Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Extracting Salient Curves from Images: An Analysis of the Saliency Network
International Journal of Computer Vision
Ground from figure discrimination
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
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
Figure-Ground Discrimination by Mean Field Annealing
ECCV '92 Proceedings of the Second European Conference on Computer Vision
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
On the distribution of saliency
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
| Hi-index | 0.00 |
The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms typically mark edge-points with some saliency measure, growing with the length and the smoothness of the curve on which this edge-point lies. We consider a generalization [10] of the Ullman-Shaashua saliency measure [13] and aim to analyze the saliency measure in a probabilistic context: regarding the basic grouping information (grouping cues) as random variables, we use ergodicity and asymptotic analysis to derive the saliency distribution associated with the main curves ("figure") and with the rest of the image ("background"). We further consider finite-length curves and analyze their saliency values. We observed several discrepancies between the observed distributions and the predictions we supply, discuss their sources and propose a way to account for them. Then, based on the derived distributions we show how to set threshold on the saliency for deciding optimally between figure and background, how to choose cues which are usable for saliency, and how to estimate bounds on the saliency performance.