Object segmentation using graph cuts based active contours

Xu, Ning; Ahuja, Narendra; Bansal, Ravi

doi:10.1016/j.cviu.2006.11.004

Cited by 194 publications

(155 citation statements)

References 18 publications

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“…The final boundary should produce the inner and outer boundaries. Let a connected graph G = (N, A) be a connected graph (Xu et al, 2003) with vertex set N and edge set A. Each arc (u, v) ∈ A has a postive capacity c(u, v) ≥ 0.…”

Section: Methodsmentioning

confidence: 99%

“…Also the boundary of each region is a possible cut boundary whose cut measure is also computed an assigned to the corresponding pixel on the fly. Xu et al (2003) prosposed a method which is a combination of active contours and the optimization tool of graph cuts and differs fundamentally from traditional active contours in that it uses graph cuts to iteratively deform the contour. They easily extends to the segmentation of three and higher dimensional objects.…”

Section: Introductionmentioning

confidence: 99%

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An Alternative Graph Cut Algorithm for Morphological Edge Detection

Radhakrishnan¹

2012

American Journal of Applied Sciences

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Problem statement: Image analysis has some basic importance of edge detection. Characterize the object boundaries are helpful for object segmentation, registration and identification in a scene. There are many methods for edge detection, but it can be identified by two major methods, search-based and zero-crossing based. Approach: Proposed method tries to follow searching the edge points by applying graph cuts in the place of morphological approach. The graph cut edge detection algorithm is very effective to detect edges with minimum searches. Results: The new approach produces connectives edge boundary with recursive approach in short duration comparatively with morphological approach. It is obtained by identifying the edge points with cut measure within its boundary. Conclusion: The graph cut algorithm detect boundary for any shapes with normal structure, this identification of edges in a complex background is always required a lot of user inputs. In each level only the local neighboring boundaries to be separated with labels. It is optimized approach for unknown region boundaries in complex basins.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Alternative Graph Cut Algorithm for Morphological Edge Detection

Radhakrishnan¹

2012

American Journal of Applied Sciences

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“…The main procedures of the proposed algorithm are summarized as follows: 1) Initialize the level set function Φ as 00 0 0 ( , 0) 0 This step has the local segmentation property. If we want to selectively segment the desired objects, this step is necessary; otherwise, it is unnecessary.…”

Section: B Implementationmentioning

confidence: 99%

“…Extensive study has been made and many techniques have been proposed [1], among which the active contour model [ACM] [2]- [3] is one of the most successful methods. The basic idea of active contour model is to evolve a curve under some constraints to extract the desired object.…”

Section: Introductionmentioning

confidence: 99%

Global Region Based Segmentation of Satellite and Medical Imagery with Active Contours and Level Set Evolution on Noisy Images

Ramudu¹,

Reddy²,

Srinivas³

et al. 2013

IJAPM

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Abstract-In this paper, we proposed a novel global segmentation method for satellite images with active contour model on noisy images with ten percentage of salt and pepper. It was implemented with a special technique selective binary and Gaussian filtering regularized level set evolution. First we selectively penalize the level set function to be binary and then use a Gaussian smoothing kernel to regularize it. The advantages of our method is a new region based signed pressure force(SPF) function is proposed, which can step effectively the contour at weak or blurred edges and automatically detect the interior and exterior boundaries with the initial contour being anywhere in the images effected with noise. The proposed method can implement by the simple finite difference scheme. Experiments on satellite images with noise demonstrate the advantages of the proposed method over the Chan-vase (CV) active contour in terms of the number of Iterations.Index Terms-Active contours, image segmentation, Chan-vase model, level set method.

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“…Methods for object extraction [6,5,30,41] take full advantage of topological freedom of graph-cut based hypersurfaces. In particular, they allow to segment objects of arbitrary topology.…”

Section: Applications Of Graph Cuts As Hypersurfacesmentioning

confidence: 99%

Graph Cuts in Vision and Graphics: Theories and Applications

Boykov¹,

Veksler²

Handbook of Mathematical Models in Computer Vision

143

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Abstract. Combinatorial min-cut algorithms on graphs emerged as an increasingly useful tool for problems in vision. Typically, the use of graphcuts is motivated by one of the following two reasons. Firstly, graph-cuts allow geometric interpretation; under certain conditions a cut on a graph can be seen as a hypersurface in N-D space embedding the corresponding graph. Thus, many applications in vision and graphics use min-cut algorithms as a tool for computing optimal hypersurfaces. Secondly, graphcuts also work as a powerful energy minimization tool for a fairly wide class of binary and non-binary energies that frequently occur in early vision. In some cases graph cuts produce globally optimal solutions. More generally, there are iterative graph-cut based techniques that produce provably good approximations which (were empirically shown to) correspond to high-quality solutions in practice. Thus, another large group of applications use graph-cuts as an optimization technique for low-level vision problems based on global energy formulations. This chapter is intended as a tutorial illustrating these two aspects of graph-cuts in the context of problems in computer vision and graphics. We explain general theoretical properties that motivate the use of graph cuts, as well as, show their limitations.

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Object segmentation using graph cuts based active contours

Cited by 194 publications

References 18 publications

An Alternative Graph Cut Algorithm for Morphological Edge Detection

An Alternative Graph Cut Algorithm for Morphological Edge Detection

Global Region Based Segmentation of Satellite and Medical Imagery with Active Contours and Level Set Evolution on Noisy Images

Graph Cuts in Vision and Graphics: Theories and Applications

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