From line segments to more organized gestalts

In this paper, we reconsider the early computer vision bottom-up program, according to which higher level features (geometric structures) in an image could be built up recursively from elementary features by simple grouping principles coming from Gestalt theory. Taking advantage of the (recent) adva...

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Bibliographic Details
Published in:	2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) pp. 137 - 140
Main Authors:	Rajaei, Boshra, von Gioi, Rafael Grompone, Morel, Jean-Michel
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.03.2016
Subjects:	a contrario detection Bars Detectors Feature extraction Gestalt detector Image edge detection Image segmentation line segment detector (LSD) Manganese non-accidentalness principle number of false alarms (NFA) Stochastic processes
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Summary:	In this paper, we reconsider the early computer vision bottom-up program, according to which higher level features (geometric structures) in an image could be built up recursively from elementary features by simple grouping principles coming from Gestalt theory. Taking advantage of the (recent) advances in reliable line segment detectors, we propose three feature detectors that constitute one step up in this bottom up pyramid. For any digital image, our unsupervised algorithm computes three classic Gestalts from the set of predetected line segments: good continuations, nonlocal alignments, and bars. The methodology is based on a common stochastic a contrario model yielding three simple detection formulas, characterized by their number of false alarms. This detection algorithm is illustrated on several digital images.
DOI:	10.1109/SSIAI.2016.7459194