Abstract
We present a biologically motivated saliency map model for active perception, and its applications to locate candidate regions of interest on various real images for further high-level analysis. The model is based on the idea that an image is memorized and recognized by the way of consecutive fixations of moving eyes on the most informative image fragments. The model suggested herein guides selecting the most informative regions of an image purely based on the properties of the input image. In order to evaluate the performance of our model, we first simulated it on various color images of natural environment, then, performed psychological human test with the same test image which were inputted to the model, and finally compared the two results to verify whether the output of the model is right. Experimental results were shown and they indicate the practicality and the promise of the model in complicated vision applications.
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References
Bajcsy, R.: Active perception. Proc. IEEE 76(8) (1998) 996–1005
Duncan, J., Humphreys, J.: Visual search and stimulus similarity. Psychological Reviews 96(1989)433–458
Fukushima, K., Imagawa, T.: Recognition and Segmentation of Connected Characters with Selective Attention. Neural Networks 6 (1993) 33–41
Eriksen, C., At.James, J.: Visual attention within and around the field of focal attention:a zoom lens model. Perception Psychology 40(4) (1986) 225–240
Giefing, G., Mallot, H.: Saccadic Object Recognition with an Active Vision System., 10th European Conf. on Artificial Intelligence (1992) 803–805
Itti, L., Koch, C.,: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40(10-12) (2000) 1489–1506
Koch, C., Ullman, S.: Shifts in Selective Visual Attention: Towards the Underlying Neu-ral Circuitry. Human Neurobiology 4 (1985) 219–227
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
Mozer, M.: The Perception of Multiple Objects: a Connectionist Approach. MIT Press, Cambridge, MA (1991)
Treisman, A., Gelade, G.: A Feature-integration theory of Attention. Cognitive Psychology 12(1) (1980) 97–136
Tsotsos, J., Culhane, S., Winky, Y., Yuzhong, L., Davis, N., Nuflo, F.: Modeling Visual Attention via Selective Tuning. Artificial Intelligence 78 (1995) 507–545
Wolfe, J., Cave, K.: Guided Search: An Alternative to Feature Integration Model of Visual Search. Journal of Experimental Psychology: Human Perception and Performance 15 (1989) 419–433
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© 2002 Springer-Verlag Berlin Heidelberg
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Cheoi, K., Lee, Y. (2002). A Saliency Map Model for Active Perception Using Color Information and Local Competitive Mechanism. In: Ishizuka, M., Sattar, A. (eds) PRICAI 2002: Trends in Artificial Intelligence. PRICAI 2002. Lecture Notes in Computer Science(), vol 2417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45683-X_35
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DOI: https://doi.org/10.1007/3-540-45683-X_35
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