Computational gestalts and perception thresholds

• Published in: Journal of physiology, Paris Vol. 97; no. 2; pp. 311 - 324
• Main Authors: Desolneux, Agnès, Moisan, Lionel, Morel, Jean-Michel
• Format: Journal Article
• Language: English
• Published: France Elsevier Ltd 01.03.2003; Elsevier
• Subjects: Animals; Computer vision; Detection threshold; Gestalt Theory; Humans; Mathematical Computing; Pattern Recognition, Visual - physiology; Sensory Thresholds - physiology; Visual perception; Visual Perception - physiology; Computer vision; Gestalt theory; Detection threshold; Visual perception
• ISSN: 0928-4257; 1769-7115
• DOI: 10.1016/j.jphysparis.2003.09.006

In 1923, Max Wertheimer proposed a research programme and method in visual perception. He conjectured the existence of a small set of geometric grouping laws governing the perceptual synthesis of phenomenal objects, or “gestalt” from the atomic retina input. In this paper, we review this set of geometric grouping laws, using the works of Metzger, Kanizsa and their schools. In continuation, we explain why the Gestalt theory research programme can be translated into a Computer Vision programme. This translation is not straightforward, since Gestalt theory never addressed two fundamental matters: image sampling and image information measurements. Using these advances, we shall show that gestalt grouping laws can be translated into quantitative laws allowing the automatic computation of gestalts in digital images. From the psychophysical viewpoint, a main issue is raised: the computer vision gestalt detection methods deliver predictable perception thresholds. Thus, we are set in a position where we can build artificial images and check whether some kind of agreement can be found between the computationally predicted thresholds and the psychophysical ones. We describe and discuss two preliminary sets of experiments, where we compared the gestalt detection performance of several subjects with the predictable detection curve. In our opinion, the results of this experimental comparison support the idea of a much more systematic interaction between computational predictions in Computer Vision and psychophysical experiments.