By Ola Ramström, Henrik I Christensen (auth.), Lucas Paletta, John K. Tsotsos, Erich Rome, Glyn Humphreys (eds.)
Inrecentresearchoncomputervisionsystems,attentionhasbeenplayingacrucialrolein mediatingbottom-upandtop-downpathsofinformationprocessing. Inappliedresearch, the advance of permitting applied sciences similar to miniaturized cellular sensors, video surveillance structures, and ambient intelligence platforms comprises the real-time research of large amounts of knowledge. wisdom needs to be utilized approximately what should be attendedto,andwhen,andwhattodoinameaningfulsequence,incorrespondencewith visible suggestions. tools on cognizance and keep an eye on are obligatory to render laptop imaginative and prescient platforms extra strong. The second foreign Workshop on consciousness and function in Computational imaginative and prescient (WAPCV 2004) used to be held within the Czech Technical collage of Prague, Czech Republic, as an linked workshop of the eighth ecu convention on laptop - sion (ECCV 2004). The target of this workshop was once to supply an interdisciplinary discussion board tocommunicatecomputationalmodelsofvisualattentionfromvariousviewpoints,such as from computing device imaginative and prescient, psychology, robotics and neuroscience. the inducement for - terdisciplinarity used to be conversation and proposal past the person group, to concentration dialogue on computational modelling, to stipulate appropriate pursuits for p- formance comparability, to discover promising program domain names, and to debate those on the subject of all similar facets of cognitive imaginative and prescient. The workshop was once held as a single-day, single-track occasion, which includes fine quality podium and poster presen- tions. Invited talks got by way of John okay. Tsotsos approximately recognition and have binding in biologically inspired desktop imaginative and prescient and by means of Gustavo Deco in regards to the context of recognition, reminiscence and present from the point of view of computational neuroscience. The interdisciplinary software committee was once composed of 21 the world over r- ognized researchers.
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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
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. In Proc. International Conference on Pattern Recognition, ICPR 2004, volume II, pages 15–18.
Lindenbaum. A probabilistic estimation approach for dynamic visual search. In Proceedings of WAPCV03 - First International Workshop on Attention and Performance in Computer Vision, pages 1–8, April 2003. 3. T. Avraham and M. Lindenbaum. Dynamic visual search using inner scene similarity - algorithms and inherent limitations. In Proceedings of ECCV04 - The 8th European Conference on Computer Vision,LNCS 3022:pages 58–70, May 2004. 4. B. Bauer, P. Jolicoeur, and W. B. Cowan. Visual search for colour targets that are or are not linearly separable from distractors.
While  determine saliency from a class-independent local measure of sample density, proving superior performance to generic interest operators,  went further by outlining a selection process on informative image fragments as features for classiﬁcation, conﬁrming performance improvements using speciﬁc in contrast to generic features. The key contribution of this work to visual attention is to investigate information theoretic saliency measures with respect to object search and recognition, and to settle a process chain from generic to speciﬁc task based feature extraction with the goal of Attentive Object Detection Using an Information Theoretic Saliency Measure 31 cascaded attention.