LEADER 06929nam 22008535 450 001 996466023703316 005 20240322225816.0 010 $a3-540-30572-6 024 7 $a10.1007/b105311 035 $a(CKB)1000000000212696 035 $a(SSID)ssj0000107425 035 $a(PQKBManifestationID)11114260 035 $a(PQKBTitleCode)TC0000107425 035 $a(PQKBWorkID)10012434 035 $a(PQKB)10648259 035 $a(DE-He213)978-3-540-30572-9 035 $a(MiAaPQ)EBC3068343 035 $a(PPN)123091497 035 $a(EXLCZ)991000000000212696 100 $a20100704d2005 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAttention and Performance in Computational Vision$b[electronic resource] $eSecond International Workshop, WAPCV 2004, Prague, Czech Republic, May 15, 2004, Revised Selected Papers /$fedited by Lucas Paletta, John K. Tsotsos, Erich Rome, Glyn Humphreys 205 $a1st ed. 2005. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2005. 215 $a1 online resource (VIII, 236 p.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v3368 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-24421-2 320 $aIncludes bibliographical references and index. 327 $aAttention in Object and Scene Recognition -- Distributed Control of Attention -- Inherent Limitations of Visual Search and the Role of Inner-Scene Similarity -- Attentive Object Detection Using an Information Theoretic Saliency Measure -- Architectures for Sequential Attention -- A Model of Object-Based Attention That Guides Active Visual Search to Behaviourally Relevant Locations -- Learning of Position-Invariant Object Representation Across Attention Shifts -- Combining Conspicuity Maps for hROIs Prediction -- Human Gaze Control in Real World Search -- Biologically Plausible Models for Attention -- The Computational Neuroscience of Visual Cognition: Attention, Memory and Reward -- Modeling Attention: From Computational Neuroscience to Computer Vision -- Towards a Biologically Plausible Active Visual Search Model -- Modeling Grouping Through Interactions Between Top-Down and Bottom-Up Processes: The Grouping and Selective Attention for Identification Model (G-SAIM) -- TarzaNN : A General Purpose Neural Network Simulator for Visual Attention Modeling -- Applications of Attentive Vision -- Visual Attention for Object Recognition in Spatial 3D Data -- A Visual Attention-Based Approach for Automatic Landmark Selection and Recognition -- Biologically Motivated Visual Selective Attention for Face Localization -- Accumulative Computation Method for Motion Features Extraction in Active Selective Visual Attention -- Fast Detection of Frequent Change in Focus of Human Attention. 330 $aInrecentresearchoncomputervisionsystems,attentionhasbeenplayingacrucialrolein mediatingbottom-upandtop-downpathsofinformationprocessing. Inappliedresearch, the development of enabling technologies such as miniaturized mobile sensors, video surveillance systems, and ambient intelligence systems involves the real-time analysis of enormous quantities of data. Knowledge has to be applied about what needs to be attendedto,andwhen,andwhattodoinameaningfulsequence,incorrespondencewith visual feedback. Methods on attention and control are mandatory to render computer vision systems more robust. The 2nd International Workshop on Attention and Performance in Computational Vision (WAPCV 2004) was held in the Czech Technical University of Prague, Czech Republic, as an associated workshop of the 8th European Conference on Computer - sion (ECCV 2004). The goal of this workshop was to provide an interdisciplinary forum tocommunicatecomputationalmodelsofvisualattentionfromvariousviewpoints,such as from computer vision, psychology, robotics and neuroscience. The motivation for - terdisciplinarity was communication and inspiration beyond the individual community, to focus discussion on computational modelling, to outline relevant objectives for p- formance comparison, to explore promising application domains, and to discuss these with reference to all related aspects of cognitive vision. The workshop was held as a single-day, single-track event, consisting of high-quality podium and poster presen- tions. Invited talks were given by John K. Tsotsos about attention and feature binding in biologically motivated computer vision and by Gustavo Deco about the context of attention, memory and reward from the perspective of computational neuroscience. The interdisciplinary program committee was composed of 21 internationally r- ognized researchers. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v3368 606 $aOptical data processing 606 $aArtificial intelligence 606 $aPattern recognition 606 $aComputer graphics 606 $aNeurosciences 606 $aControl engineering 606 $aRobotics 606 $aMechatronics 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aNeurosciences$3https://scigraph.springernature.com/ontologies/product-market-codes/B18006 606 $aControl, Robotics, Mechatronics$3https://scigraph.springernature.com/ontologies/product-market-codes/T19000 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 0$aComputer graphics. 615 0$aNeurosciences. 615 0$aControl engineering. 615 0$aRobotics. 615 0$aMechatronics. 615 14$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aPattern Recognition. 615 24$aComputer Graphics. 615 24$aNeurosciences. 615 24$aControl, Robotics, Mechatronics. 676 $a006.3/7 702 $aPaletta$b Lucas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTsotsos$b John K$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRome$b Erich$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHumphreys$b Glyn W$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Workshop on Attention and Performance in Computational Vision 906 $aBOOK 912 $a996466023703316 996 $aAttention and Performance in Computational Vision$9772661 997 $aUNISA