Image segmentation by a contrario simulation

Burrus, Nicolas; Berthet, Thierry; Jolion, Jean-Michel

doi:10.1016/j.patcog.2009.01.003

Cited by 22 publications

(19 citation statements)

References 20 publications

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“…Recall that b is the minimum number of elements necessary to uniquely characterize a given parametric model. It is easy to prove, by the linearity of expectation, that the expected number of ε-meaningful models in a finite set of random models is smaller than ε. Alternatively, N tests can be empirically set by analyzing a training dataset [8], providing a tighter bound for the expectation. Equation (5.5) provides a formal probabilistic method for testing if a model is likely to happen at random or not.…”

Section: 2mentioning

confidence: 99%

A Biclustering Framework for Consensus Problems

Tepper¹,

Sapiro²

2014

SIAM J. Imaging Sci.

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Abstract. We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation. We are interested in merging solutions from different grouping algorithms, distilling all their good qualities into a consensus solution. In this paper, we propose a bi-clustering framework and perspective for reaching consensus in such grouping problems. In particular, this is the first time that the task of finding/fitting multiple parametric models to a dataset is formally posed as a consensus problem. We highlight the equivalence of these tasks and establish the connection with the computational Gestalt program, that seeks to provide a psychologically-inspired detection theory for visual events. We also present a simple but powerful bi-clustering algorithm, specially tuned to the nature of the problem we address, though general enough to handle many different instances inscribed within our characterization. The presentation is accompanied with diverse and extensive experimental results in clustering, community detection, and multiple parametric model estimation in image processing applications.

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Section: 2mentioning

confidence: 99%

A Biclustering Framework for Consensus Problems

Tepper¹,

Sapiro²

2014

SIAM J. Imaging Sci.

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“…Among the exceptions, we can cite for example [10], in which the P F A distribution is learned from a database. In [14], random simulations are used to estimate a joint P F A distribution. For the two following features, we propose to learn their P F A online, using the weighted sampling procedure described in Algorithm 1.…”

Section: ) Priormentioning

confidence: 99%

“…These approaches use the fact that a long and contrasted level line is very unlikely in the randomness. In [14], [15], authors proposed to use candidates from an algorithm producing oversegmented regions. These regions are then merged upon the a contrario hypothesis that two adjacent regions have, in noise, a very low probability to present identical characteristics.…”

Section: Introductionmentioning

confidence: 99%

A contrario edge detection with edgelets

Widynski

Mignotte

2011

2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

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Abstract-Edge detection remains an active problem in the image processing community, because of the high complexity of natural images. In the last decade, Desolneux et al. proposed a novel detection approach, parameter free, based on the Helmhotz principle. Applied to the edge detection field, this means that observing a true edge in random and independent conditions is very unlikely, and then considered as meaningful. However, overdetection may occur, partly due to the use of a single pixelwise feature. In this paper, we propose to introduce higher level information in the a contrario framework, by computing several features along a set of connected pixels (an edgelet). Among the features, we introduce a shape prior, learned on a database. We propose to estimate online the a contrario distributions of the two other features, namely the gradient and the texture, by a Monte-Carlo simulation approach. Experiments show that our method improves the original one, by decreasing the number of non relevant edges while preserving the true ones.

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“…The a contrario framework was used for image segmentation in [5]. To obtain robust segments, the authors suggest a combination of the a contrario approach and of Monte-Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

Gestaltic Grouping of Line Segments

Rajaei¹,

Gioi²

2018

Image Processing on Line

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Using simple grouping rules in Gestalt theory, one may detect higher level features (geometric structures) in an image from elementary features. By recursive grouping of already detected geometric structures a bottom-up pyramid could be built that extracts increasingly complex geometric features from the input image. Taking advantage of the (recent) advances in reliable line segment detectors, in this paper, we propose three feature detectors along with their corresponding detailed algorithms that constitute one step up in this pyramid. For any digital image, our unsupervised algorithm computes three classic Gestalts from the set of predetected line segments: good continuations, non-local 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. Source CodeThe Matlab source code implementing the proposed algorithms, which is part of this publication, is accessible at corresponding IPOL web page. 1

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Image segmentation by a contrario simulation

Cited by 22 publications

References 20 publications

A Biclustering Framework for Consensus Problems

A Biclustering Framework for Consensus Problems

A contrario edge detection with edgelets

Gestaltic Grouping of Line Segments

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