A grouping principle and four applications

Desolneux, Agnès; Moisan, Lionel; Morel, Jean‐Michel

doi:10.1109/tpami.2003.1190576

Cited by 136 publications

(121 citation statements)

References 13 publications

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“…As a consequence, the detection is in practice parameter free. We refer the reader to [13,14] for further details.…”

Section: Helmholtz Principlementioning

confidence: 99%

“…A meaningful curve has a small probability to occur in the a contrario model. Our regularity measure is a non increasing function of the curvature (see (14)). Thus, for a meaningful curve, the quantity is a non decreasing function of the curvature.…”

Section: Comparison With Active Contoursmentioning

confidence: 99%

“…It is also known that edge is not a completely local concept and that it does not rely entirely on contrast. Indeed, following Gestalt Theory [20,42], shapes (and thus edges) result from the collaboration of a small set of perceptual laws (called "partial gestalts" by Desolneux, Moisan and Morel [14]), and contrast is only one of them. Among others, we can cite alignments, symmetry, convexity, closedness, good continuation, etc... Other theories, related to edge detection, explicitly use good continuation, which means in this case regularity of curves.…”

Section: Introductionmentioning

confidence: 99%

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Extracting Meaningful Curves from Images

2008

Lecture Notes in Mathematics

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Since the beginning, Mathematical Morphology has proposed to extract shapes from images as connected components of level sets. These methods have proved very efficient in shape recognition and shape analysis. In this paper, we present an improved method to select the most meaningful level lines (boundaries of level sets) from an image. This extraction can be based on statistical arguments, leading to a parameter free algorithm. It permits to roughly extract all pieces of level lines of an image, that coincide with pieces of edges. By this method, the number of encoded level lines is reduced by a factor 100, without any loss of shape contents. In contrast to edge detections algorithm or snakes methods, such a level lines selection method delivers accurate shape elements, without user parameter: no smoothing involved and selection parameters can be computed by Helmholtz Principle.

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“…As a consequence, the detection is in practice parameter free. We refer the reader to [13,14] for further details.…”

Section: Helmholtz Principlementioning

confidence: 99%

Section: Comparison With Active Contoursmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extracting Meaningful Curves from Images

2008

Lecture Notes in Mathematics

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“…Structures are defined by the laws of Gestalt Theory [4], underlining the importance of perceptual grouping for human visual perception. Desolneux et al proposed to apply their statistical framework to detect alignments in images [3], clusters [5], and edges [6]. Since then, the a contrario framework have been used in various purposes, such as motion detection [7], shape recognition [8], object matching [9], and local features matching [10].…”

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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“…It seems that human perception follows this rule to some extent, and this framework has been applied notably to gestalts detection with some success [3,5]. The absence of parameters mainly comes from the fact that no generative probability model of events has to be defined, but only a rarity measure in a well-defined random environment.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Vision Leveraging Digital Retinas: Extracting Meaningful Segments

Burrus¹,

Berthet²

2006

Advanced Concepts for Intelligent Vision Systems

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Abstract. In general, the less probable an event, the more attention we pay to it. Likewise, considering visual perception, it is interesting to regard important image features as those that most depart from randomness. This statistical approach has recently led to the development of adaptive and parameterless algorithms for image analysis. However, they require computer-intensive statistical measurements. Digital retinas, with their massively parallel and collective computing capababilities, seem adapted to such computational tasks. These principles and opportunities are investigated here through a case study: extracting meaningful segments from an image.

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A grouping principle and four applications

Cited by 136 publications

References 13 publications

Extracting Meaningful Curves from Images

Extracting Meaningful Curves from Images

A contrario edge detection with edgelets

Adaptive Vision Leveraging Digital Retinas: Extracting Meaningful Segments

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