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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Vydané v:	2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) s. 137 - 140
Hlavní autori:	Rajaei, Boshra, von Gioi, Rafael Grompone, Morel, Jean-Michel
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.03.2016
Predmet:	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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Popis
Shrnutí:	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