Features and objects in visual processing
Scientific American
Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attentional scene segmentation: integrating depth and motion
Computer Vision and Image Understanding
Saliency, Scale and Image Description
International Journal of Computer Vision
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Stochastic Guided Search Model for Search Asymmetries in Visual Search Tasks
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Visual Attention Using Game Theory
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Computing Visual Attention from Scene Depth
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
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
A bimodal laser-based attention system
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Expert Systems with Applications: An International Journal
Dynamic visual selective attention model
Neurocomputing
Extraction of visual motion and optic flow
Neural Networks
IEEE Transactions on Neural Networks
Obstacle Categorization Based on Hybridizing Global and Local Features
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Office-mate: selective attention and incremental object perception
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Visual selective attention model considering bottom-up saliency and psychological distance
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Affective saliency map considering psychological distance
Neurocomputing
Development of visualizing earphone and hearing glasses for human augmented cognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Goal-oriented behavior generation for visually-guided manipulation task
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Implementation of face selective attention model on an embedded system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Hi-index | 0.00 |
We propose new integrated saliency map and selective motion analysis models partly inspired by a biological visual attention mechanism. The proposed models consider not only binocular stereopsis to identify a final attention area so that the system focuses on the closer area as in human binocular vision, based on the single eye alignment hypothesis, but also both the static and dynamic features of an input scene. Moreover, the proposed saliency map model includes an affective computing process that skips an unwanted area and pays attention to a desired area, which reflects the human preference and refusal in subsequent visual search processes. In addition, we show the effectiveness of considering the symmetry feature determined by a neural network and an independent component analysis (ICA) filter which are helpful to construct an object preferable attention model. Also, we propose a selective motion analysis model by integrating the proposed saliency map with a neural network for motion analysis. The neural network for motion analysis responds selectively to rotation, expansion, contraction and planar motion of the optical flow in a selected area. Experiments show that the proposed model can generate plausible scan paths and selective motion analysis results for natural input scenes.