• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.303-311
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• 2003

Since the MPEG-4 visual standard enables content-based functionalities, it is necessary to extract video object from natural video sequences. Segmentation algorithms can largely be classified into automatic segmentation and user-assisted segmentation. In this paper, we propose a user-assisted VOP generation method based on the geometric active contour. Since the geometric active contour, unlike the parametric active contour, employs the level set method to evolve the curve, we can draw the initial curve independent of the shape of the object. In order to generate the edge function from a smoothed image, we propose a vector-valued diffusion process in the LUV color space. We also present a discrete 3-D diffusion model for easy implementation. By combining the curve shrinkage in the vector field space with the curve expansion in the empty vector space, we can make accurate extraction of visual objects from video sequences.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.4
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• pp.247-256
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• 2009

In this paper, a motion detection algorithm which works well in low illumination environment is proposed. By using the level set based bimodal motion segmentation, the algorithm obtains an automatic segmentation of the motion region and the spurious regions due to the large CCD noise in low illumination environment are removed effectively.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.53-62
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• 2012

Object segmentation is a demanding research area and not a trivial problem of image processing and computer vision. Tremendous segmentation algorithms were addressed on gray-scale (or biomedical) images that rely on numerous image features as well as their strategies. These works in practice cannot apply to natural color images because of their negative effects to color values due to the use of gray-scale gradient information. In this paper, we proposed a new approach for color object segmentation by modifying a geometric active contour model named distance regularized level set evolution (DRLSE). Its speed function will be designed to exploit as much as possible color gradient information of images. Finally, we provide experiments to show performance of our method with respect to its accuracy and time efficiency using various color images.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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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.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.660-667
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• 2006

This paper presents a novel region-based snake method for vessel segmentation. According to geometric shape analysis of the vessel structure with different scale, an efficient statistical estimation of vessel branches is introduced into the energy objective function, which applies not only the vessel intensity information, but also geometric information of line-like structure in the image. The defined energy function is minimized using the gradient descent method and a new region-based speed function is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. The narrow band algorithm in the level set framework implements the proposed method, the solution of which is steady. The segmentation experiments are shown on several images. Compared with other geometric active contour models, the proposed method is more efficient and robust.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.335-345
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• 2011

In this paper, we propose a new hybrid speed function for image segmentation using level set. A new proposed speed function uses the region and boundary information of image object for the exact result of segmentation. The region information is defined by the probability information of pixel intensity in a ROI(region-of-interest), and the boundary information is defined by the gradient vector flow obtained from the gradient of image. We show the results of experiment for an various artificial image and real medical image to verify the accuracy of segmentation using proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.668-671
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• 2010

In this paper, we present a novel automatic algorithm for scalp and skull segmentation in T1-weighted head MR images. First, the scalp and skull part are constructed by using intensity threshold. Second, the scalp outer surface is extracted based on an active level set method. Third, the skull inner surface is extracted using a canny edge detection algorithm. Finally, the fast sweeping, tagging and level set methods are applied to reconstruct surfaces from the detected points in three-dimensional space. The results of the new segmentation algorithm on MRI data acquired from eight persons were compared with manual segmented data. The average similarity indices for the scalp and skull segmented regions were equal to 84.42% for the test data.

• Speech Sciences
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• v.13 no.3
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• pp.155-167
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• 2006

Hearing impairment children possess poor underlying perceptual knowledge of the sound system and show delayed development of segmental organization of that system. The purpose of this study was to investigate the relationship between phonological awareness ability and word identification ability in hearing impaired children. 14 children with moderately severe hearing loss participated in this study. All tasks were individually administered. Phonological awareness tests consisted of syllable blending, syllable segmentation, syllable deletion, body-coda discrimination, phoneme blending, phoneme segmentation and phoneme deletion. Close-set Monosyllabic Words(12 items) and lists 1 and 2 of open-set Monosyllabic Words in EARS-K were examined for word identification. Results of this study were as follows: First, from the phonological awareness task, the close-set word identification showed a high positive correlation with the coda discrimination, phoneme blending and phoneme deletion. The open-set word identification showed a high positive correlation with phoneme blending, phoneme deletion and phoneme segmentation. Second, from the level of phonological awareness, the close-set word identification showed a high positive correlation with the level of body-coda awareness and phoneme awareness while the open-set word identification showed a high positive correlation only with the level of phoneme awareness.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.3
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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 the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1453-1463
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• 2004

The conventional image segmentation method using level set has been disadvantage since level set function in the gradient-based model evolves depending on the local profile of the edge. In this paper, a new model is introduced by hybridizing level set formulation and complementary smooth function in order to smooth the driving force. We consider an alternative way of getting the complementary function(CF) which is much easier to simulate and makes sense for most cases having no triple junctions. The rule of thumb is that CF must be computed such that the difference between their average and the original CF function should be able to introduce a reliable driving force for the evolution of the level set function. This proposed hybrid method tries to minimize drawbacks the conventional level set method.