Proc. SPIE. 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
KEYWORDS: Image processing algorithms and systems, Lithium, Visual process modeling, Image segmentation, Computer vision technology, Signal processing, Machine vision, Object recognition, Visual system, Probability theory
As segmentation step does not allow recovering semantic objects, 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 seem to be at work for perceptual grouping and some implementations have been proposed by computer vision. However, few of these works model the use of several properties in order to trigger a grouping, even if it can lead to an increase in robustness. We propose a cooperative approach for perceptual grouping by combining the influence of several Gestalt properties for each hypothesis. We make use of Dempster-Shafer formalism, as it can prevent conflicting hypotheses from jamming the grouping process.