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 booklet constitutes the completely refereed post-proceedings of the second one overseas Workshop on consciousness and function in Computational imaginative and prescient, WAPCV 2004, held in Prague, Czech Republic in might 2004. The sixteen revised complete papers provided including an invited paper have been conscientiously chosen in the course of rounds of reviewing and development. The papers are prepared in topical sections on recognition in item and scene popularity, architectures for sequential recognition, biologically believable versions for cognizance, and functions of attentive imaginative and prescient.

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Extra resources 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)

Example text

Within V4, the early spatial focus of attention becomes object-based over time. This is due to the fact that V4 is subject to two concurrent biases: A spatial bias from LIP and an object-related bias from IT. Thus, object-based attention evolves in the model’s ventral pathway due to its object-related feedback biases. The development of objectbased attention in V4 is dependent on the resolution of competition between objects in IT. In addition to the feed forward inputs from V4, competition between objects in IT is biased by a feedback current from prefrontal cortex, which is assumed to hold a working memory template of the target object.

Matas. Object recognition using local affine frames on distinguished regions. In Proc. British Machine Vision Conference, pages 113–122, 2002. 15. L. Paletta and C. Greindl. Context based object detection from video. In Proc. International Conference on Computer Vision Systems, pages 502–512, 2003. 16. E. Parzen. On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33:1065–1076,1962. 17. R. Quinlan. 5 Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.

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. 6. G. Fritz, L. Paletta, and H. Bischof. Object recognition using local information content.

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