A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Split-and-merge image segmentation based on localized feature analysis and statistical tests
CVGIP: Graphical Models and Image Processing
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Game-Theoretic Integration for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real Time Implementation of the Saliency-Based Model of Visual Attention on a SIMD Architecture
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Assessing the contribution of color in visual attention
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Optimal Cue Combination for Saliency Computation: A Comparison with Human Vision
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Assessing the contribution of color in visual attention
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Local energy saliency for bottom-up visual attention
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Linear vs. nonlinear feature combination for saliency computation: a comparison with human vision
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
A visual attention-based approach for automatic landmark selection and recognition
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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This paper reports a novel Multiscale Attention-based Pre-Segmentation method (MAPS) which is built around the multi-feature, multiscale, saliency-based model of visual attention. From the saliency map, provided by the attention algorithm, MAPS first derives the spatial locations of salient regions that will be considered further in the segmentation process. Then, the salient scale and the salient feature of each salient region is determined by exploring the scale and feature spaces computed by the model of attention. A first and rough multiscale segmentation of the salient regions is performed on the corresponding salient scale. This innovative presegmentation but yet uncomplete procedure is followed by some refined segmentation that operates in the salient feature at full resolution.