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

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

ISBN-10: 3540244212

ISBN-13: 9783540244219

ISBN-10: 3540305726

ISBN-13: 9783540305729

This e-book constitutes the completely refereed post-proceedings of the second one overseas Workshop on cognizance and function in Computational imaginative and prescient, WAPCV 2004, held in Prague, Czech Republic in may well 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 geared up in topical sections on realization in item and scene popularity, 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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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. AMS Translations. Series 2,17:277–364, 1961. 12. D.

The model presented here replicates these spatial and object-based effects, at the neuronal level, with temporal precision. Such effects at the neuronal level allow the model, at the systems level, to produce the search behaviour similar to that seen in humans [33] [34] and monkeys [35]. A “cross-stream” interaction between the ventral “what” pathway, responsible for the encoding of features and objects and leading from primary visual cortex (V1) through V2 and V4 to temporal cortex, and the dorsal “where” pathway, leading from V1 to parietal cortex [36][37], allows the search scan path to be drawn to behaviourally relevant locations rather than less relevant stimulus locations or blank areas.

8. Sample building recognition for object of TSG-20 database: (a) train image, (b) test image with result overlaid, (c) entropy image of (a), entropy image of (b), (e) local voting results, and (f) posterior distribution for informative appearances (IA) 40 G. Fritz et al. patterns, we expected to further disambiguate these locations from a more thorough analysis in a hierarchical framework of object recognition, dedicated to future work. Discussion. The experiments illustrate that discriminative interest operators may provide a good basis for multi-stage task based attention for object recognition.

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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) by Lucas Paletta, John K. Tsotsos, Erich Rome, Glyn Humphreys


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