• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.857-866
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• 2006

Performance of an image retrieval system is usually very low because of the semantic gap between the low level feature and the high level concept in a query image. Semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters. In this paper, we propose a content based image rertrieval approach which combines region based image retrieval and a new relevance feedback method using adaptive clustering together. Our main goal is finding semantically related clusters to narrow down the semantic gap. Our method consists of region based clustering processes and cluster-merging process. All segmented regions of relevant images are organized into semantically related hierarchical clusters, and clusters are merged by finding the number of the latent clusters. This method, in the cluster-merging process, applies r: using v principal components instead of classical Hotelling's $T_v^2$ [1] to find the unknown number of clusters and resolve the singularity problem in high dimensions and demonstrate that there is little difference between the performance of $T^2$ and that of $T_v^2$. Experiments have demonstrated that the proposed approach is effective in improving the performance of an image retrieval system.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.449-452
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• 2003

This paper propose a extracting method of the region for image using segmentation and edge information. First propose algorithm extract information using canny edge detector and the image was divided by watershed segmentation. And it extract the mage with edge information by merging region. Finally we compare the proposed method with levelset method. In the result proposed method not only extract the image with accurate region but also reduce operation time.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.709-721
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• 2009

This paper discusses an efficient image segmentation using morphological watershed algorithm that is robust to noise. Morphological image segmentation consists of four steps: image simplification, computation of gradient image and watershed algorithm and region merging. Conventional watershed segmentation exhibits a serious weakness for over-segmentation of images. In this paper we present a morphological edge detection methods for detecting edges under noisy condition and apply our watershed algorithm to the resulting gradient images and merge regions using Kolmogorov-Smirnov test for eliminating irrelevant regions in the resulting segmented images. Experimental results are analyzed in both qualitative analysis through visual inspection and quantitative analysis with percentage error as well as computational time needed to segment images. The proposed algorithm can efficiently improve segmentation accuracy and significantly reduce the speed of computational time.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.16 no.2
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• pp.187-194
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• 1998

In this study, effective multispectral Landsat TM band combinations for a merging with the high resolution IRS-1C PAN data using the IHS method to improve landuse accuracy is discussed. From the pre-classified image using the merged images with TM all six band images(with the exception of band 6 image) and PAN image, a sample data which has ten classes was generated. An evaluation of the overall classification accuracy for the representative seven merged images which were merged using each TM three-band images and IRS-1C PAN image by IHS method for the sample area. The increase in classification accuracy is most significant with the inclusion of two of TM4, TM5 and TM7 infrared band images. Especially, the largest increase(11.8 percent) in landuse classification accuracy were investigated when Landsat TM247 bands were merged with IRS-1C PAN data. The classification accuracy when TM three band image and PAN image were used without merging is higher than result of the case of using the merged images.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.251-257
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• 2004

The Electro-Optical Camera(EOC) image of the first Korea Multi-Purpose Satellitel(KOMPSAT-1) has both high resolution and convenient acquisition of research data, but on the other hand it has a defect of one band image. Fortunately, the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data are receiving every day at the Korea Aerospace Research Institute (KARI). Therefore, this paper performed an effective merging for survey of reclaimed land using the high-resolution (6.6m) KOMPSAT-1 EOC image and the multispectral MODIS image data. According this paper prepared map of reclaimed lands in Western Coast of North Korea as quantitative(position and form) survey of reclaimed lands of North Korea using merged image. The use of KOPSAT-1 EOC image and MODIS images was found to be economical such using of large scale areas as reclaimed land or according easy to collect information and such north korea as inaccessible areas like as receiving every day.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.181.2-181
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• 2001

Stereo PACS (Picture Archiving and Communication System) is not available yet because of some limitations of medical stereo image software and viewing devices. As a stereo PACS viewer, we designed two functions. One is selecting and viewing a multiplexed stereo image directly, and the other is selecting a stereo pair image (left and right sides both) and merging the stereo pair image into a multiplexed image in software. For the medical image compression of 3-D stereo endoscopic images, we used JPEG and Wavelet compression and to determine an acceptable compression rate using PSNR (Peak Signal-to-Noise Ratio). As a result, we got the conclusion that medically acceptable image compression rate should have the PSNR of above about 40[dB] (JPEG (5:1), Wavelet (10:1)).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.33-42
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• 2005

In this paper, we propose an efficient segmentation algerian using morphological grayscale reconstruction for region-based coding. Each segmentation stage consists of simplification, marker extraction and decision. The simplification removes unnecessary components to make an easier segmentation. The marker extraction finds the flat zones which are the seed points from the simplified image. The decision is to locate the contours of regions detected by the marker extraction. For the simplification, we use a new connected operator based on the size and contrast. In the marker extraction stage, the regions reconstructed to original values we excluded from the candidate marker. For the other regions, the regions which are larger than structuring elements or have higher contrast than a threshold value are selected as markers. For the initial segmentation, the conventional hierarchical watershed algorithm and the extracted markers are used. Finally in the region merging stage, we propose an efficient region merging algorithm which preserves a high quality in terms of the number of regions. At the same time, the pairs which have higher contrast than a threshold are excluded from the region merging stage. Experimental results show that the proposed marker extraction method produces a small number of markers, while maintaining high quality and that the proposed region merging algorithm achieves a good performance in terms of the image quality and the number of regions.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.41-47
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• 2001

In this paper, I propose a detection algorithm that can reliably separate moving objects from noisy background in the image sequence received from a camera at the fixed position. The proposed algorithm consists of four processes: generation of the difference image between the input image and the reference image. multilevel quantization of the difference image, and multistage merging in the quantized image, detection of the moving object using a back propagation in a neural network. The test results show that the proposed algorithm can detect moving objects very effectively in noisy environment.