LEADER 01601nam2-2200433li-450 001 990000210280203316 005 20180312154907.0 010 $a3-540-58429-3 035 $a0021028 035 $aUSA010021028 035 $a(ALEPH)000021028USA01 035 $a0021028 100 $a20001109d1994----km-y0itay0103----ba 101 0 $aeng 102 $aGW 200 1 $aParallel computer routing and communication$efirst International workshop, PCRCW '94$eSeattle, Washington, USA, May 16-18, 1994$eproceedings$fKevin Bolding, Lawrence Snyder (eds.) 210 $aBerlin [etc.]$cSpringer-Verlag$dcopyr. 1994 215 $aIX, 317 p.$cill.$d25 cm 225 2 $aLecture notes in computer science$v853 410 0$10010020264$12001$aLecture notes in computer science 610 1 $acongressi$aseattle$a1994 610 1 $aelaborazione dei dati$areti di trasmissione$acongressi$a1994 610 1 $aelaborazione parallela$acongressi$a1994 676 $a00435$9Multielaborazione 702 1$aBolding,$bKevin 702 1$aSnyder,$bLawrence 710 12$aInternational workshop PCRCW'94$d1.$eSeattle$f1994$0754802 801 $aSistema bibliotecario di Ateneo dell' Universitą di Salerno$gRICA 912 $a990000210280203316 951 $a001 LNCS (853)$b0017413$c001$d00103655 959 $aBK 969 $aSCI 979 $c19960112 979 $c20001110$lUSA01$h1714 979 $aALANDI$b90$c20010130$lUSA01$h1739 979 $c20020403$lUSA01$h1629 979 $aPATRY$b90$c20040406$lUSA01$h1615 996 $aParallel computer routing and communication$91519012 997 $aUNISA LEADER 07265nam 2200721 450 001 996465910203316 005 20210317152254.0 010 $a3-540-77343-6 024 7 $a10.1007/978-3-540-77343-6 035 $a(CKB)1000000000490348 035 $a(SSID)ssj0000316387 035 $a(PQKBManifestationID)11246741 035 $a(PQKBTitleCode)TC0000316387 035 $a(PQKBWorkID)10281777 035 $a(PQKB)10069465 035 $a(DE-He213)978-3-540-77343-6 035 $a(MiAaPQ)EBC337267 035 $a(MiAaPQ)EBC4976517 035 $a(MiAaPQ)EBC6413290 035 $a(Au-PeEL)EBL337267 035 $a(OCoLC)808680724 035 $a(PPN)123731763 035 $a(EXLCZ)991000000000490348 100 $a20210317d2007 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAttention in cognitive systems $etheories and systems from an interdisciplinary viewpoint ; 4th International Workshop on Attention in Cognitive Systems, WAPCV 2007, Hyderabad, India, January 8, 2007 : revised selected papers /$fLucas Paletta, Erich Rome 205 $a1st ed. 2007. 210 1$aBerlin, Germany ;$aNew York, New York :$cSpringer,$d[2007] 210 4$d©2007 215 $a1 online resource (XI, 500 p.) 225 1 $aLecture notes in computer science,$x0302-9743 ;$v4840.$aLecture notes in artificial intelligence 300 $aEarlier conferences titled: Attention and performance in computational vision. 300 $a"euCognition"--Cover. 311 $a3-540-77342-8 320 $aIncludes bibliographical references and index. 327 $aEmbodiment of Attention -- The Embodied Dynamics of Emotion, Appraisal and Attention -- The Role of Attention in Creating a Cognitive System -- The Influence of the Body and Action on Spatial Attention -- Abstraction Level Regulation of Cognitive Processing Through Emotion-Based Attention Mechanisms -- Embodied Active Vision in Language Learning and Grounding -- Language Label Learning for Visual Concepts Discovered from Video Sequences -- Cognitive Control of Attention -- Learning to Attend ? From Bottom-Up to Top-Down -- An Attentional System Combining Top-Down and Bottom-Up Influences -- The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images -- A Bayesian Approach to Attention Control and Concept Abstraction -- Modeling of Saliency and Visual Search -- An Information Theoretic Model of Saliency and Visual Search -- An Experimental Comparison of Three Guiding Principles for the Detection of Salient Image Locations: Stability, Complexity, and Discrimination -- A Proto-object Based Visual Attention Model -- Context Driven Focus of Attention for Object Detection -- Color Saliency and Inhibition Using Static and Dynamic Scenes in Region Based Visual Attention -- I See What You See: Eye Movements in Real-World Scenes Are Affected by Perceived Direction of Gaze -- Sequential Attention -- Selective Attention in the Learning of Viewpoint and Position Invariance -- Generating Sequence of Eye Fixations Using Decision-Theoretic Attention Model -- Reinforcement Learning for Decision Making in Sequential Visual Attention -- Biologically Inspired Framework for Learning and Abstract Representation of Attention Control -- Biological Aspects of Attention -- Modeling the Dynamics of Feature Binding During Object-Selective Attention -- The Spiking Search over Time and Space Model (sSoTS): Simulating Dual Task Experiments and the Temporal Dynamics of Preview Search -- On the Role of Dopamine in Cognitive Vision -- Differences and Interactions Between Cerebral Hemispheres When Processing Ambiguous Words -- Attention in Early Vision: Some Psychophysical Insights -- Auditory Gist Perception: An Alternative to Attentional Selection of Auditory Streams? -- Applications of Attentive Vision -- Simultaneous Robot Localization and Mapping Based on a Visual Attention System -- Autonomous Attentive Exploration in Search and Rescue Scenarios -- Attention-Based Landmark Selection in Autonomous Robotics -- Simulation and Formal Analysis of Visual Attention in Cognitive Systems -- Region-Oriented Visual Attention Framework for Activity Detection -- Autonomous Attentive Exploration in Search and Rescue Scenarios. 330 $aAttentionhasbeenrepresentingacorescienti?ctopicinthedesignofAI-enabled systems within the last decades. Today, in the ongoing debate, design, and c- putationalmodelingofarti?cialcognitivesystems,attentionhasgainedacentral position as a focus of research. For instance, attentional methods are considered in investigating the interfacing of sensory and cognitive information processing, for the organization of behaviors, and for the understanding of individual and social cognition in re?ection of infant development. Whilevisualcognitionplaysacentralroleinhumanperception,?ndingsfrom neuroscience and experimental psychology have provided strong evidence about theperception-actionnatureofcognition.Theembodiednatureofsensory-motor intelligence requires a continuous and focused interplay between the control of motor activities and the interpretation of feedback from perceptual modalities. Decision making about the selection of information from the incoming sensory stream ? in tune with contextual processing on a current task and an agent?s global objectives ? becomes a further challenging issue in attentional control. Attention must operate at interfaces between bottom-up driven world int- pretation and top-down driven information selection, thus acting at the core of arti?cial cognitive systems. These insights have already induced changes in AI-related disciplines, such as the design of behavior-based robot control and the computational modeling of animats. Today, the development of enabling technologiessuch as autonomous robotic systems,miniaturizedmobile?evenwearable?sensors,andambientintelligence systems involves the real-time analysis of enormous quantities of data. These data have to be processed in an intelligent way to provide ?on time delivery? of the required relevant information. Knowledge has to be applied about what needs to be attended to, and when, and what to do in a meaningful sequence, in correspondence with visual feedback. 410 0$aLecture notes in computer science.$pLecture notes in artificial intelligence. 410 0$aLecture notes in computer science ;$v4840. 517 3 $a4th International Workshop on Attention in Cognitive Systems 517 3 $aInternational Workshop on Attention in Cognitive Systems 517 3 $aWAPCV 2007 517 3 $aFourth International Workshop on Attention in Cognitive Systems 606 $aComputer vision$vCongresses 606 $aNeurosciences$vCongresses 606 $aAttention$xComputer simulation$vCongresses 615 0$aComputer vision 615 0$aNeurosciences 615 0$aAttention$xComputer simulation 676 $a006.3/7 702 $aRome$b Erich 702 $aPaletta$b Lucas 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465910203316 996 $aAttention in Cognitive Systems$9774134 997 $aUNISA