• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.595-598
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• 2004

In this paper, a shape decomposition method using morphological operations is studied for decomposing the complex shape in 2-D mage into its simple primitive elements. The serious drawback of conventional shape representation algorithm is that the primitive elements are extracted too much to represent the shape and the processing time is long. To solve these problems, a new shape decomposition algorithm using the 8 bit planes obtained from gray code is proposed.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.897-902
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• 2007

인간과 컴퓨터간의 전통적인 인터페이스는 인간이 요구하는 다양한 인터페이스를 제공하지 못한다는 점에서 점차 사용하기 불편하게 되었고 이는 새로운 형태의 인터페이스에 대한 요구로 이어지게 되었다. 본 논문에서는 이러한 추세에 맞추어 카메라를 통해 인간의 손 제스처를 인식하는 새로운 인터페이스를 연구하였다. 손은 자유도가 높고 3차원의 view direction에 의해 형상이 매우 심하게 변한다. 따라서 윤곽선 기반방법과 같은 2차원으로 투영된 영상에서 contour나 edge의 정보로 손 제스처를 인식하는 데는 한계가 있다. 그러나 모델기반 방법은 3차원 정보를 이용하기 때문에 손 제스처를 인식하는데 좋으나 계산량이 많아 실시간으로 처리하기가 쉽지 않다. 이러한 문제점을 해결하기 위해 손 형상에 대한 대규모 데이터베이스를 구성하고 정규화된 공간에서 Feature 간의 연관성을 파악하여 훈련 데이터 모델을 구성하여 비교함으로써 실시간으로 손 포즈를 구별할 수 있다. 이러한 통계적 학습 기반의 알고리즘은 다양한 데이터와 좋은 feature의 검출이 최적의 성능을 구현하는 것과 연관된다. 따라서 배경으로부터 노이즈를 최대한 줄이기 위해 피부의 색상 정보를 이용하여 손 후보 영역을 검출하고 검출된 후보 영역으로부터 HLF(Haar-like Feature)를 이용하여 손 영역을 검출한다. 검출된 손 영역으로부터 패턴 분류 과정을 거쳐 손 포즈를 인식 하게 된다. 패턴 분류 과정은 HLF를 이용하여 손 포즈를 인식하게 되는데 미리 학습된 각 포즈에 대한 HLF를 이용하여 손 포즈를 인식하게 된다. HLF는 Violar가 얼굴 검출에 적용한 것으로 얼굴 검출에 좋은 결과를 보여 주었으며, 이는 적분 이미지로부터 추출한 HLF를 이용한 Adaboost 학습 알고리즘을 사용하였다. 본 논문에서는 피부색의 색상 정보를 이용 배경과 손 영상을 최대한 분리하여 배경의 대부분이 Adaboost-Haar Classifier의 첫 번째 스테이지에서 제거되는 방법을 이용하여 그 성능을 더 향상 시켜 손 형상 인식에 적용하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.160-174
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• 2023

LiDAR plays a key role in autonomous vehicles, and to improve its visibility, it is necessary to improve its performance and the detection objects. Accordingly, this study proposes a shape for traffic safety signs that is advantageous for self-driving vehicles to recognize. Improvement plans are also proposed using a shape-recognition algorithm based on point cloud data collected through LiDAR sensors. For the experiment, a DBSCAN-based road-sign recognition and classification algorithm, which is commonly used in point cloud research, was developed, and a 32ch LiDAR was used in an actual road environment to conduct recognition performance tests for 5 types of road signs. As a result of the study, it was possible to detect a smaller number of point clouds with a regular triangle or rectangular shape that has vertical asymmetry than a square or circle. The results showed a high classification accuracy of 83% or more. In addition, when the size of the square mark was enlarged by 1.5 times, it was possible to classify it as a square despite an increase in the measurement distance. These results are expected to be used to improve dedicated roads and traffic safety facilities for sensors in the future autonomous driving era and to develop new facilities.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.748-753
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• 2012

In this paper, Face recognition algorithm is designed based on optimized pRBFNNs pattern classifier using three-dimensional scanner. Generally two-dimensional image-based face recognition system enables us to extract the facial features using gray-level of images. The environmental variation parameters such as natural sunlight, artificial light and face pose lead to the deterioration of the performance of the system. In this paper, the proposed face recognition algorithm is designed by using three-dimensional scanner to overcome the drawback of two-dimensional face recognition system. First face shape is scanned using three-dimensional scanner and then the pose of scanned face is converted to front image through pose compensation process. Secondly, data with face depth is extracted using point signature method. Finally, the recognition performance is confirmed by using the optimized pRBFNNs for solving high-dimensional pattern recognition problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.4
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• pp.348-359
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• 1989

The multitree pattern recognition algorithm proposed by [1] and [2] is modified in order to improve its performance. The basic idea of the multitree pattern classification algorithm is that the binary dceision tree used to classify an unknow pattern is constructed for each feature and that at each stage, classification rule decides whether to classify the unknown pattern or to extract the feature value according to the feature ordet. So the feature ordering needed in the calssification procedure is simple and the number of features used in the classification procedure is small compared with other classification algorithms. Thus the algorithm can be easily applied to real pattern recognition problems even when the number of features and that of the classes are very large. In this paper, the wighting factor assignment scheme in the decision procedure is modified and various classification rules are proposed by means of the weighting factor. And the branch and bound method is applied to feature subset selection and feature ordering. Several experimental results show that the performance of the multitree pattern classification algorithm is improved by the proposed scheme.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.254-260
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• 1991

본 연구는 CAD데이터로 부터 부품의 형상을 인식하여 CAD시스템과 인터페이스된 자동공정설계시스템을 지식공학적 접근방법을 도입하여 개발하고자 함이 연구의 목적이다. 연구의 대상은 NC선반가공용 회전형상부품을 대상으로 하며, CAD시스템은 AutoCAD를, 소프트웨어개발에 사용된 프로그래밍언어는 Turbo-C(Version 2.0)를, 전문가시스템셀(Expert System Shell)은 CLIPS를 이용하여 개인용컴퓨터(PC)를 사용하여 개발한다. 본 연구의 내용은 NC선반가공용 회전형상부품을 대상으로 CAD/CAPP 데이터베이스 생성과 부품형상인식, 공정 및 작업의 선정, 가공순서의 결정에 대한 알고리즘을 개발하고, 공학적 규칙 및 전문가의 경험적 지식을 획득하여 지식베이스를 구축하며, 이 지식베이스를 사용한 공정설계 전문가시스템의 개발에 관한 연구의 중간결과이다.

• Journal of the Korea Computer Graphics Society
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• v.3 no.2
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• pp.77-84
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• 1997

Solid modeling refers to techniques for unambiguous representations of three- dimensional objects. The most widely used techniques for solid modeling have been Constructive Solid Geometry (CSG) and Boundary Representation (BRep). Contemporary solid modeling systems typically support both representations, and bilateral conversions between CSG and BRep are essential. However, computing a CSG from a BRep is largely an open problem. This paper presents 3D geometric reasoning algorithms for converting a BRep into a special CSG, called Destructive Solid Geometry (DSG) whose Boolean operations are all subtractions. The major application area of BRep-to-DSG conversion is feature recognition, which is essential for integrating CAD and CAM.

• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1177-1188
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• 2002

To classify real time Lead pin images in this paper, The image acquisition system was composed to C.C.D, image frame grabber(DT3153), P.C(PentiumIII). I proposed image processing algorithms. This algorithms were composed to real time monitoring, Lead Pin image acquisition, image noise deletion, object area detection, point detection and pattern classification algorithm. The raw images were acquired from Lead pin images using the system. The result images were obtained from raw images by image processing algorithms. In implemental result, The right recognition was 97 of 100 acceptable products, 95 of 100 defective products. The recognition rate was 96% for total 200 Lead Pins.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.383-396
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• 2009

Bone age assessment has been widely used in pediatrics to identify endocrine problems of children. Since the number of trained doctors is far less than the demands, there has been numerous requests for automatic estimation of bone age. Therefore, in this paper, we propose an automatic bone age assessment method that utilizes pattern classification techniques. The proposed method consists of three modules; a finger segmentation module, a normalized shape model generation module and a bone age estimation module. The finger segmentation module segments fingers and epiphyseal regions by means of various image processing algorithms. The shape model abstraction module employ ASM to improves the accuracy of feature extraction for bone age estimation. In addition, SVM is used for estimation of bone age. Features for the estimation include the length of bone and the ratios of bone length. We evaluated the performance of the proposed method through statistical analysis by comparing the bone age assessment results by clinical experts and the proposed automatic method. Through the experimental results, the mean error of the assessment was 0.679 year, which was better than the average error acceptable in clinical practice.