• Proceedings of the KSMRM Conference
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• 2002.11a
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• pp.126-126
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• 2002

Purpose： For quantitative analysis of the cardiac diseases, it is necessary to segment the left-ventricle(LV) in MR cardiac images. Snake or active contour model has been used to segment LV boundary. In using these models, however, the contour of the LV may not converge to the desirable one because the contour may fall into local minimum value due to image artifact in inner region of the LV Therefore, in this paper, we propose the new preprocessing method using K-means clustering and merging algorithms that can improve the performance of the active contour model.

• 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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• 1999.11a
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• pp.525-528
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• 1999

Abstract - In this paper, a segmentation method for brain Magnetic Resonance(MR) image using region clustering technique with statistical distribution of gradient image and fuzzy rules is described. The brain MRI consists of gray matter and white matter, cerebrospinal fluid. But due to noise, overlap, vagueness, and various parameters, segmentation of MR image is a very difficult task. We use gradient information rather than intensity directly from the MR images and find appropriate thresholds for region classification using gradient approximation, rayleigh distribution function, region clustering, and merging techniques. And then, we propose the adaptive fuzzy rules in order to extract anatomical structures and diseases from brain MR image data. The experimental results shows that the proposed segmentation algorithm given better performance than traditional segmentation techniques.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.105-113
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• 2007

In this paper, an image segmentation method which is based on edge salience map and region merging is presented. The edge salience map is calculated by combining a texture edge map with a color edge map. The texture edge map is computed over multiple spatial orientations and frequencies by using Gabor filter. A color edge is computed over the H component of the HSI color model. Then the Watershed transformation technique is applied to the edge salience map to and homogeneous regions where the dissimilarity of color and texture distribution is relatively low. The Watershed transformation tends to over-segment images. To merge the over-segmented regions, first of all, morphological operation is applied to the edge salience map to enhance a contrast of it and also to find mark regions. Then the region characteristics, a Gabor texture vector and a mean color, in the segmented regions is defined and regions that have the similar characteristics, are merged. Experimental results have demonstrated the superiority in segmentation results for various images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.2
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• pp.1-10
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• 2003

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.

• 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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.6
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• pp.102-108
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• 2011

The flow characteristics of unventilated dual impinging jets were experimentally investigated. Two nozzles with an aspect ratio of 20 were separated by 6 nozzle widths. The Reynolds number based on nozzle width and nozzle exit velocity was set to 5,000. A Particle Image Velocimetry (PIV) was used to measure turbulent velocity components. It was found that, when an impingement plate was installed in the converging region, there was a stagnation region in the inner area between nozzles. However, when it was installed in the combined region, both jets were merged and collided into the plate, showing single-jet characteristics. In addition, at a dual impinging jet, as the distance between a nozzle and an impingement plate decreased, the spanwise turbulent intensity at the plate increased.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.63-69
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• 2017

The extraction of attention NPC in a FPS game has emerged as a very significant issue. We propose an efficient FPS game operation method, using the attention NPC extraction with a simple arithmetic. First, we define the NPC, using the color histogram interaction and texture similarity in the block to determine the attention NPC. Next, we use the histogram of movement distribution and frequency of movement of the NPC. Becasue, except for the block boundary according to the texture and to extract only the boundaries of the object block. The edge strength is defined to have high values at the NPC object boundaries, while it is designed to have relatively low values at the NPC texture boundaries or in interior of a region. The region merging method also adopts the color histogram intersection technique in order to use color distribution in each region. Through the experiment, we confirmed that NPC has played a crucial role in the FPS game and as a result it draws more speed and strategic actions in the game.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.215-225
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• 2004

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.81-87
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• 2022

In this paper, we propose a watershed algorithm that applies a high-frequency enhancement filter to emphasize the boundary and a local adaptive threshold to search for minimum points. The previous method causes the problem of over-segmentation, and over- segmentation appears around the boundary of the object, creating an inaccurate boundary of the region. The proposed method applies a high-frequency enhancement filter that emphasizes the high-frequency region while preserving the low-frequency region, and performs a minimum point search to consider local characteristics. When merging regions, a fixed threshold is applied. As a result of the experiment, the proposed method reduced the number of segmented regions by about 58% while preserving the boundaries of the regions compared to when high frequency emphasis filters were not used.