Detecting perceptually important regions in an image based on human visual attention characteristic

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2396, Issue , 2002, Pages 329-338

  Cheoi, Kyungjoo ^a   Lee, Yillbyung ^a  

^a Yonsei University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; IMAGE SEGMENTATION; ITERATIVE METHODS; PIXELS; SYNTACTICS;

HUMAN PERCEPTION; HUMAN VISUAL ATTENTION; IMPORTANCE MAP; LATERAL INHIBITION; LOCAL REGION; SALIENCY MAP; STATISTICAL INFORMATION; THREE COMPONENT;

PATTERN RECOGNITION;

EID: 34249304968     PISSN: 03029743     EISSN: 16113349     Source Type: Journal    
DOI: 10.1007/3-540-70659-3_34     Document Type: Article

References (14)

1. Aguilar, M., Ross, W.:Incremental art:A neural network system for recognition by incremental feature extraction. Proc. of WCNN-93 (1993)
2. Cave, K., Wolfe, J.: Modeling the Role of Parallel Processing in Visual Search. Cognitive Psychology 22 (1990) 225-271
3. Chapman, D.: Vision, Instruction, and Action. Ph.D. Thesis, AI Laboratory, Massachusetts Institute of Technology (1990)
4. Colby:The neuroanatomy and neurophysiology of attention. Journal of Child Neurology 6 (1991) 90-118
5. Exel, S., Pessoa, L.:Attentive visual recognition. Proc. of Intl. Conf. on Pattern Recognition 1 (1998) 690-692
6. Itti, L., Koch, C., Niebur, E.: Model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (1998) 1254-1259
7. Koch, C., Ullman, S.: Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry. Human Neurobiology 4 (1985) 219-227
8. Laar, P., Heskes, T., Gielen, S.:Task-Dependent Learning of Attention. Neural Networks 10, 6 (1997) 981-992
9. Milanese, R., Wechsler, H., Gil, S., Bost, J., Pun, T.: Integration of Bottom-up and Top-down Cues for Visual Attention Using Non-Linear Relaxation. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (1994) 781-785
10. Olivier, S., Yasuo, K., Gordon, C.:Development of a Biologically Inspired Real-Time Visual Attention System. In:Lee, S.-W., Buelthoff, H.-H., Poggio, T.(eds.):BMCV 2000.Lecture Notes in Computer Science, Vol. 1811. Springer-Verlag, Berlin Heidelberg New York (2000) 150-159
11. Olshausen, B., Essen, D., Anderson, C.: A neurobiological model of visual attention and Invariant pattern recognition based on dynamic routing of information. NeuroScience 13 (1993) 4700-4719
12. Stewart, B., Reading, I., Thomson, M., Wan, C., Binnie, T.: Directing attention for traffic scene analysis. Proc. of Intl. Conf. on Image Processing and Its Applications (1995) 801-805
13. Treisman, A.-M., Gelade, G.-A.: A Feature-integration Theory of Attention. Cognitive Psychology 12 (1980) 97-136
14. Yagi, T., Asano, N., Makita, S., Uchikawa, Y.:Active vision inspired by mammalian fixation mechanism. Intelligent Robots and Systems (1995) 39-47