• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.209-210
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• 2007

Obtained disparity map from the stereo camera by using the several stereo matching algorithms carries lots of noise because of various causes. In our approach, mode filtering and noise elimination technique using the histogram and projection-based region merging methods are adopted for improving the quality of disparity map and image segmentation. The proposed algorithms are implemented in VHDL and the real-time experimentation shows the accurately divided objects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.104-108
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• 1999

It is presented many effect that represented by implicated one image more than each images with a fragment meaning. ‘Mosaic’ is technique be applied at this situation. ‘Mosaic’ is created by complicated one new image which by multi image be eliminated overlap region. This research is development for mosaic system by multi image. The system is divided that shot segmentation and mosaic image creation. Shot segmentation divided that merge with respect to similarity images which video data of moving picture in sequence time and mosaic image creation is composed of one image with which all frames in segmented shot.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.10a
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• pp.7-10
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• 1987

• Proceedings of the KSRS Conference
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• v.1
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• pp.126-129
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• 2006

The marine environment is under considerable threat from intentional or accidental oil spills, ballast water discharged, dredging and infilling for coastal development, and uncontrolled sewage and industrial wastewater discharges. Monitoring spills and illegal oil discharges is an important component in ensuring compliance with marine protection legislation and general protection of the coastal environments. For the monitoring task an image processing system is needed that can efficiently perform the detection and the tracking of oil spills and in this direction a significant amount of research work has taken place mainly with the use of radar (SAR) remote sensing data. In this paper the level set image segmentation technique was tested for the detection of oil spills. Level set allow the evolving curve to change topology (break and merge) and therefore boundaries of particularly intricate shapes can be extracted. Experimental results demonstrated that the level set segmentation can be used for the efficient detection and monitoring of oil spills, since the method coped with abrupt shape’s deformations and splits.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.333-342
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• 2022

In this paper, we perform a direct comparison study of real experimental data for domain rearrangement and the Cahn-Hilliard (CH) equation on the dynamics of morphological evolution. To validate a mathematical model for physical phenomena, we take initial conditions from experimental images by using an image segmentation technique. The image segmentation algorithm is based on the Mumford-Shah functional and the Allen-Cahn (AC) equation. The segmented phase-field profile is similar to the solution of the CH equation, that is, it has hyperbolic tangent profile across interfacial transition region. We use unconditionally stable schemes to solve the governing equations. As a test problem, we take domain rearrangement of lipid bilayers. Numerical results demonstrate that comparison of the evolutions with experimental data is a good benchmark test for validating a mathematical model.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.1-8
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• 2021

Prostate cancer is one of the most diagnosed malignancies found across the world today. American cancer society in recent research predicted that over 174,600 new prostate cancer cases found and nearly 31,620 death cases recorded. Researchers are developing modest and accurate methodologies to detect and diagnose prostate cancer. Recent work has been done in radiology to detect prostate tumors using ultrasound imaging and resonance imaging techniques. Transrectal ultrasound and Magnetic resonance images of the prostate gland help in the detection of cancer in the prostate gland. The proposed paper is based on comparison and analysis between two novel image segmentation approaches. Seed region growing and cluster based image segmentation is used to extract the region from trans-rectal ultrasound prostate and MR prostate images. The region of extraction represents the abnormality area that presents in men's prostate gland. Detection of such abnormalities in the prostate gland helps in the identification and treatment of prostate cancer

• Journal of Advanced Navigation Technology
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• v.10 no.1
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• pp.48-56
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• 2006

This paper proposes an efficient preprocessing technique for a binary centroid tracker in correlated image sequences. It is known that the following factors determine the performance of the binary centroid target tracker: (1) an efficient real-time preprocessing technique, (2) an exact target segmentation from cluttered background images and (3) an intelligent tracking window sizing, and etc. The proposed centroid tracker consists of an adaptive segmentation method based on novel distance features and an efficient real-time preprocessing technique in order to enhance the distinction between the objects of interest and their local background. Various tracking experiments using synthetic images as well as real Forward-Looking InfraRed (FLIR) images are performed to show the usefulness of the proposed methods.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1003-1017
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• 2017

Multispectral image data of high spatial resolution is required to obtain correct information on the ground surface. The multispectral image data has lower resolution compared to panchromatic data. PAN-sharpening fusion technique produces the multispectral data with higher resolution of panchromatic image. Recently the object-based approach is more applied to the high spatial resolution data than the conventional pixel-based one. For the object-based image analysis, it is necessary to perform image segmentation that produces the objects of pixel group. Image segmentation can be effectively achieved by the process merging step-by-step two neighboring regions in RAG (Regional Adjacency Graph). In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. This degradation increases variation of pixel values in same area, and results in deteriorating the accuracy of image segmentation. An iterative approach that reduces the difference of pixel values in two neighboring pixels of same area is employed to alleviate variation of pixel values in same area. The size of segmented regions is associated with the quality of image segmentation and is decided by a stopping rue in the merging process. In this study, the image restoration and segmentation was quantitatively evaluated using simulation data and was also applied to the three PAN-sharpened multispectral images of high resolution: Dubaisat-2 data of 1m panchromatic resolution from LA, USA and KOMPSAT3 data of 0.7m panchromatic resolution from Daejeon and Chungcheongnam-do in the Korean peninsula. The experimental results imply that the proposed method can improve analytical accuracy in the application of remote sensing high resolution PAN-sharpened multispectral imagery.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.1031-1036
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• 1999

A Hierarchical vector Quantization scheme is implemented and an optimal thresholding technique of quadtree segmentation for performing high quality low bit rate image compression is proposes. A mathematical model is constructed under the assumption that the standard deviations of sub-blocks are larger than or equal to the standard deviation of the upper level block which is generated by merging of sub-blocks. This thresholding technique based on the mathematical modeling allows producing about 1 dB improved performance in terms of PSNR at most ranges of bit rates over the quadtree coder, which is based on MSE for quadtree segmentation.