• Journal of Information Processing Systems
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• 제15권1호
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• pp.7-21
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• 2019

In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence of the initial contour on the level set method, a saliency level set image segmentation model based on local Renyi entropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detection algorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energy and edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. At the same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, the local binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrast experiments are implemented in different image database. The robustness of the proposed model for segmentation of images with intensity inhomogeneity and complicated edges is verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1760-1778
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• 2018

For image segmentation with intensity inhomogeneity, many region-based level set methods have been proposed. Some of them however can't get the relatively ideal segmentation results under the severe intensity inhomogeneity and weak edges, and without use of the image gradient information. To improve that, we propose a new level set method combined with local direction gradient in this paper. Firstly, based on two assumptions on intensity inhomogeneity to images, the relationships between segmentation objects and image gradients to local minimum and maximum around a pixel are presented, from which a new pixel classification method based on weight of Euclidian distance is introduced. Secondly, to implement the model, variational level set method combined with image spatial neighborhood information is used, which enhances the anti-noise capacity of the proposed gradient information based model. Thirdly, a new diffusion process with an edge indicator function is incorporated into the level set function to classify the pixels in homogeneous regions of the same segmentation object, and also to make the proposed method more insensitive to initial contours and stable numerical implementation. To verify our proposed method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.827-833
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• 2015

In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제21권2호
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• pp.63-73
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• 2017

Segmenting the image into multiple regions is at the core of image processing. Many segmentation formulations of an images with multiple regions have been suggested over the years. We consider segmentation algorithm based on the multi-phase level set method in this work. Proposed method gives the best result upon other methods found in the references. Moreover it can segment images with intensity inhomogeneity and have multiple junction. We extend our method (GLIF) in [T. Dultuya, and M. Kang, Segmentation with shape prior using global and local image fitting energy, J.KSIAM Vol.18, No.3, 225-244, 2014.] using a multiphase level set formulation to segment images with multiple regions and junction. We test our method on different images and compare the method to other existing methods.

• Journal of Ship and Ocean Technology
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• 제6권2호
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• pp.37-50
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• 2002

In the present numerical simulation of viscous free surface flow around a ship, two-fluids in-compressible Reynolds-averaged Navier-Stokes equations with the standard $\textsc{k}-\varepsilon$turbulence model are discretized on a regular grid by using a finite volume method. A local level-set method is introduced for capturing the free surface movement and the influence of the viscous layer and dynamic boundary condition of the free surface are implicitly considered. Partial differential equations in the level-set method are discretized with second order ENO scheme and explicit Euler scheme in the space and time integration, respectively. The computational results for the Series-60 model with $C_B=0.6$ show a good agreement with the experimental data, but more validation studies for commercial complicated hull forms are necessary.

• 한국정보통신학회논문지
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• 제8권7호
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• pp.1453-1463
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• 2004

기존의 레벨셋을 이용한 이미지 분할 방법은 화소값의 기울기를 이용하기 때문에 지역적 형태에 좌우되는 문제점을 지니고 있다. 본 논문에서는 평활한 구동력을 위하여 레벨 셋 함수와 새로운 보상 평활화 함수를 결합시키는 하이브리드 방법을 이용한 방법이 소개된다. 대부분의 경우에 3 교점을 가지고 있지 않다는 가정하에 보상함수를 얻는 방법을 대안으로 고려하였다. 보상함수의 주요 역할은 원보상 함수와 평균 보상함수의 차가 새로운 레벨셋 함수의 합리적인 구동력으로 소개될 수 있다. 본 논문에서 제안한 하이브리드 방법은 기존 레벨셋을 이용한 방법의 단점을 최소화시키는 방법이다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제18권3호
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• pp.225-244
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• 2014

In this work, we discuss segmentation algorithms based on the level set method that incorporates shape prior knowledge. Fundamental segmentation models fail to segment desirable objects from a background when the objects are occluded by others or missing parts of their whole. To overcome these difficulties, we incorporate shape prior knowledge into a new segmentation energy that, uses global and local image information to construct the energy functional. This method improves upon other methods found in the literature and segments images with intensity inhomogeneity, even when images have missing or misleading information due to occlusions, noise, or low-contrast. We consider the case when the shape prior is placed exactly at the locations of the desired objects and the case when the shape prior is placed at arbitrary locations. We test our methods on various images and compare them to other existing methods. Experimental results show that our methods are not only accurate and computationally efficient, but faster than existing methods as well.

• Journal of Electrical Engineering and Technology
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• 제7권6호
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• pp.1001-1008
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• 2012

Non-destructive testing (NDT) is a technique used in science and industry to evaluate the properties of a material without causing damage. In this paper we propose a method for segmenting radiographic images of welding in order to extract the welding defects which may occur during the welding process. We study different methods of level set and choose the model adapted to our application. The methods presented here take the property of local segmentation geodesic active contours and have the ability to change the topology automatically. The computation time is considerably reduced after taking into account a new level set function which eliminates the re-initialization procedure. Satisfactory results are obtained after applying this algorithm both on synthetic and real images.

• 한국정보과학회논문지:시스템및이론
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• 제34권12호
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• pp.641-655
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• 2007

컴퓨터 그래픽스를 비롯한 여러 응용 분야에서 3차원 다면체 모델에 대한 매개 변수화(parameterization) 기법이 중요하게 연구되어왔다. 매개 변수화에 대한 연구는 주로 3차원 다면체 모델을 2차원 평면으로 매핑 시켜주는 문제를 고려하는데, 이러한 매핑 과정에서 종종 다각형의 세밀한 형태를 제대로 표현하지 못하거나, 텍스처 매핑 등의 기법을 적용할 때 일부 왜곡이 발생하는 문제가 발생하고는 한다. 이러한 문제점을 해결하기 위해서 여러 가지 왜곡 처리 방법이 연구되었지만, 3차원 물체의 임의 영역에 대한 사각형 형태의 부분 매개 변수화(local parameterization)를 수행하기에는 종종 한계점을 가지고 있었다. 본 논문에서는 투영 등위 집합이라고 하는 수학적 도구를 사용하여 3차원 다면체 모델의 특정 지역을 효과적으로 매개 변수화 해주는 기법을 제안한다. 이 방법에서는 사용자가 지정한 임의의 영역에 대해 등간격의 곡선을 생성한 후, 이를 이용하여 사각형 형태의 영역에 대한 부분 매개 변수화 정보를 추출하는 방식을 취한다. 본 논문에서는 새로운 부분 매개 변수화 기법에 대하여 자세히 설명한 후 실험 결과를 기술하도록 한다.

• 한국통신학회논문지
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• 제21권12호
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• pp.3033-3045
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• 1996

In this paper, we propse an object-oriented coding method in low bit-rate channels using pyramid structure and residual image compensation. In the motion estimation step, global motion is estimated using a set of multiresolution images constructed in a pyramid structure. We split an input image into two regions based on the gradient value. Regions with larte motions obtain observation points at low resolution level to guarantee robustness to noise and to satisfy a motion constraint equation whereas regions with local motions such as eye, and lips get observation points at the original resolution level. Local motion variations and intesity variations of an image reconstructed by the golbal motion are compensated additionally by using the previous residual image component. Finally, the model failure (MF) region is compensated by the pyramid mapping of the previous displaced frame difference (DFD). Computer simulation results show that the proposed method gives better performance that the convnetional one in terms of the peak signal to noise ratio (PSNR), compression ratio (CR), and computational complexity.