Introduction to algorithms
Extracting Salient Curves from Images: An Analysis of the Saliency Network
International Journal of Computer Vision
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Local Parallel Computation of Stochastic Completion Fields
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Ground from Figure Discrimination
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Volterra Filtering of Noisy Images of Curves
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
On the Distribution of Saliency
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
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The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms aim to find image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This note proposes a modified saliency estimation mechanism, which is based on probabilistically specified grouping cues and on length estimation. In the context of the proposed method, the well-known saliency mechanism, proposed by Shaashua and Ullman [SU88], may be interpreted as a process trying to detect the curve with maximal expected length. The new characterization of saliency using probabilistic cues is conceptually built on considering the curve starting at a feature point, and estimating the distribution of the length of this curve, iteratively. Different saliencies, like the expected length, may be specified as different functions of this distribution. There is no need however to actually propagate the distributions during the iterative process. The proposed saliency characterization is associated with several advantages: First, unlike previous approaches, the search for the "best group" is based on a probabilistic characterization, which may be derived and verified from typical images, rather than on pre-conceived opinion about the nature of figure subsets. Therefore, it is expected also to be more reliable. Second, the probabilistic saliency is more abstract and thus more generic than the common geometric formulations. Therefore, it lends itself to different realizations of saliencies based on different cues, in a systematic rigorous way. To demonstrate that, we created, as instances of the general approach, a saliency process which is based on grey level similarity but still preserve a similar meaning. Finally, the proposed approach gives another interpretation for the measure than makes one curve a winner, which may often be more intuitive to grasp, especially as the saliency levels has a clear meaning of say, expected curve length.