• International journal of advanced smart convergence
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• v.11 no.4
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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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• v.1 no.2
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.847-852
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• 2010

Text in natural images has a various and important feature of image. Therefore, to detect text and extraction of text, recognizing it is a studied as an important research area. Lately, many applications of various fields is being developed based on mobile phone camera technology. Detecting edge component form gray-scale image and detect an boundary of text regions by local standard deviation and get an connected components using Euclidean distance of RGB color space. Labeling the detected edges and connected component and get bounding boxes each regions. Candidate of text achieved with heuristic rule of text. Detected candidate text regions was merged for generation for one candidate text region, then text region detected with verifying candidate text region using ectilarity characterization of adjacency and ectilarity between candidate text regions. Experctental results, We improved text region detection rate using completentary of edge and color connected component.

• Journal of Korea Entertainment Industry Association
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• v.5 no.1
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• pp.127-133
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• 2011

Characters in natural image are an important information with various context. Previous work of character region detection algorithms is not detect of character region in case of image complexity and the surrounding lighting, similar background to character, so this paper propose an method of character region detection in natural image using edge and connected component by morphological reconstructions. Firstly, we detect edge using Canny-edge detector and connected component with local min/max value by morphological reconstructed-operation in gray-scale image, and labeling each of detected connected component elements. lastly, detected candidate of text regions was merged for generation for one candidate text region, Final text region detected by checking the similarity and adjacency of neighbor of text candidate individual character. As the results of experiments, proposed algorithm improved the correctness of character regions detection using edge and connected components.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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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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• v.21 no.6
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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.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.107-112
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• 2010

Wind power generation is the natural source energy without pollution, and the highest economic efficiency among renewable energies. Amid the recent development of small wind power generation, wind power generation facilities can be installed in the super high-rise buildings or complexes. It is necessary to conduct analyses on appropriate locations in a bid to promote efficient power generation in those locations. In this study, relative weight of the elements required for the wind power generation were estimated by using the Analytic Hierarchy Process in Busan City, and the characteristics of wind velocity and wind direction in Busan City were expressed. As a result, 'Dadae 1-dong, Cheolma-myeon, Noksan-dong' was selected as the candidate region inside Gangseo-gu, Gijang-gun, Saha-gu by using the geo-spatial information system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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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.