• International journal of advanced smart convergence
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• 제11권4호
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• pp.47-56
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• 2022

Recently bank card number recognition plays an important role in improving payment efficiency. In this paper we propose a new bank-card number identification algorithm. The proposed algorithm consists of three modules which include edge detection, candidate region generation, and recognition. The module of 'edge detection' is used to obtain the possible digital region. The module of 'candidate region generation' has the role to expand the length of the digital region to obtain the candidate card number regions, i.e. to obtain the final bank card number location. And the module of 'recognition' has Convolutional deep learning Neural Network (CNN) to identify the final bank card numbers. Experimental results show that the identification rate of the proposed algorithm is 95% for the card numbers, which shows 20% better than that of conventional algorithm or method.

• IEIE Transactions on Smart Processing and Computing
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• 제1권2호
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• pp.73-77
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• 2012

This paper reports a combined depth- and model-based face detection and tracking approach. The proposed algorithm consists of four functional modules; i) color-based candidate region extraction, ii) generation of the depth histogram for handling occlusion, iii) rotation-invariant face region detection using ellipse fitting, and iv) face tracking based on motion prediction. This technique solved the occlusion problem under complicated environment by detecting the face candidate region based on the depth-based histogram and skin colors. The angle of rotation was estimated by the ellipse fitting method in the detected candidate regions. The face region was finally determined by inversely rotating the candidate regions by the estimated angle using Haar-like features that were robustly trained robustly by the frontal face.

• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.490-498
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• 2011

In this paper, we present a two-stage vision-based approach to detect multi views of pedestrian in road scene images. The first stage is HG (Hypothesis Generation), in which potential pedestrian are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map, and different colors between road background and pedestrian's clothes to determine the leg position of pedestrian, then a novel symmetry peaks processing is performed to define how many pedestrians is covered in one potential candidate region. Finally, the real candidate region where pedestrian exists will be constructed. The second stage is HV (Hypothesis Verification). In this stage, all hypotheses are verified by Support Vector Machine for classification, which is robust for multi views of pedestrian detection and recognition problems.

• 한국산학기술학회논문지
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• 제11권3호
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• pp.847-852
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• 2010

자연 영상 내에 포함된 텍스트는 영상의 다양하고 중요한 특징을 갖는다. 그러므로 텍스트를 검출하고 추출하여 인식하는 것이 중요한 연구대상으로 연구되고 있다. 최근 모바일 폰 카메라를 기반으로 다양한 분야에서 많은 응용 기술이 연구 개발되고 있다. 본 논문은 에지 및 연결요소를 이용한 장면 텍스트 검출 방법을 제안한다. 그레이스케일 영상으로부터 에지 성분 검출과 지역적 표준편차를 이용하여 텍스트 영역의 경계선을 검출하고, RGB 컬러공간의 유클리디안 거리를 기준으로 연결요소를 검출한다. 검출된 에지 및 연결요소를 레이블링하고 각각 영역의 외곽사각형을 구한다. 텍스트의 휴리스틱 이용하여 후보 텍스트를 추출한다. 후보 텍스트 영역을 병합하여 하나의 후보 텍스트 영역을 생성하고, 후보 텍스트의 지역적 인접성과 구조적 유사성으로 후보 텍스트를 검증함으로서 최종적인 텍스트 영역을 검출하였다. 실험결과 에지 및 컬러 연결요소 특징을 상호 보완함으로서 텍스트 영역의 검출률을 향상시켰다.

• 한국엔터테인먼트산업학회논문지
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• 제5권1호
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• pp.127-133
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• 2011

자연영상에 내포되어 있는 문자는 다양한 내용을 표현하는 중요한 정보이다. 기존의 문자 검출 알고리즘은 영상의 복잡도와 주변의 조명, 문자와 유사한 배경색 등의 환경에서 문자영역을 검출하지 못하는 문제점이 있으므로 본 논문에서는 에지 및 형태학적 재구성에 의한 연결요소를 이용한 자연영상에 포함된 문자영역을 검출하는 방법을 제안한다. 첫 번째 단계로, 명암도 영상에서 캐니에지(Canny-Edge) 검출기를 이용한 에지 성분과 형태학적 연산에 의한 지역적 최소/최대값을 갖는 연결요소를 검출하고, 각각 검출된 연결성분을 레이블링하고, 레이블링 된 각 성분에 대해 문자가 갖는 특징을 이용한 후보 문자영역을 검출한다. 마지막으로 검출된 후보 문자 영역을 서로 합병하여 하나의 후보 문자 영역을 생성하고, 후보 문자 영역의 인접성과 유사성으로 후보 문자 영역을 검증하여 최종 문자 영역을 검출한다. 실험결과 제안한 에지 및 연결요소 성분을 이용한 방법은 문자영역 검출의 정확성이 개선되었다.

• Asian-Australasian Journal of Animal Sciences
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• 제29권12호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.636-639
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• 2003

In this paper, we present a robust method for detecting other vehicles from n forward-looking CCD camera in a moving vehicle. This system uses edge and shape information to detect other vehicles. The algorithm consists of three steps: lane detection, ehicle candidate generation, and vehicle verification. First after detecting a lane from the template matching method, we divide the road into three parts: left lane, front lane, and right lane. Second, we set the region of interest (ROI) using the lane position information and extract a vehicle candidate from the ROI. Third, we use local orientation coding (LOC) edge image of the vehicle candidate as input to a pretrained neural network for vehicle recognition. Experimental results from highway scenes show the robustness and effectiveness of this method.

• Journal of Veterinary Science
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• 제21권6호
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• pp.85.1-85.6
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• 2020

A cold-adapted porcine reproductive and respiratory syndrome virus (CA-VR2332) was generated from the modified live virus strain VR2332. CA-VR2332 showed impaired growth when cultured at 37℃ with numerous mutations (^S731^F, ^E819^D, ^G975^E, and ^D1014^N) in the hypervariable region of the NSP2, in which the mutation ^S731^F might play a vital role in viral replication at 30℃. Conserved amino acid sequences of the GP5 protein suggests that CA-VR2332 is a promising candidate for producing an effective vaccine against PRRSV infection. Further studies on replication and immunogenicity in vivo are required to evaluate the properties of CA-VR2332.

• 대한공간정보학회지
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• 제18권2호
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• pp.107-112
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• 2010

풍력발전은 자연 상태의 무공해 에너지원이며 신재생에너지 중 가장 경제성이 높은 에너지원이다. 최근 소형 풍력 발전의 개발에 따라 단지나 초고층 건축물에도 풍력 발전 시설 설치가 가능해졌다. 이러한 지역에서의 효율적인 발전을 위해서는 적절한 입지 분석이 필요하다. 본 연구에서는 부산시를 대상으로 계층적 분석 기법을 이용하여 풍력 발전 시 요구되어지는 요인들의 상대적 가중치를 산정하고 부산시의 풍속과 풍향의 특징을 나타내었고 지형 공간정보체계를 이용하여 강서구, 기장군, 사하구 지역 내에서 녹산동, 철마면, 다대1동으로 후보지를 선정하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.166-171
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• 2009

In recent years, video detection systems have been implemented in various infrastructures such as airport, public transportation, power generation system, water dam and so on. Recognizing moving objects in video sequence is an important problem in computer vision, with applications in several fields, such as video surveillance and target tracking. Segmentation and tracking of multiple vehicles in crowded situations is made difficult by inter-object occlusion. In the system described in this paper, the mean shift algorithm is firstly 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 or not. And then some characters in the license plate is recognized by using the fuzzy ARTMAP neural network, which is a relatively new architecture of the neural network family and has the capability to learn incrementally unlike the conventional BP network. We finally design a license plate recognition system using the mean shift algorithm and fuzzy ARTMAP neural network and show its performance via some computer simulations.