Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
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 Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Eye Movements in Visual Cognition: A Computational Study
Eye Movements in Visual Cognition: A Computational Study
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Real-Time Gesture Recognition by Learning and Selective Control of Visual Interest Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning the best subset of local features for face recognition
Pattern Recognition
Dynamic visual attention model in image sequences
Image and Vision Computing
Distinctiveness of faces: A computational approach
ACM Transactions on Applied Perception (TAP)
An efficient algorithm for attention-driven image interpretation from segments
Pattern Recognition
Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream
Neural Processing Letters
Selective Visual Attention for Object Detection on a Legged Robot
RoboCup 2006: Robot Soccer World Cup X
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
Recognition of human faces: from biological to artificial vision
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Recognition of handwritten Arabic (Indian) numerals using Radon-Fourier-based features
ISPRA'10 Proceedings of the 9th WSEAS international conference on Signal processing, robotics and automation
The use of radon transform in handwritten Arabic (Indian) numerals recognition
WSEAS Transactions on Computers
A model of saliency-based selective attention for machine vision inspection application
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Recognition of Arabic (Indian) bank check digits using log-gabor filters
Applied Intelligence
“What” and “where” information based attention guidance model
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
An evolutionary feature-based visual attention model applied to face recognition
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Top-down attention guided object detection
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Attention improves the recognition reliability of backpropagation network
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Parallel pattern recognition requires great computational resources; it is NP-complete. From an engineering point of view it is desirable to achieve good performance with limited resources. For this purpose, we develop a serial model for visual pattern recognition based on the primate selective attention mechanism. The idea in selective attention is that not all parts of an image give us information. If we can attend only to the relevant parts, we can recognize the image more quickly and using less resources. We simulate the primitive, bottom-up attentive level of the human visual system with a saliency scheme and the more complex, top-down, temporally sequential associative level with observable Markov models. In between, there is a neural network that analyses image parts and generates posterior probabilities as observations to the Markov model. We test our model first on a handwritten numeral recognition problem and then apply it to a more complex face recognition problem. Our results indicate the promise of this approach in complicated vision applications.