A contrario detection of good continuation of points

Jose Lezama; Rafael Grompone Von Gioi; Gregory Randall; Jean-Michel Morel

doi:10.1109/ICIP.2014.7025964

A contrario detection of good continuation of points

Jose Lezama
, Rafael Grompone Von Gioi
, Gregory Randall
, Jean-Michel Morel

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

We will consider the problem of detecting configurations of points regularly spaced and lying on a smooth curve. This corresponds to the notion of good continuation introduced in the Gestalt theory. We present a robust algorithm for clustering points along such curves, whilst at the same time discarding noisy samples. Based on the a contrario methodology, the detector builds upon a simple, symmetric primitive for a triplet of points, and finds statistically meaningful chains of such triplets. An efficient implementation is proposed using the Floyd-Warshall algorithm. Experiments on synthetic and real data show that the method is able to identify the perceptually relevant configuration of points in good continuation. © 2014 IEEE.

Original language	English
Title of host publication	2014 IEEE International Conference on Image Processing, ICIP 2014
Publisher	IEEE
Pages	4757-4761
ISBN (Print)	9781479957514
DOIs	https://doi.org/10.1109/ICIP.2014.7025964
Publication status	Published - 28 Jan 2014
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

a contrario
curves
Gestalt
good continuation detection
points

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10.1109/ICIP.2014.7025964

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title = "A contrario detection of good continuation of points",

abstract = "We will consider the problem of detecting configurations of points regularly spaced and lying on a smooth curve. This corresponds to the notion of good continuation introduced in the Gestalt theory. We present a robust algorithm for clustering points along such curves, whilst at the same time discarding noisy samples. Based on the a contrario methodology, the detector builds upon a simple, symmetric primitive for a triplet of points, and finds statistically meaningful chains of such triplets. An efficient implementation is proposed using the Floyd-Warshall algorithm. Experiments on synthetic and real data show that the method is able to identify the perceptually relevant configuration of points in good continuation. {\textcopyright} 2014 IEEE.",

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doi = "10.1109/ICIP.2014.7025964",

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A contrario detection of good continuation of points. / Lezama, Jose; Von Gioi, Rafael Grompone; Randall, Gregory et al.
2014 IEEE International Conference on Image Processing, ICIP 2014. IEEE, 2014. p. 4757-4761 7025964.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

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AU - Von Gioi, Rafael Grompone

AU - Randall, Gregory

AU - Morel, Jean-Michel

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - We will consider the problem of detecting configurations of points regularly spaced and lying on a smooth curve. This corresponds to the notion of good continuation introduced in the Gestalt theory. We present a robust algorithm for clustering points along such curves, whilst at the same time discarding noisy samples. Based on the a contrario methodology, the detector builds upon a simple, symmetric primitive for a triplet of points, and finds statistically meaningful chains of such triplets. An efficient implementation is proposed using the Floyd-Warshall algorithm. Experiments on synthetic and real data show that the method is able to identify the perceptually relevant configuration of points in good continuation. © 2014 IEEE.

AB - We will consider the problem of detecting configurations of points regularly spaced and lying on a smooth curve. This corresponds to the notion of good continuation introduced in the Gestalt theory. We present a robust algorithm for clustering points along such curves, whilst at the same time discarding noisy samples. Based on the a contrario methodology, the detector builds upon a simple, symmetric primitive for a triplet of points, and finds statistically meaningful chains of such triplets. An efficient implementation is proposed using the Floyd-Warshall algorithm. Experiments on synthetic and real data show that the method is able to identify the perceptually relevant configuration of points in good continuation. © 2014 IEEE.

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SP - 4757

EP - 4761

BT - 2014 IEEE International Conference on Image Processing, ICIP 2014

PB - IEEE

ER -

A contrario detection of good continuation of points

Abstract

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Research Keywords

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