Image segmentation based on geodesic aided Chan-Vese model

Tran, Thi-Thao; Pham, Van-Truong; Chiu, Yun-Jen; Shyu, Kuo-Kai

doi:10.1109/iccsit.2010.5563751

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…In (18), let ILFI(x) be a local fitted image and IGFI(x) a global fitted image, using a level set ϕ, which are defined as:…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Within this type, geodesic active contour (GAC) [4] is one of the most accurate techniques. GAC meets difficulty when dealing with the objects having blurred or discrete boundaries and it hardly segments the object corrupted by noise [18], although it has been effectively applied for images with high variation in gradient at the contours of the objects.…”

Section: Introductionmentioning

confidence: 99%

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Active contour model for satellitale image combining local and global region based approach

Hayat

Fizazi

2019

IJEECS

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Global and local image information is crucial for accurate segmentation of images with intensity inhomogeneity valuable minute details and multiple objects with various intensities. We propose a region-based active contour model which is able to utilize together local and global image information. The major contribution of this paper is to expand the LIF model which is includes only local image infofmation to a local and global model. The introduction of a new local and global signed pressure force function enables the extraction of accurate local and global image information and extracts multiple objects with several intensities. Several tests performed on some synthetic and real images indicate that our model is effective as well as less sensitivity to the initial contour location and less time compared with the related works.

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“…In (18), let ILFI(x) be a local fitted image and IGFI(x) a global fitted image, using a level set ϕ, which are defined as:…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active contour model for satellitale image combining local and global region based approach

Hayat

Fizazi

2019

IJEECS

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“…is the Heaviside function and SPF(.) is the signed pressure force function defined in (9). Comparing with the level set formulation of the ACM with SBGFRLS, ORACM does not need parameters to be tuned according to input images, which make it parameter free.…”

Section: The Oracm Modelmentioning

confidence: 99%

“…Geodesic Active Contour (GAC) [2] is one of the well-known models in this category. Although GAC has been successfully applied for images with high variation in gradient at the object's boundaries, it meets difficulty when dealing with the objects having blurred or discrete boundaries and it hardly detects the object corrupted by noise [9].…”

Section: Introductionmentioning

confidence: 99%

Fast Region-based Active Contour Model Driven by Local Signed Pressure Force

Abdallah

Elkourd²

2016

ELCVIA

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Intensity inhomogeneity is a well-known problem in image segmentation. In this paper, we present a new region-based active contour model for image segmentation which can handle intensity inhomogeneity problem. This model introduced a new region-based signed pressure force (SPF) function, which uses the local mean values provided by the local binary fitting (LBF) model. In addition, the proposed model utilizes a new regularization operation such as morphological opening and closing to regularize the level set function in the evolution process. Experimental results on synthetic and real images show that the proposed model gives satisfactory segmentation results as well as less sensitivity to the initial contour location and less time consuming compared with the related methods.

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“…Tao et al [28] convert the GACV model into the graph cuts framework, and the GACV model can be effectively minimized by means of graph cuts algorithms. Reference [29] proposed a novel model for intensity inhomogeneous image segmentation, which incorporates the GAC model into the CV model. The proposed model uses the local information of the image to be segmented.…”

Section: Introductionmentioning

confidence: 99%

Image segmentation based on adaptive mixture model

Wang

Fang

2013

J. Opt.

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As an important research field, image segmentation has attracted considerable attention. The classical geodesic active contour (GAC) model tends to produce fake edges in smooth regions, while the Chan–Vese (CV) model cannot effectively detect images with holes and obtain the precise boundary. To address the above issues, this paper proposes an adaptive mixture model synthesizing the GAC model and the CV model by a weight function. According to image characteristics, the proposed model can adaptively adjust the weight function. In this way, the model exploits the advantages of the GAC model in regions with rich textures or edges, while exploiting the advantages of the CV model in smooth local regions. Moreover, the proposed model is extended to vector-valued images. Through experiments, it is verified that the proposed model obtains better results than the traditional models.

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Image segmentation based on geodesic aided Chan-Vese model

Cited by 7 publications

References 17 publications

Active contour model for satellitale image combining local and global region based approach

Active contour model for satellitale image combining local and global region based approach

Fast Region-based Active Contour Model Driven by Local Signed Pressure Force

Image segmentation based on adaptive mixture model

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