Radial Projection: An Efficient Update Rule for Relaxation Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generic Grouping Algorithm and Its Quantitative Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Salient Curves from Images: An Analysis of the Saliency Network
International Journal of Computer Vision
On the optimal detection of curves in noisy pictures
Communications of the ACM
Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Complexity, Confusion, and Perceptual Grouping. Part II: Mapping Complexity
International Journal of Computer Vision - Joint special issue on image analysis
On the Performance of Connected Components Grouping
International Journal of Computer Vision
A Comparison of Measures for Detecting Natural Shapes in Cluttered Backgrounds
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Probabilistic Interpretation of the Saliency Network
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Quantitative Measures of Change based on Feature Organization: Eigenvalues and Eigenvectors
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Stochastic completion fields: a neural model of illusory contour shape and salience
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the distribution of saliency
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An Approach to the Parameterization of Structure for Fast Categorization
International Journal of Computer Vision
Object of interest detection by saliency learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
REM: relational entropy-based measure of saliency
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Global salient information maximization for saliency detection
Image Communication
Saliency detection using centroid weight map
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Detecting salient structures is a basic task in perceptual organization. Saliency algorithms typically mark edge-points with some saliency measure, which grows with the length and smoothness of the curve on which these edge-points lie. Here, we propose a modified saliency estimation mechanism that is based on probabilistically specified grouping cues and on curve length distributions. In this framework, the Shashua and Ullman saliency mechanism may be interpreted as a process for detecting the curve with maximal expected length. Generalized types of saliency naturally follow. We propose several specific generalizations (e.g., gray-level-based saliency) and rigorously derive the limitations on generalized saliency types. We then carry out a probabilistic analysis of expected length saliencies. Using ergodicity and asymptotic analysis, we derive the saliency distributions associated with the main curves and with the rest of the image. We then extend this analysis to finite-length curves. Using the derived distributions, we derive the optimal threshold on the saliency for discriminating between figure and background and bound the saliency-based figure-from-ground performance.