• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1972-1978
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• 2010

AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.983-990
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• 2010

The image processing technique is very sensitive to the variation of external environment, so it tends to lose a lot of accuracy when the external environment changes rapidly. In this paper, we propose a vehicle detecting and tracking system for crime prevention suitable for an external environments with various changes using the image processing technique. Because the vehicle camera detector for crime prevention extracts and tracks the vehicle within one lane, it is important to classify a characteristic region rather than the contour of a vehicle. The proposed system detects the entrance of the vehicle using optical flow and tracks the vehicle by classifying the headlights, the bonnet, the front-window and the roof area of the vehicle. Experimental results show that the proposed method is robust to the environmental changes such as type, speed and time of a vehicle.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.2
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• pp.59-63
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• 2007

We propose a controller design analysis for a cross-coupling control system, which is essential for achieving high contouring accuracy in multi-axis CNC systems. The proposed analysis combines three axial controllers for each individual feed drive system together with a cross-coupling controller at the beginning of the design stage in an integrated manner. These two types of controllers used to be separately designed and analyzed since they have different control objectives. The proposed scheme is based on a mathematical formulation of a three-dimensional contour error model and includes a stability analysis for the overall control system and a performance analysis in terms of contouring and tracking accuracy at steady state. A computer simulation was used to demonstrate the validity of the proposed methodology. The performance variation was investigated under different operating conditions and controller gains, and a design range was elicited that met the given performance specifications. The results provide basic guidelines in systematic and comprehensive controller designs for multi-axis CNC systems. A cross-coupling control system was also implemented on a PC-based three-axis CNC testbed, and the experimental results confirmed the usefulness of the proposed control system in terms of contouring accuracy.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.539-548
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• 2001

In this paper, we propose that fast tracking algorithm for moving object is separated from background, using partial boundary line information. After detecting boundary line from input image, we track moving object by using the algorithm which takes boundary line information as feature of moving object. we extract moving vector on the imput image which has environmental variation, using high-performance BMA, and we extract moving object on the basis of moving vector. Next, we extract boundary line on the moving object as an initial feature-vector generating step for the moving object. Among those boundary lines, we consider a part of the boundary line in every direction as feature vector. And then, as a step for the moving object, we extract moving vector from feature vector generated under the information of the boundary line of the moving object on the previous frame, and we perform tracking moving object from the current frame. As a result, we show that the proposed algorithm using feature vector generated by each directional boundary line is simple tracking operation cost compared with the previous active contour tracking algorithm that changes processing time by boundary line size of moving object. The simulation for proposed algorithm shows that BMA operation is reduced about 39% in real image and tracking error is less than 2 pixel when the size of feature vector is [$10{\times}5$] using the information of each direction boundary line. Also the proposed algorithm just needs 200 times of search operation bout processing cost is varies by the size of boundary line on the previous algorithm.

• Progress in Medical Physics
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• v.10 no.3
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• pp.115-123
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• 1999

The two dimensional echocardiography is widely used to evaluate regional wall motion abnormality, because of its abilities to depict left ventricular wall motion. A number of researches have been processed for evaluation and quantitative analysis of left ventricular wall motion functions. In this paper, we proposed an algorithm which detects automatically and analyze quantitatively endocardial wall motion during systole. The echocardiograms were obtained in the short-axis views in normal subjects. Automated edge detection and endocardial contour tracking algorithm was applied to each frames, quantitative analysis based on segmentation was performed, pre-defined color overlays superimposed on the gray scale images, and the images was animated. The proposed algorithm provided automated, quantitative diagnosis of regional wall motion abnormality.

• 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.

• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.355-362
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• 2009

Most material of engineering interest undergoes solidification process from liquid to solid state. Identifying the underlying mechanism during solidification process is essential to determine the microstructure of material which governs the physical properties of final product. In this paper, we expand our previous two-dimensional numerical technique to three-dimensional simulation for computing dendritic solidification process with fluid convection. We used Level Contour Reconstruction Method to track the moving liquid-solid interface and Sharp Interface Technique to correctly implement phase changing boundary condition. Three-dimensional results showed clear difference compared to two-dimensional simulation on tip growth rate and velocity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.648-651
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• 2003

In this paper, we propose the recognition system of a license plate using SOM algorithm. The recognition of license plate was investigated by means of the SOM algorithm. The morphological information of horizontal and vertical edges was used to extract a plate region from a car image. In addition, the 4-direction contour tracking algorithm was applied to extract the specific area, which includes characters from an extracted plate area. Therefore, we proposed how to extract license plate region using morphological information and how to recognize the character string using SOM algorithm. In this paper, 50 car images were tested. The extraction rate obtained by the proposed extraction method showed better results than that from the color information of RGB and HSI, respectively. And the license plate recognition using SOM algorithm was very efficient.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.235-239
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• 2002

운송 컨테이너의 식별자를 추출하고 인식하는 것은 컨테이너 식별자들의 크기나 위치가 정형화되어 있지 않고 외부의 잡음으로 인하여 식별자의 형태가 훼손되어 있기 때문에 어렵다 된 논문에서는 이러한 특성을 고려하여 컨테이너 영상에 대해 Canny 마스크를 이용하여 에지를 검출하고, 검출된 에지 정보를 이용하여 수직 블록과 수평 블록을 추출하여 컨테이너의 식별자 영역을 추출한다. 추출된 컨테이너의 식별자 영역에서 히스토그램 방법과 윤곽선 추적 알고리즘을 이용하여 개별 식별자를 추출한다. 컨테이너의 개별 식별자 인식은 ART1을 개선하여 지도 학습 방법과 결합한 개선된 신경망을 제안하여 적용한다. 실험 결과에서는 제안된 컨테이너 식별자 추출 린 인식 방법이 다양한 컨테이너 영상에 대해 효율적인 것을 보인다.

• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.19-22
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• 2010

A finger language helping hearing impaired people in communication A sign language helping hearing impaired people in communication is not popular to ordinary healthy people. In this paper, we propose a method for real-time sign language recognition from a vision system using color information and fuzzy clustering system. We use YCbCr color model and canny mask to decide the position of hands and the boundary lines. After extracting regions of two hands by applying 8-directional contour tracking algorithm and morphological information, the system uses FCM in classifying sign language signals. In experiment, the proposed method is proven to be sufficiently efficient.