• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1490-1499
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• 1999

This paper proposes a new handwritten numeral recognition method using numeral shape grasps and FSOM (Fuzzy Self-Organizing Map). The proposed algorithm is based on the idea that numeral input data with similar shapes are classified into the same class. Shapes of numeral data are created using lines of external-contact and the class of numeral data is determined by template matching of the shapes. Each class of numeral data has FSOM and feature extraction method, respectively. In this paper, we divide the numeral database into the 16 classes. The divided FSOM model allows not only an independent learning phase of SOM but also step-by-step learning. Experiments using Concordia University handwritten numeral database proved that the proposed algorithm is effective to improve recognition accuracy.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.107-116
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• 2014

This paper presents camera-based road surface marking detection methods suited to sensor fusion-based positioning system that consists of low-cost GPS (Global Positioning System), INS (Inertial Navigation System), EDM (Extended Digital Map), and vision system. The proposed vision system consists of two parts: lane marking detection and RSM (Road Surface Marking) detection. The lane marking detection provides ROIs (Region of Interest) that are highly likely to contain RSM. The RSM detection generates candidates in the regions and classifies their types. The proposed system focuses on detecting RSM without false detections and performing real time operation. In order to ensure real time operation, the gating varies for lane marking detection and changes detection methods according to the FSM (Finite State Machine) about the driving situation. Also, a single template matching is used to extract features for both lane marking detection and RSM detection, and it is efficiently implemented by horizontal integral image. Further, multiple step verification is performed to minimize false detections.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.179-183
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• 1996

Most of the conventional electrocardiogaphies fail to detect signals other than P-QRS-T due to the limited SNR and bandwidth. High-resolution electrocardiography (HRECG) provides better SNR and wider bandwidth for the detection of micro-potentials with higher frequency components such as ventricual late potentials(LP). In this paper, we developed a HRECG using uncorrected XYZ lead. The overall gain of the amplifier is 4000 and the bandwidth is $0.5{\sim}300Hz$ without using 60Hz notch filter. Three 16-bit AH converters sample X, Y, and Z signals simultaneously with a sampling frequency of 2000Hz. Sampled data are transmitted to PC via a DMA-controlled serial communication channel using RS-485 and HDLC protocol. The noise level of the developed HRECG is less than $5{\mu}V_{rms,\;RTI}$. In order to further reduce the noise level, signal averaging technique is implemented utilizing template matching method. The SNR of the developed HRECG is high enough for the detection of LP.

• Annual Conference of KIPS
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• 2018.10a
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• pp.733-735
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• 2018

최근의 차량 주차관리 시설, 출입통제가 필요한 장소 그리고 도로 방범카메라를 통한 단속 등 다양한 곳에서 차량 번호판 자동 인식 기술들이 활용되고 있다. 하지만 현재 사용되고 있는 LPR(License Plate Recognition) 시스템에는 많은 장비와 비용이 들어간다는 큰 단점이 존재한다. 본 논문에서는 하나의 컴퓨터와 최소의 카메라를 가지고 할 수 있는 기계학습을 통한 영상처리를 제안하려 한다. 먼저 딥러닝 프레임워크 중 하나인 YOLO(You Only Look Once) [4]를 활용하여 자동차의 번호판 부분의 영역을 검출하고 Grayscale를 통해 햇빛 또는 조명 등의 영향을 감소시켜 번호판의 특징을 보존시킨다. 전처리 작업이 끝난 후 번호판에서 숫자를 인식 하는 부분에서는 k-NN(k-Nearest Neighbor) 알고리즘을 사용하였으며 한글 문자 인식부분은 Template Matching을 이용하였다. 제안한 알고리즘을 사용하여 기존 LPR 시스템에서 획득한 차량이미지를 대상으로 시뮬레이션 한 결과 좋은 결과를 얻을 수 있어 향후 연구 방향의 시스템 확장성의 가능성을 발견할 수 있었다.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1970-1979
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• 1999

This paper proposes an AVI system which recognizes the license plate and the driver's face simultaneously using passive AVI techniques. For this, firstly, the pro-processing algorithm independent of the environment is proposed and region extraction of the car number plate and the driver's face is described. Secondly, characters are separated and recognition parameters are extracted from target regions. Thirdly, template matching of car number plate is performed and the fuzzy relation matrix of driver face is made for the final recognition processes. The merits of the proposed system are following : Pre-processing is accomplished regardless of the environment. The application areas of conventional AVI system can be expanded in the content that the driver's face is also recognized in the proposed system compared with only the number plast is recognized in the existing systems.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.207-214
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• 2010

A method of the region extraction and recognition of a traffic light and speed sign board in the real road environment is proposed. Traffic light was recognized by using brightness and color information based on HSI color model. Speed sign board was extracted by measuring red intensity from the HSI color information We improve the recognition rate by performing an incline compensation of the speed sign for directions clockwise and counterclockwise. The proposed algorithm shows a robust recognition rate in the image sequence which includes traffic light and speed sign board.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.95-101
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• 2019

Recently, there have been many issues related to text recognition using Tesseract. One of these issues is that the text recognition accuracy is significantly lower for smaller fonts. Tesseract extracts text by creating an outline with direction in the image. By searching the Tesseract database, template matching with characters with similar feature points is used to select the character with the lowest error. Because of the poor text extraction, the recognition accuracy is lowerd. In this paper, we compared text recognition accuracy after applying various super-resolution methods to smaller text images and experimented with how the recognition accuracy varies for various image size. In order to recognize small Korean text images, we have used super-resolution algorithms based on deep learning models such as SRCNN, ESRCNN, DSRCNN, and DCSCN. The dataset for training and testing consisted of Korean-based scanned images. The images was resized from 0.5 times to 0.8 times with 12pt font size. The experiment was performed on x0.5 resized images, and the experimental result showed that DCSCN super-resolution is the most efficient method to reduce precision error rate by 7.8%, and reduce the recall error rate by 8.4%. The experimental results have demonstrated that the accuracy of text recognition for smaller Korean fonts can be improved by adding super-resolution methods to the OCR preprocessing module.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.121-126
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• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

• Annual Conference of KIPS
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• 2020.05a
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• pp.330-333
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• 2020

장신구 산업은 인건비의 비중이 높고 노동자의 역량에 따라 제품의 제작 작업 시간 및 품질의 편차가 심하다. 이에 산업계의 수요에 맞추어 실리콘 금형 표면 지름 0.75mm 홀에 이어핀을 삽입하는 공정을 자동화하기 위하여 삽입 자동화 시스템이 개발되고 있다. 본 논문에서는 이어핀 삽입 자동화시스템에서 적용할 수 있는 템플릿 매칭 방법과 관심 영역 레이블링을 통한 홀 판별 방법을 제안한다. 제안한 방법의 안정성을 확보하기 위하여 실험을 통해 최적의 매칭 방법과 이진화 기법을 적용하였으며 이어핀 홀의 좌표를 확보하여 X-Y 정밀 이송 시스템에 적용할 수 있다.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.861-869
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• 2020

We introduce the application that automatically makes several images stored in user's device into one video by using the different climax patterns appearing for each film genre. For the classification of the genre characteristics of movies, a climax pattern model style was created by analyzing the genre of domestic movie drama, action, horror and foreign movie drama, action, and horror. The climax pattern was characterized by the change in shot size, the length of the shot, and the frequency of insert use in a specific scene part of the movie, and the result was visualized. The model visualized by genre developed as a template using Firebase DB. Images stored in the user's device were selected and matched with the climax pattern model developed as a template for each genre. Although it is a short video, it is a feature of the proposed application that it can create an emotional story video that reflects the characteristics of the genre. Recently, platform operators such as YouTube and Naver are upgrading applications that automatically generate video using a picture or video taken by the user directly with a smartphone. However, applications that have genre characteristics like movies or include video-generation technology to show stories are still insufficient. It is predicted that the proposed automatic video editing has the potential to develop into a video editing application capable of transmitting emotions.