Perceptual Organization for Scene Segmentation and Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Grouping as a searching process for minimum-energy configurations of labelled random fields
Computer Vision and Image Understanding
Automatic Detection of Human Nudes
International Journal of Computer Vision - 1998 Marr Prize
Deformable Shape Detection and Description via Model-Based Region Grouping
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Grouping Principle and Four Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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Content-Based Image Retrieval: on theWay to Object Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
IEEE Transactions on Computers
Energy-Based Perceptual Segmentation Using an Irregular Pyramid
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Color image segmentation using an enhanced Gradient Network Method
Pattern Recognition Letters
A new perception-based segmentation approach using combinatorial pyramids
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Learning discriminative localization from weakly labeled data
Pattern Recognition
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Segmentation is usually unable to cope with artifacts due to slight change in lighting conditions or object occlusion for instance. That is why perceptual grouping is often used to overcome segmentation's lacks. This refers to the ability of human visual system to impose structure and regularity over signal-based data. Gestalt psychologists have exhibited some properties which are used during perceptual grouping, such as proximity, continuity, or symmetry. Then, some implementations of these have been proposed in computer vision. However, most of these works rely on contour-based primitives. Besides, they often use one single property to merge close regions, which may not be sufficiently robust. We propose a new framework for bottom-up perceptual grouping, which relies on a region-based segmentation. It allows us to use region or contour information, when it is the most suitable. Besides, we propose to trigger a grouping when several Gestalt properties support it. This could increase the robustness of perceptual grouping. We use Dempster-Shafer theory to combine the influence of several Gestalt properties over each grouping, as it is especially designed for this. We also present numerous promising results, which show the efficiency of our approach.