Computer Vision Pattern Recognition

Attention and Performance in Computational Vision: Second by Lucas Paletta, John K. Tsotsos, Erich Rome, Glyn Humphreys

By Lucas Paletta, John K. Tsotsos, Erich Rome, Glyn Humphreys

This e-book constitutes the completely refereed post-proceedings of the second one foreign Workshop on cognizance and function in Computational imaginative and prescient, WAPCV 2004, held in Prague, Czech Republic in may possibly 2004. The sixteen revised complete papers provided including an invited paper have been rigorously chosen in the course of rounds of reviewing and development. The papers are equipped in topical sections on realization in item and scene reputation, architectures for sequential awareness, biologically believable types for consciousness, and purposes of attentive imaginative and prescient.

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Additional info for Attention and Performance in Computational Vision: Second International Workshop, WAPCV 2004, Prague, Czech Republic, May 15, 2004, Revised Selected Papers ... Vision, Pattern Recognition, and Graphics)

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Cognitive Psychology, 25:43–110, 1993. 8. L. Itti. Models of bottom-up and top-down visual attention. Thesis, January 2000. 9. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 20(11): 1254–1259, November 1998. 10. C. Koch and S. Ullman. Shifts in selective visual attention: towards the underlying neural vircuity. Human Neurobiology, 4:219–227, 1985. 11. N. M. Tikhomirov. epsilon-entropy and epsilon-capacity of sets in functional spaces.

De Verdiére and J. L. Crowley. Visual recognition using local appearance. In Proc. European Conference on Computer Vision, 1998. 4. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scaleinvariant learning. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 264–271,2003. 5. J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 3(3):209–226, 1977.

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proc. 8th Int’l Conf. Computer Vision, volume 2, pages 416–423, July 2001. 13. U. Neisser. Cognitive Psychology. Appleton-Century-Crofts, New York, 1967. 14. S. Nene, S. Nayar, and H. Murase. Columbia object image library (coil-100). Technical Report CUCS-006-96, Department of Computer Science, Columbia University, February 1996. 15. N. H. Ballard. An active vision architecture based on iconic representations.

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