• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.1
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• pp.6-17
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• 2013

Recently, the color of vehicle license plate has been changed from green to white. Thus the vehicle plate recognition system used for parking management systems, speed and signal violation detection systems should be robust to the both colors. This paper presents a vehicle license plate recognition system, which works on both of green and white plate at the same time. In the proposed system, the image of license plate is taken from a captured vehicle image by using morphological information. In the next, each character region in the license plate image is extracted based on the vertical and horizontal projection of plate image and the relative position of individual characters. Finally, for the recognition process of extracted characters, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis) are sequentially utilized. In the experiment, vehicle license plates of both green background and white background captured under irregular illumination conditions have been tested, and the relatively high extraction and recognition rates are observed.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.113-123
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• 2012

Recently, various attempts have been made to apply Intelligent Transportation System under various environments and conditions. Consequently, an accurate license plate recognition regardless of vehicle location and viewing angle is required. In this paper, we propose a novel license plate recognition system which exploits a) the format of license plates to remove false candidates of license plates and to extract characters in license plates and b) the characteristics of Hangul for accurate character recognition. In order to eliminate false candidates of license plates, the proposed method first aligns the candidates of license plates horizontally, and compares the position and the shape of objects in each candidate with the prior information of license plates provided by Korean Ministry of Construction & Transportation. The prior information such as aspect ratio, background color, projection image is also used to extract characters in license plates accurately applying an improved local binarization considering luminance variation of license plates. In case of recognizing Hangul in license plates, they are initially grouped according to their shape similarity. Then a super-class method, a hierarchical analysis based on key feature points is applied to recognize Hangul accurately. The proposed method was verified with high recognition rate regardless of background image, which eventually proves that the proposed LPR system has high performance regardless of the vehicle location or viewing angle.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.263-271
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• 2016

Localizing an object in image is a common task in the field of computer vision. As the existing methods provide a detection for the single object in an image, they have an utilization limit for the use of the application, due to similar objects are in the actual picture. This paper proposes an efficient method of object localization for image recognition. The new proposed method uses color correlation back-projection in the YCbCr chromaticity color space to deal with the object localization problem. Using the proposed algorithm enables users to detect and locate primary location of object within the image, as well as candidate regions can be detected accurately without any information about object counts. To evaluate performance of the proposed algorithm, we estimate success rate of locating object with common used image database. Experimental results reveal that improvement of 21% success ratio was observed. This study builds on spatially localized color features and correlation-based localization, and the main contribution of this paper is that a different way of using correlogram is applied in object localization.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.809-817
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• 2002

This paper describes a new face detection method that is a pre-processing algorithm for on-line face recognition. To complement the weakness of using only edge or rotor features from previous face detection method, we propose the two types of face detection method. The one is a combined method with edge and color features and the other is a center area color sampling method. To prevent connecting the people's face area and the background area, which have same colors, we propose a new adaptive edge detection algorithm firstly. The adaptive edge detection algorithm is robust to illumination variance so that it extracts lots of edges and breakouts edges steadily in border between background and face areas. Because of strong edge detection, face area appears one or multi regions. We can merge these isolated regions using color information and get the final face area as a MBR (Minimum Bounding Rectangle) form. If the size of final face area is under or upper threshold, color sampling method in center area from input image is used to detect new face area. To evaluate the proposed method, we have experimented with 2,100 face images. A high face detection rate of 96.3% has been obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.801-804
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• 2008

During face recognition process, face detection process is most preceding process. However, face has very high floating property, so the result could be very different according to which method we used. This paper studies about eye detection and eye blinking verification using edge and color information from YCbCr distribution map, segmentation, and labeling methods.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.766-768
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• 2004

자연영상에서 획득된 문자를 인식하는 연구는 대부분 디지털 카메라나 캠코더를 이용하여 획득된 고해상도 영상을 입력영상으로 사용하고 있다. 본 논문에서는 휴대폰 카메라로 획득된 저해상도 영상을 입력영상으로 사용하였다. 저해상도의 영상은 적은 수의 픽셀로 정보를 표현하고 있기 때문에 기존에 제시되었던 다양한 이진화 방법으로는 문자와 배경을 깨끗하게 분리해 낼 수 없다. 본 논문은 입력영상의 이진화를 위친 K-Means 알고리즘을 이용하여 칼라 클러스터링을 하였으며, 이진화 성능을 향상시키기 위해 지능형 주파수 필터를 사용하였다. 이진화된 영상을 파이프라인 구조의 인식 시스템에 인식시킴으로써 기존의 제안 방법들에 비하여 인식 성능을 향상시킬 수 있었다.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1044-1049
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• 2006

얼굴 추적은 Vision base HCI의 핵심인 얼굴인식, 표정인식 그리고 Gesture recognition등의 다른 여러 기술을 지원하는 중요한 기술이다. 이런 얼굴 추적기술에는 영상(Image)의 Color또는 Contour등의 불변하는 특징들을 사용 하거나 템플릿(template)또는 형태(appearance)를 사용하는 방법 등이 있는데 이런 방법들은 조명환경이나 주위 배경등의 외부 환경에 민감하게 반응함으로 해서 다양한 환경에 사용할 수 없을 뿐더러 얼굴영상만을 정확하게 추출하기도 쉽지 않은 실정이다. 이에 본 논문에서는 deformable한 model을 사용하여 model과 유사한 shape과 appearance를 찾아 내는 AAM(Active Appearance Model)을 사용하는 얼굴 추적 시스템을 제안하고자 한다. 제안된 시스템에는 기존의 Combined AAM이 아닌 Independent AAM을 사용하였고 또한 Fitting Algorithm에 Inverse Compositional Image Alignment를 사용하여 Fitting 속도를 향상 시켰다. AAM Model을 만들기 위한 Train set은 150장의 4가지 형태에 얼굴을 담고 있는 Gray-scale 영상을 사용 하였다. Shape Model은 각 영상마다 직접 표기한 47개의 Vertex를 Trianglize함으로서 생성되는 71개의 Triangles을 하나의 Mesh로 구성하여 생성 하였고, Appearance Model은 Shape 안쪽의 모든 픽셀을 사용해서 생성하였다. 시스템의 성능 평가는 Fitting후 Shape 좌표의 정확도를 측정 함으로서 평가 하였다.

• Journal of the Korean Institute of Landscape Architecture
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• v.23 no.4
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• pp.20-27
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• 1996

The main purpose of this research is to develop a program supporting landscape planting design on AutoCAD basis using AutoLISP and DCL language. Current CAD use in landscape architecture field is mainly focused on customizing plant symbols for supporting two dimensional drafting rather than three dimensional consideration. This program is composed of eight module a such as PLANT module for inserting plant symbols, LABEL module for labeling task, SIMULATION module for simulating plant growth and seasonal color variation, TABLE module for generating plant table automatically, BUILDING module, BLOCK module, UTILITY module for deleting, transforming, shading symbols and DB MANAGER module for manipulating data. Design automation ability using automatic object recognition technique in this program allows AutoCAD to be used as a design tool in addition to its main role as a drafting tool through supporting landscape designers to generate many alternatives in the early phase of design.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.451-454
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• 2001

본 논문에서는 그룹화 블록스네이크와 영상분할을 이용하여 다차선을 검출하고 컬러 정보를 기반으로 차량 후면에 위치하는 미등과 브레이크등을 인식, 저속 주행환경에서의 다차선 및 전방차량을 인식하는 알고리즘을 제안하였다. 제안한 알고리즘에서는 기울기 값과 명암도 값으로 기초 블록을 얻은 뒤, 차선의 가능성이 큰 블록을 탐색하여 영상분할을 시행한다. 영상 분할에서 잡음 블록들을 제거하여 차선일 가능성이 가장 높은 블록들만을 검출하고, 그룹화 블록스네이크를 이용하여 차선을 검출하도록 하였다. 또한 전방 차량인식을 위해 미등과 브레이크등의 컬러 특징을 이용하여 후보 영역을 분할한 후, 미등과 브레이크등의 패턴의 기하학적 특징과 위치적 특징을 이용하여 한 쌍의 미등 혹은 브레이크등을 탐지하도록 하였다. 탐지된 양쪽 등의 위치정보를 이용하여 전방차량의 위치를 측정 할 수 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.372-375
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• 2003

The incidence of blindness resulting from diabetic retinopathy has significantly increased despite the intervention of insulin to control diabetes mellitus. Early signs are microaneurysms, exudates, intraretinal hemorrhages, cotton wool patches, microvascular abnormalities, and venous beading. Advanced stages include neovascularization, fibrous formations, preretinal and vitreous microhemorrhages, and retinal detachment. Microaneurysm count is important because it is an indicator of retinopathy progression. The purpose of this paper is to apply data mining to detect diabetic retinopathy patterns in routine fundus fluorescein angiography. Early symptoms are of principal interest and therefore the emphasis is on detecting microaneurysms rather than vessel tortuosity. The analysis does not involve image-recognition algorithms. Instead, mathematical filtering isolates microaneurysms, microhemorrhages, and exudates as objects of disconnected sets. A neural network is trained on their distribution to return fractal dimension. Hausdorff and box counting dimensions grade progression of the disease. The field is acquired on fluorescein angiography with resolution superior to color ophthalmoscopy, or on patterns produced by physical or mathematical simulations that model viscous fingering of water with additives percolated through porous media. A mathematical filter and neural network perform the screening process thereby eliminating the time consuming operation of determining fractal set dimension in every case.