The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Konstantinos Rapantzikos - One of the best experts on this subject based on the ideXlab platform.
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Wavelet Based Estimation of Saliency Maps in Visual Attention Algorithms
Lecture Notes in Computer Science, 2020Co-Authors: Nicolas Tsapatsoulis, Konstantinos RapantzikosAbstract:This paper deals with the problem of saliency map estimation in computational models of visual attention. In particular, we propose a wavelet based approach for efficient computation of the topographic Feature maps. Given that wavelets and multiresolution Theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our proposal goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation) that are used to create the topographic Feature maps. Topographic Feature maps are then combined through a sigmoid function to produce the final saliency map. The computational model we use is based on the Feature Integration Theory of Treisman et al and follows the computational philosophy of this Theory proposed by Itti et al. A series of experiments, conducted in a video encoding setup, show that the proposed method compares well against other implementations found in the literature both in terms of visual trials and computational complexity.
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ICANN (2) - Wavelet based estimation of saliency maps in visual attention algorithms
Artificial Neural Networks – ICANN 2006, 2006Co-Authors: Nicolas Tsapatsoulis, Konstantinos RapantzikosAbstract:This paper deals with the problem of saliency map estimation in computational models of visual attention. In particular, we propose a wavelet based approach for efficient computation of the topographic Feature maps. Given that wavelets and multiresolution Theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our proposal goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation) that are used to create the topographic Feature maps. Topographic Feature maps are then combined through a sigmoid function to produce the final saliency map. The computational model we use is based on the Feature Integration Theory of Treisman et al and follows the computational philosophy of this Theory proposed by Itti et al. A series of experiments, conducted in a video encoding setup, show that the proposed method compares well against other implementations found in the literature both in terms of visual trials and computational complexity.
Eric Soetens - One of the best experts on this subject based on the ideXlab platform.
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Feature Integration and spatial attention: common processes for endogenous and exogenous orienting
Psychological Research PRPF, 2010Co-Authors: David Henderickx, Kathleen Maetens, Eric SoetensAbstract:Briand (J Exp Psychol Hum Percept Perform 24:1243–1256, 1998 ) and Briand and Klein (J Exp Psychol Hum Percept Perform 13:228–241, 1987 ) demonstrated that spatial cueing effects are larger for detecting conjunction of Features than for detecting simple Features when spatial attention is oriented exogenously, and not when attention is oriented endogenously. Their results were interpreted as if only exogenous attention affects the posterior spatial attention system that performs the Feature binding function attributed to spatial attention by Treisman’s Feature Integration Theory (FIT; 1980). In a series of 6 experiments, we attempted to replicate Briand’s findings. Manipulations of distractor string size and symmetry of stimulus presentation left and right from fixation were implemented in Posner’s cueing paradigm. The data indicate that both exogenous and endogenous cueing address the same attentional mechanism needed for Feature binding. The results also limit the generalisability of Briand’s proposal concerning the role of exogenous attention in Feature Integration. Furthermore, the importance to control the effect of unintended attentional capture in a cueing task is demonstrated.
William G Hayward - One of the best experts on this subject based on the ideXlab platform.
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Feature Integration Theory revisited dissociating Feature detection and attentional guidance in visual search
Journal of Experimental Psychology: Human Perception and Performance, 2009Co-Authors: Louis K H Chan, William G HaywardAbstract:In Feature Integration Theory (FIT; A. Treisman & S. Sato, 1990), Feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed results are difficult to explain in its absence. The present study measured dimension-specific performance during detection and localization, tasks that require operation of dimensional modules and the master map, respectively. Results showed a dissociation between tasks in terms of both dimension-switching costs and cross-dimension attentional capture, reflecting a dimension-specific nature for detection tasks and a dimension-general nature for localization tasks. In a Feature-discrimination task, results precluded an explanation based on response mode. These results are interpreted to support FIT’s postulation that different mechanisms are involved in parallel and focal attention searches. This indicates that the FIT architecture should be adopted to explain the current results and that a variety of visual attention findings can be addressed within this framework.
Hermann J. Müller - One of the best experts on this subject based on the ideXlab platform.
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A theoretical attempt to revive the serial/parallel-search dichotomy
Attention Perception & Psychophysics, 2019Co-Authors: Heinrich René Liesefeld, Hermann J. MüllerAbstract:A core distinction in Anne Treisman’s Feature-Integration Theory (FIT) is in that between parallel and serial search. We outline this dichotomy and selectively review the reasons why it has largely been abandoned in the visual-search community—namely, its theoretical dispensability, failure to find reliable yardsticks for differentiating parallel and serial search, and falsification of core predictions. We then go on to introduce a new theoretical framework that, we argue, clears up some of the theoretical confusion by merging FIT with various competing theories. This framework’s core Feature is the distinction between and characterization of two fundamentally different search modes: one in which attention is guided to a single item via a priority map ( priority guidance ), and one in which clumps of multiple items are scanned in parallel in a spatially systematic order ( clump scanning ). Finally, we will elaborate how this new theoretical framework can resolve current controversies in the literature and how it relates to other existing theories. We (somewhat optimistically) believe that the outcome of this theoretical exercise is a unification of theories of visual search that can explain, or at least is consistent with, all phenomena reported in the visual-search literature that have previously been accounted for by various conflicting theories.
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Additional-singleton interference in efficient visual search: a common salience route for detection and compound tasks.
Attention Perception & Psychophysics, 2009Co-Authors: Michael Zehetleitner, Michael J. Proulx, Hermann J. MüllerAbstract:In efficient search for Feature singleton targets, additional singletons (ASs) defined in a nontarget dimension are frequently found to interfere with performance. All search tasks that are processed via a spatial saliency map of the display would be predicted to be subject to such AS interference. In contrast, dual-route models, such as Feature Integration Theory, assume that singletons are detected not via a saliency map, but via a nonspatial route that is immune to interference from cross-dimensional ASs. Consistent with this, a number of studies have reported absent interference effects in detection tasks. However, recent work suggests that the failure to find such effects may be due to the particular frequencies at which ASs were presented, as well as to their relative saliency. These two factors were examined in the present study. In contrast to previous reports, cross-dimensional ASs were found to slow detection (target-present and target-absent) responses, modulated by both their frequency of occurrence and saliency (relative to the target). These findings challenge dual-route models and support single-route models, such as dimension weighting and guided search.
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Visual search and selective attention
Visual Cognition, 2006Co-Authors: Hermann J. Müller, Joseph KrummenacherAbstract:Visual search is a key paradigm in attention research that has proved to be a test bed for competing theories of selective attention. The starting point for most current theories of visual search has been Treisman's “Feature Integration Theory” of visual attention (e.g., Treisman & Gelade, 1980). A number of key issues that have been raised in attempts to test this Theory are still pertinent questions of research today: (1) The role and (mode of) function of bottom-up and top-down mechanisms in controlling or “guiding” visual search; (2) in particular, the role and function of implicit and explicit memory mechanisms; (3) the implementation of these mechanisms in the brain; and (4) the simulation of visual search processes in computational or, respectively, neurocomputational (network) models. This paper provides a review of the experimental work and the—often conflicting—theoretical positions on these thematic issues, and goes on to introduce a set of papers by distinguished experts in fields designed to ...
Nicolas Tsapatsoulis - One of the best experts on this subject based on the ideXlab platform.
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Wavelet Based Estimation of Saliency Maps in Visual Attention Algorithms
Lecture Notes in Computer Science, 2020Co-Authors: Nicolas Tsapatsoulis, Konstantinos RapantzikosAbstract:This paper deals with the problem of saliency map estimation in computational models of visual attention. In particular, we propose a wavelet based approach for efficient computation of the topographic Feature maps. Given that wavelets and multiresolution Theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our proposal goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation) that are used to create the topographic Feature maps. Topographic Feature maps are then combined through a sigmoid function to produce the final saliency map. The computational model we use is based on the Feature Integration Theory of Treisman et al and follows the computational philosophy of this Theory proposed by Itti et al. A series of experiments, conducted in a video encoding setup, show that the proposed method compares well against other implementations found in the literature both in terms of visual trials and computational complexity.
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ICANN (2) - Wavelet based estimation of saliency maps in visual attention algorithms
Artificial Neural Networks – ICANN 2006, 2006Co-Authors: Nicolas Tsapatsoulis, Konstantinos RapantzikosAbstract:This paper deals with the problem of saliency map estimation in computational models of visual attention. In particular, we propose a wavelet based approach for efficient computation of the topographic Feature maps. Given that wavelets and multiresolution Theory are naturally connected the usage of wavelet decomposition for mimicking the center surround process in humans is an obvious choice. However, our proposal goes further. We utilize the wavelet decomposition for inline computation of the Features (such as orientation) that are used to create the topographic Feature maps. Topographic Feature maps are then combined through a sigmoid function to produce the final saliency map. The computational model we use is based on the Feature Integration Theory of Treisman et al and follows the computational philosophy of this Theory proposed by Itti et al. A series of experiments, conducted in a video encoding setup, show that the proposed method compares well against other implementations found in the literature both in terms of visual trials and computational complexity.
