Affine-invariant geometric shape priors for region-based active contours

Foulonneau, Alban; Charbonnier, Pierre; Heitz, Fabrice

doi:10.1109/tpami.2006.154

Cited by 73 publications

(69 citation statements)

References 14 publications

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“…The set of Legendre polynomials provide a good platform for such representation, since the basis functions are smoothly changing themselves. Legendre polynomials have got a great application in certain aspects of image processing in designing illumination models for object tracking [17] and generating shape signatures for supervised segmentation [18]. Using Legendre polynomials model intensity based appearance allows the estimating functions to vary spatially, but at the same time variation is constrained by the inherent smoothness of the polynomials.…”

Section: B Choice Of Basis Functionmentioning

confidence: 99%

Region Based Segmentation in Presence of Significant Intensity Inhomogeneity using Legendre Polynomials

Rangaswamy¹

2017

IJRASET

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we propose a region based segmentation method which is capable of segmenting objects in presence of significant intensity inhomogeneity. Present method use some form of local processing to tackle intra region inhomogeneity, which further makes such methods susceptible to local minima. Here we present a framework which generalizes the traditional Chan-Vese algorithm. In contrast with existing local techniques, we represent the illumination of the regions of interest in a lower dimensional subspace using set of pre-specified basis functions. This representation can accommodate heterogeneous objects, even in presence of noise. We compare our results with several techniques like biological/biomedical images with tubular or filamentous structures. As the, we achieve a 44% increase in performance, which demonstrates efficacy of the method. I. INTRODUCTION Active contours [1]-[4]are popular for image segmentation. Active contours have ability to elastically deform and delineate object boundaries with sub-pixel accuracy. Furthermore, the energy optimization framework, which serves as the basic platform for most active contour based techniques, can be handle easily to introduce ad-additional constraints based on shape, appearance etc. to assist segmentation. Geometric active contours are used when the application requires the propagating curves to be able to adapt to the varying topology of the underlying object by automated splitting or merging. The geometric active contours may be divided in two types. Edge based techniques and region based techniques. Edge based techniques [2], [5], [6] perform curve evolution geometrically, with the stopping criteria controlled by edge dependent features extracted from the image. However, in many applications where the edge information is unreliable, region based techniques are used. Chan and Vese [3] proposed a level set formulation to minimize the Mumford Shah functional [7] for segmentation. The Chan-Vese frame-work models the image as a set of constant illumination regions and performs a two-class segmentation by computing the optimal partition which hereby satisfies the constant illumination constraint. The authors also propose a multi-phase variant [8] of their approach to perform multi-class grouping.

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Section: B Choice Of Basis Functionmentioning

confidence: 99%

Region Based Segmentation in Presence of Significant Intensity Inhomogeneity using Legendre Polynomials

Rangaswamy¹

2017

IJRASET

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“…While it was shown that segmentation results can be substantially improved by imposing shape priors [10,5,8], existing approaches typically suffer from the following problems:…”

Section: Shape Priors For Image Segmentationmentioning

confidence: 99%

“…In particular, the lower-order moments allow to constrain the area/volume, the centroid and the size or covariance of objects without imposing any constraints on their local shape. A related idea of using Legendre moments (albeit in a local optimization scheme) was developed in [8].…”

Section: Shape Priors For Image Segmentationmentioning

confidence: 99%

Moment Constraints in Convex Optimization for Segmentation and Tracking

Klodt¹,

Steinbrücker²,

Cremers³

2013

Advanced Topics in Computer Vision

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Convex relaxation techniques have become a popular approach to shape optimization as they allow to compute solutions independent of initialization to a variety of problems. In this chapter, we will show that shape priors in terms of moment constraints can be imposed within the convex optimization framework, since they give rise to convex constraints. In particular, the lower-order moments correspond to the overall area, the centroid, and the variance or covariance of the shape and can be easily imposed in interactive segmentation methods. Respective constraints can be imposed as hard constraints or soft constraints. Quantitative segmentation studies on a variety of images demonstrate that the user can impose such constraints with a few mouse clicks, leading to substantial improvements of the resulting segmentation, and reducing the average segmentation error from 12% to 0.35%. GPU-based computation times of around 1 second allow for interactive segmentation.

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“…Shape descriptors have been recently used to add prior information to region-based active contours. Foulonneaux et al [10] use Legendre moments to make the contour evolve to a shape of reference. It introduces a quadratic distance between the contour and the object of reference.…”

Section: Introductionmentioning

confidence: 99%

Fourier-based geometric shape prior for snakes

Charmi

Derrode

Ghorbel

2008

Pattern Recognition Letters

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A novel method of snakes with shape prior is presented in this paper. We propose to add a new force which makes the curve evolve to particular shape corresponding to a template to overcome some well-known problems of snakes. The template is an instance or a sketch of the researched contour without knowing its exact geometric pose in the image. The prior information is introduced through a set of complete and locally stable invariants to Euclidean transformations (translation, rotation and scale factor) computed using Fourier Transform on contours. The method is evaluated with the segmentation of myocardial scintigraphy slices and the tracking of an object in a video sequence.

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Affine-invariant geometric shape priors for region-based active contours

Cited by 73 publications

References 14 publications

Region Based Segmentation in Presence of Significant Intensity Inhomogeneity using Legendre Polynomials

Region Based Segmentation in Presence of Significant Intensity Inhomogeneity using Legendre Polynomials

Moment Constraints in Convex Optimization for Segmentation and Tracking

Fourier-based geometric shape prior for snakes

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