Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques
International Journal of Computer Vision
Object-based visual attention for computer vision
Artificial Intelligence
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Visual attention in 3D video games
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Visual surveillance by dynamic visual attention method
Pattern Recognition
A simple method for detecting salient regions
Pattern Recognition
Visual attention guided bit allocation in video compression
Image and Vision Computing
Segmenting salient objects from images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Attention for Robotic Cognition: A Survey
IEEE Transactions on Autonomous Mental Development
Context-Aware Saliency Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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An automatic scale selection approach is developed to improve the coherent visual attention model (Le Meur, O., Le Callet, P., Barba, D., Thoreau, D., 2006. A coherent computational approach to model bottom-up visual attention. IEEE Trans. Pattern Anal. Machine Intell. 28 (5), 802-817). The new approach uses linear regression to combine the automatic scale selection attention model with the coherent visual attention model. It is biologically more plausible because two important properties (i.e. edge detection and scale selection) of human vision are taken into account. Its performance is evaluated using a large human fixation dataset. The t-test indicates that the improved model outperforms the coherent visual attention model highly significantly in both the non-weighting and weighting cases. The new model also outperforms seven other state-of-the-art saliency prediction models highly significantly (p