Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Goal Oriented Attention Guidance Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Using visual attention to extract regions of interest in the context of image retrieval
Proceedings of the 44th annual Southeast regional conference
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Dynamic visual attention model in image sequences
Image and Vision Computing
An attention-driven model for grouping similar images with image retrieval applications
EURASIP Journal on Applied Signal Processing
Attention links sensing to recognition
Image and Vision Computing
Attention can improve a simple model for object recognition
Image and Vision Computing
Biologically Motivated Face Selective Attention Model
Neural Information Processing
Incremental Knowledge Representation Based on Visual Selective Attention
Neural Information Processing
Improving AdaBoost Based Face Detection Using Face-Color Preferable Selective Attention
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
On Semantic Object Detection with Salient Feature
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Attention driven visual processing for an interactive dialog robot
Proceedings of the 2009 ACM symposium on Applied Computing
Temporal spectral residual: fast motion saliency detection
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A smart automatic thumbnail cropping based on attention driven regions of interest extraction
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
The use of attention and spatial information for rapid facial recognition in video
Image and Vision Computing
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Integrating context-free and context-dependent attentional mechanisms for gestural object reference
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Recognition of gestural object reference with auditory feedback
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Implementation of visual attention system using bottom-up saliency map model
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Object recognition: a focused vision based approach
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
IEEE Transactions on Image Processing
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An improved SalBayes model with GMM
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Salient region detection using weighted feature maps based on the human visual attention model
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
A biologically-inspired automatic matting method based on visual attention
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Biologically motivated visual selective attention for face localization
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
A color saliency model for salient objects detection in natural scenes
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Temporal Spectral Residual for fast salient motion detection
Neurocomputing
Saliency-Driven tactile effect authoring for real-time visuotactile feedback
EuroHaptics'12 Proceedings of the 2012 international conference on Haptics: perception, devices, mobility, and communication - Volume Part I
Assessment of computational visual attention models on medical images
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Multi-spectral dataset and its application in saliency detection
Computer Vision and Image Understanding
Tag-Saliency: Combining bottom-up and top-down information for saliency detection
Computer Vision and Image Understanding
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Attentional selection of an object for recognition is often modeled using all-or-nothing switching of neuronal connection pathways from the attended region of the retinal input to the recognition units. However, there is little physiological evidence for such all-or-none modulation in early areas. We present a combined model for spatial attention and object recognition in which the recognition system monitors the entire visual field, but attentional modulation by as little as 20% at a high level is sufficient to recognize multiple objects. To determine the size and shape of the region to be modulated, a rough segmentation is performed, based on pre-attentive features already computed to guide attention. Testing with synthetic and natural stimuli demonstrates that our new approach to attentional selection for recognition yields encouraging results in addition to being biologically plausible.