• 정보처리학회논문지B
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• 제10B권1호
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• pp.73-80
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• 2003

본 논문에서는 기준영상과 탐색영상 사이의 대응되는 특징 점을 정합 하는 알고리즘을 제안한다. 두 영상에서 특징 점을 찾기 위하여 Harris의 코너 점 검출기를 사용하였다. 기준영상의 각 특징 점에 대해, 정규상관계수가 임계치 이상인 탐색영상의 특징 점들로 후보 정합 점을 구한다. 최종적으로 동적 프로그래밍을 사용하여 후보 정합 점들 중에서 대응되는 특징 점을 구한다. 실험으로 인위적인 영상과 실제 영상에서 특징 점을 정합 하는 결과를 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.1427-1430
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• 1987

The feature points of an image play a very important role in understanding the image. Especially, when an image is composed of lines, vertices of the image offer informations about its property and structure. In this paper, a series of process for extracting feature points from actual IC image is described. This result can be used to acquire CIF ( Caltech Intermediate Form ) file.

• 대한원격탐사학회지
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• 제36권4호
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• pp.545-555
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• 2020

This paper focuses on automatic image registration between EO (Electro-Optical) and IR (InfraRed) satellite images with different spectral properties using block-based approach and simple preprocessing technique to enhance the performance of feature matching. If unpreprocessed EO and IR images from Kompsat-3A satellite were applied to local feature matching algorithms(Scale Invariant Feature Transform, Speed-Up Robust Feature, etc.), image registration algorithm generally failed because of few detected feature points or mismatched pairs despite of many detected feature points. In this paper, we proposed a new image registration method which improved the performance of feature matching with block-based registration process on 9-divided image and pre-processing technique based on adaptive histogram equalization. The proposed method showed better performance than without our proposed technique on visual inspection and I-RMSE. This study can be used for automatic image registration between various images acquired from different sensors.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1192-1197
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• 1991

This paper describes a method of extracting the 3-D coordinates of feature points of an object from two images taken by one camera. The first image is from a CCD camera before approaching the object and the second image is from same camera after approaching the object along the optical axis. In the two images, the feature points appear at different position on the screen due to image enlargement. From the change of positions of feature points their world coordinates are calculated. In this paper, the correspondence problem is solved by image shrinking and correlation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.6-8
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• 2005

Study that uses geometrical information in computer vision is lively. Problem that should be preceded is matching problem before studying. Feature point should be extracted for well matching. There are a lot of methods that extract feature point from former days are studied. Because problem does not exist algorithm that is applied for all images, it is a hot water. Specially, it is not easy to find feature point in endoscope image. The big problem can not decide easily a point that is predicted feature point as can know even if see endoscope image as eyes. Also, accuracy of matching problem can be decided after number of feature points is enough and also distributed on whole image. In this paper studied algorithm that can apply to endoscope image. SIFT method displayed excellent performance when compared with alternative way (Affine invariant point detector etc.) in general image but SIFT parameter that used in general image can't apply to endoscope image. The gual of this paper is abstraction of feature point on endoscope image that controlled by contrast threshold and curvature threshold among the parameters for applying SIFT method on endoscope image. Studied about method that feature points can have good distribution and control number of feature point than traditional alternative way by controlling the parameters on experiment result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권9호
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• pp.3177-3195
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• 2014

In many moving object detection problems of an aerial video, accurate and robust stabilization is of critical importance. In this paper, a novel accurate image alignment algorithm for aerial electronic image stabilization (EIS) is described. The feature points are first selected using optimal derivative filters based Harris detector, which can improve differentiation accuracy and obtain the precise coordinates of feature points. Then we choose the Delaunay Triangulation edges to find the matching pairs between feature points in overlapping images. The most "useful" matching points that belong to the background are used to find the global transformation parameters using the projective invariant. Finally, intentional motion of the camera is accumulated for correction by Sage-Husa adaptive filtering. Experiment results illustrate that the proposed algorithm is applied to the aerial captured video sequences with various dynamic scenes for performance demonstrations.

• 한국통신학회논문지
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• 제17권7호
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• pp.767-778
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• 1992

3-D 모델 기반 부호화 시스템에서 특징점 추출과 영상합성에 대하여 연구하였다. 얼굴의 특징점들은 영상처리 기술들과 얼굴에 대한 사전지식을 이용하여 자동적으로 추출된다. 추출된 얼굴의 특징점들을 이용하여 얼굴에 정합된 철선 프레임을 특징점의 움직임에 따라 변형시킨다. 변형된 철선 프레임 위에 초기 정면 영상의 질감을 매핑함으로써 합성영상이 만들어진다. 실험결과, 합성영상은 부자연스러움이 거의 나타나지 않았다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

• 정보처리학회논문지B
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• 제12B권3호
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• pp.265-272
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• 2005

본 논문에서는 특징점 기반 영상 모자익을 위해 보로노이거리를 이용하여 두 영상의 대응점을 신속히 검색하는 영상정합 방법을 제안한다. 먼저 SUSAN 코너 검출기에 의해 정차하고자 하는 영상의 특징점을 추출한 후, 기준 영상의 특징점을 기반으로 우선 순위 기반 보로노이 거리 알고리즘을 이용하여 특징점 사이의 거리 정보를 가지는 보로노이 평면을 생성한다. 모델 영상에서 특징점 위치의 분산값이 가장 큰 곳을 모델 영역으로 선택하여, 모델 영역이 포개지는 기준 영상의 보로노이 평면에서 보로노이 거리의 합이 최소화되는 대응 영역을 큐를 이용한 분할 검색 알고리즘에 의해 찾아낸다. 이 방법의 장점은 새로운 보로노이 거리 계산 알고리즘과 보로노이 평면의 검색범위를 매번 최대 1/4씩 줄여 주는 큐를 이용한 분할 검색 알고리즘을 이용함으로써 보다 신속히 대응점을 찾을 수 있다는 것이다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.714-718
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• 2009

3D reconstruction of a human face from an image sequence remains an important problem in computer vision. We propose a method, based on a factorization algorithm, that reconstructs a 3D face model from short image sequences exhibiting rotational motion. Factorization algorithms can recover structure and motion simultaneously from one image sequence, but they usually require that all feature points be well tracked. Under rotational motion, however, feature tracking often fails due to occlusion and frame out of features. Additionally, the paucity of images may make feature tracking more difficult or decrease reconstruction accuracy. The proposed 3D reconstruction approach can handle short image sequences exhibiting rotational motion wherein feature points are likely to be missing. We implement the proposal as a reconstruction method; it employs image sequence division and a feature tracking method that uses Active Appearance Models to avoid the failure of feature tracking. Experiments conducted on an image sequence of a human face demonstrate the effectiveness of the proposed method.