• Journal of radiological science and technology
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• v.38 no.4
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• pp.421-427
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• 2015

In this study, we have observed the change of the Hounsfield (HU) in the alteration of by changing in size of physical area and setting size of region of interest (ROI) at focus on kVp and mAs. Four-channel multi-detector computed tomography was used to get transverse axial scanning images and HU. Three dimensional printer which is type of fused deposition modeling (FDM) was used to produce the Phantom. The structure of the phantom was designed to be a type of cylinder that contains 33 mm, 24 mm, 19 mm, 16 mm, 9 mm size of circle holes that are symmetrically located. It was charged with mixing iodine contrast agent and distilled water in the holes. The images were gained with changing by 90 kVp, 120 kVp, 140 kVp and 50 mAs, 100 mAs, 150 mAs, respectively. The 'image J' was used to get the HU measurement of gained images of ROI. As a result, it was confirmed that kVp affects to HU more than mAs. And it is suggested that the smaller size of physical area, the more decreasing HU even in material of a uniform density and the smaller setting size of ROI, the more increasing HU. Therefore, it is reason that to set maximum ROI within 5 HU is the best way to minimize in the alteration of by changing in size of physical area and setting size of region of interest.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5344-5356
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• 2018

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.99-106
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• 2017

Electrical impedance tomography is a nondestructive imaging modality in which the internal resistivity distribution is reconstructed based on the injected currents and measured voltages inside a domain of interest. In this paper, an adaptive threshold value based region of interest (ROI) method is proposed to improve the spatial resolution of reconstructed images as well as to reduce the computational time of the inverse problem. Adaptive threshold value is calculated by INTERMODES method and ROI is determined from the domain based on this value. Moreover, the computational domain of image reconstruction is restricted within a ROI and iterative Gauss-Newton method is employed to estimate the resistivity distribution. To evaluate the performance of the proposed method, numerical experiments have been performed and the results are analyzed.

• 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 People, Plants, and Environment
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• v.22 no.2
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• pp.167-180
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• 2019

This study was conducted to investigate effective ways to meet social and cultural interest in and needs of gardens and gardening. A total of 191 respondents who answered they were living in Gyeongnam region in the questionnaire were selected: 102 (53.4%) were males and 89 (46.6%) were females. In frequency of garden visits, 45% of the respondents answered they visited gardens once a year. Their preferred companion was family (43.6%), followed by friends/colleagues (24.3%). Their important motives of garden visits included admiration of gardens' scenery and ambience, pleasure in being outdoors, relaxing mentally and physically, and appreciation of plants. Relatively less important motives included understanding or educating about nature and environmental conservation, and interest in garden design and horticulture techniques. In the overall assessment of gardens and gardening, the quality of the establishment, management and operation of botanic gardens and arboreta in Gyeongnam region scored 3.32 scale, which was close to the level of 'fair.' Also, the respondents agreed at 3.91 scale that it was necessary to improve the garden creation, gardening, and garden culture. Meanwhile, many people in Gyeongnam region did not clearly understand differences between garden and public park, also had a very obscure perception of public garden. The results of importance-performance analysis (IPA) indicated that it is necessary to concentrate on directing and developing some programs such as admiration of beautiful and exotic plants, and education on garden culture including garden making and horticultural techniques.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.810-813
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• 2004

Indexing and Retrieving images from large and varied collections using image content as a key is a challenging and important problem in computer vision application. In this paper, a color Content-based Image Retrieval (CBIR) system using hierarchical Region of Interest (ROI) query and indexing is presented. During indexing process, First, The ROIs on every image in the image database are extracted using a region-based image segmentation technique, The JSEG approach is selected to handle this problem in order to create color-texture regions. Then, Color features in form of histogram and correlogram are then extracted from each segmented regions. Finally, The features are stored in the database as the key to retrieve the relevant images. As in the retrieval system, users are allowed to select ROI directly over the sample or user's submission image and the query process then focuses on the content of the selected ROI in order to find those images containing similar regions from the database. The hierarchical region-of-interest query is performed to retrieve the similar images. Two-level search is exploited in this paper. In the first level, the most important regions, usually the large regions at the center of user's query, are used to retrieve images having similar regions using static search. This ensures that we can retrieve all the images having the most important regions. In the second level, all the remaining regions in user's query are used to search from all the retrieved images obtained from the first level. The experimental results using the indexing technique show good retrieval performance over a variety of image collections, also great reduction in the amount of searching time.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.411-420
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• 2004

We propose a novel still image compression system, which is superior in its function than the JPEG2000 system developed by David Taubman. The system shows 40 : 1 high compression ratio using $2\times2$ bitmatrix subblock coding. The $2\times2$ bitmatrix subblock is constructed in the bitplanes by organizing the bits into subblocks composing of $2\times2$matrices. The arithmetic coding performs the high compression by the bitmatrices in the subblock. The input of the system consists of a segmentation mode and a ROI(Region Of Interest) mode. In segmentation mode, the input image is segmented into a foreground consisting of letters and a background consisting of the remaining region. In ROI mode, the input image is represented by the region of interest window. The high compression ratio shows that the proposed system is competent among the JPEG2000 products currently in the market. This system also uses gray coding to improve the compression ratio.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.275-302
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• 2007

We propose a novel still image compression system which supports a high quality text compression function. The system segments the text from the image and compresses the text with a high quality. The system shows 48:1 high compression ratio using context-based adaptive binary arithmetic coding. The arithmetic coding performs the high compression by the codeblocks in the bitplane. The input of the system consists of a segmentation mode and a ROI(Region Of Interest) mode. In segmentation mode, the input image is segmented into a foreground consisting of text and a background consisting of the remaining region. In ROI mode, the input image is represented by the region of interest window. The high quality text compression function with a high compression ratio shows that the proposed system can be comparable with the JPEG2000 products. This system also uses gray coding to improve the compression ratio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.679-686
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• 2014

Recently, users can get countless images and videos by network. So, the compression technology of image and video is researched more and more. However, the situation which is the interested range of the image is occurred. For instance, since the region of face is more important than background, the image compression technology bases on the region of interest (ROI) is necessary, in the ATM environment. In this research, given the human visual system, which are not sensitive to illumination variations at very dark and light regions of image, we calculate the standard deviation of block and use this value to define the ROI. In encoding process, the relatively high quality can be obtained at the ROI and the relatively low quality can be obtained at the non ROI. In proposed scheme, the feature which is the encoding process according to subjectively image quality can be demonstrated. Finally, this proposed scheme is applied to JPEG standard. The experimental results demonstrate that proposed scheme can achieve better image quality at the high compression ratio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.