• 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 Institute of Electronics Engineers of Korea CI
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• v.41 no.1
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• pp.38-46
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• 2004

Image segmentation is the task which partitions the image into meaningful regions and considered to be one of the most important steps in computer vision and image processing. Image segmentation is also widely used in object-based video compression such as MPEG-4 to extract out the object regions from the given frame. Watershed algorithm is frequently used to obtain the more accurate region boundaries. But, it is well known that the watershed algorithm is extremely sensitive to gradient noise and usually results in oversegmentation. To solve such a problem, we propose an image segmentation method which is robust to noise by using stabilized inverse diffusion equation (SIDE) and is more efficient in segmentation by employing multiresolution approach. In this paper, we apply both the region projection method using labels of adjacent regions and the region merging method based on region adjacency graph (RAG). Experimental results on noisy image show that the oversegmenation is reduced and segmentation efficiency is increased.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.421-430
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• 2010

Image segmentation technique is becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification to extract object regions of interest within images. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Improved Seeded Region Growing (ISRG) and Region merging. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained multi-spectral edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying ISRG to consider spectral and edge information. Finally the region merging process, integrating region texture and spectral information, was carried out to get the final segmentation result. The accuracy assesment was done using the unsupervised objective evaluation method for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2876-2884
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• 2000

Clausal segmentation is the method that parses Korean sentences by segmenting one long sentence into several phrases according to the predicates. So far most of researches could be useful for literary sentences, but long sentences increase complexities of the syntax analysis. Thus this paper proposed Two-Level Clausal Segmentation using sense information which was designed and implemented to solve this problem. Analysis of clausal segmentation and understanding of word senses can reduce syntactic and semantic ambiguity. Clausal segmentation using Sense Information is necessary because there are structural ambiguity of sentences and a frequent abbreviation of auxiliary word in common sentences. Two-Level Clausal Segmentation System(TLCSS) consists of Complement Selection Process(CSP) and Noncomplement Expansion Process(NEP). CSP matches sentence elements to subcategorization dictionary and noun thesaurus. As a result of this step, we can find the complement and subcategorization pattern. Secondly, NEP is the method that uses syntactic property and the others methods for noncomplement increase of growth. As a result of this step, we acquire segmented sentences. We present a technique to estimate the precision of Two-Level Clausal Segmentation System, and shows a result of Clausal Segmentation with 25,000 manually sense tagged corpus constructed by ETRl-KONAN group. An Two-Level Clausal Segmentation System shows clausal segmentation precision of 91.8%.

• Smart Structures and Systems
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• v.29 no.1
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1075-1085
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• 2012

In clinical research using medical images, the image segmentation is one of the most important processes. Especially, the hippocampal atrophy is helpful for the clinical Alzheimer diagnosis as a specific marker of the progress of Alzheimer. In order to measure hippocampus volume exactly, segmentation of the hippocampus is essential. However, the hippocampus has some features like relatively low contrast, low signal-to-noise ratio, discreted boundary in MRI images, and these features make it difficult to segment hippocampus. To solve this problem, firstly, We selected region of interest from an experiment image, subtracted a original image from the negative image of the original image, enhanced contrast, and applied anisotropic diffusion filtering and gaussian filtering as preprocessing. Finally, We performed an image segmentation using two level set methods. Through a variety of approaches for the validation of proposed hippocampus segmentation method, We confirmed that our proposed method improved the rate and accuracy of the segmentation. Consequently, the proposed method is suitable for segmentation of the area which has similar features with the hippocampus. We believe that our method has great potential if successfully combined with other research findings.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.7
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• pp.566-577
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• 1991

In this paper, the method of image segmentation based on a pyramid of reduced resolution versions of the input input image is persented. In a pyramid structure, two regions (a given pixel and its mother pixels) are compared by the proposed inverse-mother-son variance ratio (IMSVR) method for the detection of an optinal object pixel and are determined whether they are similar enough to be viewed as one region or disparate to be viewed as ditinct regions By the proposed method, an l`timal object pixel has been setectedat some level, it is necessary to retrieve its boundary precisely. Moving down the pyramid to levels of higher resolution is requires. In this paper, the top-sown pyramid traversing algorithm for an image segmentation using a pyrmid structure is presented. Using the computer simulation, the results by the proposed statistical method and object traversing method are investigated for the binary image and the real image at the results of computer simulation, the proposed method of image segmentation based on a pyramid structure seem to have useful properties and deserve consideration as a possible alternative to existing methods of omage segmentation. The computation for the proposed method is required 0 (log n), for an TEX>$n{\times}n$ input image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.179-184
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• 2004

This paper presents an image segmentation algorithm to obtain the 3D body shape data that the grid pattern and the body contour lute in the background image are extracted using the new proposed hybrid method. The body contour line is extracted based on maximum biased anisotropic recognition(MaxBAR) algorithm which recognizes the most strong and robust edges in the image since the normal derivative at the edges is large, while the tangential derivatives can be small. The grid patterns within body contour lines are extracted by grid pattern detection (GPD). The body contour lilies and the grid patterns are combined. The consecutive run test based on heuristic method is used to link the disconnected line and reduce noise line. This proposed segmentation method is more effective than the conventional method which uses a gradient and a laplacian operator, verified with application two conventional method.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.31-37
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• 2015

Symmetry is easily found in animals and plants as well as in artificial structures. It is useful not only for human cognitive process but also for image understanding by computer. Application areas include face detection and recognition, indexing of image database, image segmentation and detection, and analysis of medical images. The method used in this paper extracts edges, and the perpendicular bisector of any pair of selected edge points is considered to be a candidate axis of symmetry. The coefficients of the perpendicular bisectors are accumulated in the coefficient space. Axis of symmetry is determined to be the line for which the histogram has maximum value. This method shows good results, but the usefulness of the method is restricted because the amount of computation increases proportional to the square of the number of edges. In this paper, an acceleration method is proposed which performs $2^{2n}$ times faster than the original one. Experiment on 20 test images shows that the proposed method using level-3 image segmentation performs 63.9 times faster than the original method.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.189-194
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• 2017

In this study, we propose a method which is based on "Image segmentation by histogram thresholding using hierarchical cluster analysis"/HCA/ and "A nonparametric approach for histogram segmentation"/NHS/. HCA method uses that all histogram bins are one cluster then it reduces cluster numbers by using distance metric. Because this method has too many clusters, it is more computation. In order to eliminate disadvantages of "HCA" method, we used "NHS" method. NHS method finds all local minima of histogram. To reduce cluster number, we use NHS method which is fast. In our approach, we combine those two methods to eliminate disadvantages of Arifin method. The proposed method is not only less computational than "HCA" method because combined method has few clusters but also it uses local minima of histogram which is computed by "NHS".