Smooth contour coding with minimal description length active grid segmentation techniques

Galland, Frédéric; Jaegler, Arnaud; Allain, Marc; Savéry, David; Réfrégier, Philippe

doi:10.1016/j.patrec.2010.12.007

Cited by 9 publications

(1 citation statement)

References 32 publications

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“…Models involving explicit descriptors and moments based descriptors can be found in [18] and [16] respectively. Related work using polygonal and B-spline curve representations in conjunction with region-based active contour models can be found in [9,11,17,38]. Related work in shape signatures using Fourier descriptors, generalized cone representations, and Legendre moments can be found in [12], [35], and [16] respectively.…”

Section: Introductionmentioning

confidence: 99%

Geodesic Active Contours with Shape Priors for Segmentation, Disocclusion, and Illusory Contour Capture

Householder¹

2020

SIURO

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Image segmentation is the task of finding salient regions of importance in an image. In this work, we take a curve evolution approach to this problem where we deform an initial curve in the inward normal direction with the objective of finding the boundaries of objects in an image. To achieve this, we propose a variational image segmentation model that incorporates a clique based shape signature with a geodesic active contours energy. The model scheme consists of evolving a parametric representation of an active contour to minimize the penalty that the model induces. This penalty is minimized when the curve is on the boundaries of objects in the image, areas with sudden change in pixel intensity i.e. light to dark. We demonstrate successful capture of illusory contours, segmentation of objects in a cluttered background, and segmentation of occluded objects.

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Section: Introductionmentioning

confidence: 99%

Geodesic Active Contours with Shape Priors for Segmentation, Disocclusion, and Illusory Contour Capture

Householder¹

2020

SIURO

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Fast nonparametric active contour adapted to quadratic inhomogeneous intensity fluctuations

Liu¹,

Galland²,

Bertaux³

2014

Pattern Recognition

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Nonparametric MDL segmentation of inhomogeneous images based on Quadratic Local Binary Fitting

Liu

Galland

Bertaux

2014

2014 IEEE International Conference on Image Processing (ICIP)

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Top 10% paperInternational audienceThis paper addresses the problem of two-region noisy image segmentation in the presence of intensity inhomogeneity and of unknown noise fluctuations. For that purpose, the inhomogeneity is modeled as spatial variations of the mean intensity (which are different inside and outside the object) and are estimated using Local Binary Fitting (LBF) approach. In order to be robust to non standard noise phenomena, the intensity fluctuations are then modeled with nonparametric probability density functions (pdf) leading to a new polygonal active contour segmentation technique based on a Minimum Description Length (MDL) criterion which does not require a priori knowledge on the intensity fluctuations and on the inhomogeneity present in the image. Furthermore, it will be shown that in the case of highly inhomogeneous images, the standard LBF approach used to estimate the intensity inhomogeneity can be generalized to Quadratic Local Binary Fitting (QLBF) in order to improve the performance of the proposed segmentation technique

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Smooth contour coding with minimal description length active grid segmentation techniques

Cited by 9 publications

References 32 publications

Geodesic Active Contours with Shape Priors for Segmentation, Disocclusion, and Illusory Contour Capture

Geodesic Active Contours with Shape Priors for Segmentation, Disocclusion, and Illusory Contour Capture

Fast nonparametric active contour adapted to quadratic inhomogeneous intensity fluctuations

Nonparametric MDL segmentation of inhomogeneous images based on Quadratic Local Binary Fitting

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