• Asian-Australasian Journal of Animal Sciences
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• v.25 no.11
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• pp.1521-1528
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• 2012

Previously identified QTL regions on BTA1 and BTA5 were investigated to validate the QTL regions and to identify candidate genes for growth and carcass traits in commercial cattle populations from the USA and Korea. Initially, a total of 8 polymorphic microsatellite (MS) markers in the BTA1 and 5 QTL regions were used for Chi-square tests to compare the frequencies of individual alleles between high and low phenotypic groups for the US (Michigan Cattleman's Association/Michigan State University; MCA/MSU) cattle. For a subsequent study, 24 candidate genes containing missense mutations and located within the QTL regions based on bovine genome sequence data were analyzed for genotyping in the two commercial cattle populations. Re-sequencing analyses confirmed 18 public missense SNPs and identified 9 new SNPs. Seventeen of these SNPs were used for genotyping of the MCA/MSU cattle (n = 98) and Korean native cattle (n = 323). On BTA1, UPK1B, HRG, and MAGEF1 polymorphisms residing between BM1312 and BMS4048 were significantly associated with growth and carcass traits in one or both of the MCA/MSU and Korean populations. On BTA5, ABCD2, IL22 and SNRPF polymorphisms residing between BL4 and BR2936 were associated with marbling and backfat traits in one or both of the MCA/MSU and Korean cattle populations. These results suggested that BTA 1 and 5 QTL regions may be segregating in both Korean Hanwoo and USA commercial cattle populations and DNA markers tested in this study may contribute to the identification of positional candidate genes for marker-assisted selection programs.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.103-110
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• 2005

In this paper, we present a shadow detection algorithm using the shadow features which appear in color images. Shadow regions have lower luminance and saturation than those of nearby regions, and is generally shown as dark colors. The regions are detected by means of analysing and applying their properties to images represented as the HSI color model. The proposed algorithm is consisted of two steps: at the first step, the candidate regions of shadow are found with using shadow features, and then, real shadow regions are detected only in candidate regions by using their information to reduce real objects and dark marks. The experimental results show that the algorithm is effective.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.12
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• pp.1675-1681
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• 2016

The aim of this study was to detect positional candidate genes located within the support interval (SI) regions based on the results of red blood cell, mean corpuscular volume (MCV), and mean corpuscular hemoglobin quantitative trait locus (QTL) in Sus scrofa chromosome 13, and to verify the correlation between specific single-nucleotide polymorphisms (SNPs) located in the exonic region of the positional candidate gene and the three genetic traits. The flanking markers of the three QTL SI regions are SW38 and S0215. Within the QTL SI regions, 44 genes were located, and runt-related transcription factor 1, dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A), and potassium inwardly-rectifying channel, subfamily J, member 15 KCNJ15-which are reported to be related to the hematological traits and clinical features of Down syndrome-were selected as positional candidate genes. The ten SNPs located in the exonic region of the three genes were detected by next generation sequencing. A total of 1,232 pigs of an $F_2$ resource population between Landrace and Korean native pigs were genotyped. To investigate the effects of the three genes on each genotype, a mixed-effect model which is the considering family structure model was used to evaluate the associations between the SNPs and three genetic traits in the $F_2$ intercross population. Among them, the MCV level was highly significant (nominal $p=9.8{\times}10^{-9}$) in association with the DYRK1A-SNP1 (c.2989 G$F_2$ intercross, our approach has limited power to distinguish one particular positional candidate gene from a QTL region.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.138-148
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• 2015

Object detection methods based on background learning are widely used in video surveillance. However, when a car runs with headlights on, these methods are likely to detect the car region and the area illuminated by the headlights as one connected change region. This paper describes a method of separating the car region from the area illuminated by the headlights. First, we detect change regions with a background learning method, and extract blobs, connected components in the detected change region. If a blob is larger than the maximum object size, we extract candidate object regions from the blob by clustering the intensity histogram of the frame difference between the mean of background images and an input image. Finally, we compute the similarity between the mean of background images and the input image within each candidate region and select a candidate region with weak similarity as an object region.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.4
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• pp.189-196
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• 2013

This paper presents a two-stage approach to detect pedestrians in video sequences taken from a moving vehicle. The first stage is a preprocessing step, in which potential pedestrians are hypothesized. During the preprocessing step, a difference image is constructed using a global motion estimation, vertical and horizontal edge maps are extracted, and the color difference between the road and pedestrians are determined to create candidate regions where pedestrians may be present. The candidate regions are refined further using the vertical edge symmetry features of the pedestrians' legs. In the next stage, each hypothesis is verified using the integral channel features and an AdaBoost classifier. In this stage, a decision is made as to whether or not each candidate region contains a pedestrian. The proposed algorithm was tested on a range of dataset images and showed good performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.262-266
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• 2010

Recognizing a license plate of a vehicle has widely been issued. In this thesis, firstly, mean shift algorithm is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. We then present an approach to recognize a vehicle's license plate using the Fuzzy ARTMAP neural network, a relatively new architecture of the neural network family. We show that the proposed system is well to recognize the license plate and shows some compute simulations.

• Korean Journal of Remote Sensing
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• v.11 no.1
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• pp.47-64
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• 1995

In this paper, an algorithm that finds man-made structures in a praylevel aerial images is proposed to perform stereo matching. An extracted contour of buildings must have a high accuracy in order to get a good feature-based stereo matching result. Therefore this study focuses on the use of edge following in the original image rather than use of ordinary edge filters. The Algorithm is composed of two main categories; one is to find candidate regions in the whole image and the other is to extract exact contours of each building which each candidate region.. The region growing method using the centroid linkage method of variance value is used to find candidate regions of building and the contour line tracing algorithm based on an adge following method is used to extract exact contours. The result shows that the almost contours of building composed of line segments are extracted.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.1 no.1
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• pp.68-73
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• 2020

Korean fir (Abies koreana) is an evergreen coniferous tree species that is unique to South Korea. A. koreana is found in a limited sub-alpine habitat and is considered particularly vulnerable to climate change. Identification of populations vulnerable to climate change is an important component of conservation programs. In this study, a heat stress-induced transcriptome RNA-seq dataset was used to identify a subset of six genes for assessment as candidate marker genes for ecologically vulnerable populations. Samples of A. koreana were isolated from ecologically stable and vulnerable regions of the Halla and Jiri mountains, and the expression levels of the six candidate markers were assessed using quantitative real-time polymerase chain reaction. All six of the candidate genes exhibited higher expression levels in samples from vulnerable regions compared with stable regions. These results confirm that the six high temperature-induced genes can be used as diagnostic markers for the identification of populations of A. koreana that are experiencing stress due to the effects of climate change.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.73-78
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• 2006

Conventional caption extraction methods use the difference between frames or color segmentation methods from the whole image. Because these methods depend heavily on heuristics, we should have a priori knowledge of the captions to be extracted. Also they are difficult to implement. In this paper, we propose a method that uses little heuristic and simplified algorithm. We use topographical features of characters to extract the character points and use MST(Minimum Spanning Tree) to extract the candidate regions for captions. Character regions are determined by testing several conditions and verifying those candidate regions. Experimental results show that the candidate region extraction rate is 100%, and the character region extraction rate is 98.2%. And then we can see the results that caption area in complex images is well extracted.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.213-220
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• 2011

This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.