Kwak , B. Ko and H. Byun, "Salient human detection for robot vision," Pattern Analysis and Applications, Vol.10, No.4, pp.291-299, 2007.   DOI   ScienceOn
5 K. Rapantzikos, Y. Avrithis, and S. Kollias, "Handling Uncertainty in Video Analysis with Spatiotemporal Visual Attention," The 14th IEEE International Conference on Fuzzy Systems, pp.213-217, 2005.
6 L. Itti and C. Koch, "A saliency-based search mechanism for overt and covert shifts of visual attention," Vision Research, Vol.40, pp.1489-1506, 2000.   DOI   ScienceOn
7 F. Tombari, S. Mattoccia and L. Stefano, "Segmentation-based adaptive support for accurate stereo correspondence," In Proc. IEEE Pacific-Rim Symposium on Image and Video Technology, 2007.
8 S. Li and M. Lee, "An Efficient Spatiotemporal Attention Model and Its Application to Shot Matching," IEEE Transactions on Circuits and Systems for Video Technology, Vol.17, No.10, pp.1383-1387, 2007.   DOI
9 M. Park and K. Cheoi, "An Adaptive ROI Detection System for Spatiotemporal Features," J. of the Korea Contents Association, Vol.6, No.1, pp.41-53, 2006.
10 M. Park and K. Cheoi, "Selective Visual Attention System Based on Spatiotemporal Features," LNCS, Vol.5068, pp.203-212, 2008.
11 K. Yoon and I. Kweon, "Adaptive Support- Weight Approach for Correspondence Search," IEEE Tranansactions on PAMI, Vol.28, No.4, pp.650-656, 2006.   DOI
12 L. Spillmann and J. Werner, "Visual Perception: the neurophysiological foundations," Academinc Press Inc., 1990.
13 A.Treisman and G.Gelad, "A Feature-integration theory of Attention," Congnitive Psychology, Vol.12, No.1, pp.97-136, 1980.   DOI   ScienceOn
14 차명희, 김기협, 조경은, 엄기현, "시각적 특징 맵을 이용한 자율 가상 캐릭터의 실시간 주목 시스템," 한국멀티미디어학회논문지, v.12, no.5, pp.745-756, 2009.