• KSCI Review
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• v.15 no.1
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• pp.179-183
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• 2007

다시점에서의 다중 객체 추적은 여러 분야에서 연구되고 있다. 다시점 영상 추적은 두 객체가 서로 근접하면 하나로 인식하는 문제점을 가지고 있다. 이러한 문제를 해결하기 위한 하나의 방법으로 능동형태모델(active shape mode: ASM)을 들 수 있다 ASM은 훈련집합을 이용하여 다른 객체에 가려진 목표 객체를 추적할 수 있다. 본 논문에서는 겹쳐진 객체를 추적하기 위해 ASM 기반의 다시점 추적 알고리듬(Multi-view tracking using ASM: MVTA)에 대해서 제안한다. 제안된 추적 방법은 (i) 영상 획득, (ii) 객체 추출, (iii) 객체 추적, 그리고 (iv) 현재 형태의 업데이트, 4가지 단계로 나눌 수 있다. 첫 번째 단계에서는 여러 대의 카메라를 사용해서 다시점 영상을 획득하며, 두 번째 단계에서는 객체를 배경으로부터 분리하며, 겹쳐진 객체로부터 목표 객체를 분리해낸다. 세 번째 단계에서는 추적을 위해 ASM을 사용하며, 마지막 단계인 네 번째 단계는 현재 입력 영상의 업데이트이다. 실험결과 제안한 MVTA는 겹쳐진 객체를 추적 시에 생기는 문제에 대해서 향상 된 결과를 보여준다.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.681-684
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• 2006

본 논문은 능동형상모델(Active Shape Model: ASM)을 사용하여 손바닥의 형상을 추출하고 경계형상을 추적하기 위한 방법을 제안한다. 먼저, 경계추적을 위한 초기위치를 입력하기 위해 컬러영상에서 피부색영역의 위치 정보를 통해 중심점을 찾고 그 값을 통해 ASM을 이용하여 손바닥의 영역을 찾는다. ASM은 다양한 경계형상의 학습을 통해 평균값과 형상의 지배적 변형을 나타내는 형상벡터를 추출하기 위한 방법론이며 생체조직과 같은 형상이 일정하지 않고 평균형상을 기준으로 변화하는 형상의 외형을 추출, 추적하기에 적합한 기술이다. 본 논문에서는 피부색 특징을 이용하여 초기 손바닥의 위치를 찾고 이러한 위치정보를 이용하여 손 경계형상의 변화를 추적하는 방법을 실험을 통해 검증하였다

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.141-149
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• 2007

Associating the shoulder line with head location of the human body is useful in verifying, localizing and tracking persons in an image. Since the head line and the shoulder line, what we call ${\Omega}$-shape, move together in a consistent way within a limited range of deformation, we can build a statistical shape model using Active Shape Model (ASM). However, when the conventional ASM is applied to ${\Omega}$-shape fitting, it is very sensitive to background edges and clutter because it relies only on the local edge or gradient. Even though appearance is a good alternative feature for matching the target object to image, it is difficult to learn the appearance of the ${\Omega}$-shape because of the significant difference between people's skin, hair and clothes, and because appearance does not remain the same throughout the entire video. Therefore, instead of teaming appearance or updating appearance as it changes, we model the discriminative appearance where each pixel is classified into head, torso and background classes, and update the classifier to obtain the appropriate discriminative appearance in the current frame. Accordingly, we make use of two features in fitting ${\Omega}$-shape, edge gradient which is used for localization, and discriminative appearance which contributes to stability of the tracker. The simulation results show that the proposed method is very robust to pose change, occlusion, and illumination change in tracking the head and shoulder line of people. Another advantage is that the proposed method operates in real time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1551-1563
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• 2004

This paper proposes a hierarchical approach to shape model ASM using wavelet transform. Local structure model fitting in the ASM plays an important role in model-based pose and shape analysis. The proposed algorithm can robustly find good solutions in complex images by using wavelet decomposition. we also proposed effective method that estimates and corrects object's movement by using Wavelet transform-based hierarchical motion estimation scheme for ASM-based, real-time video tracking. The proposed algorithm has been tested for various sequences containing human motion to demonstrate the improved performance of the proposed object tracking.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.512-517
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• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.266-272
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• 2013

Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.105-108
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• 2018

Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.868-870
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• 2005

본 논문은 포즈가 취해진 얼굴의 정확한 특징점 추출을 위하여 적응적인 평균 모양 방법을 이용한 ASM(Active Shape Model)을 제안한다. ASM은 사람 얼굴의 모양을 모델링하기 위하여 통계학상의 모양 모델을 이용한다. 통계학상의 모양 모델의 평균 모양은 입력 영상의 얼굴 포즈와 관계없이 하나로 고정되어 있으며, 이는 모양 모델 제한 조건 검사 및 복원과정에서 잘못된 결과를 만드는 원인이 된다. 이러한 문제를 해결하기 위하여 입력 영상의 얼굴 모양에 적응적인 평균 모양을 제안하며, 실험을 통해 제안한 방법이 고정된 평균 모양 방법의 문제를 해결하고 특징점 추출 성능을 향상시킴을 보였다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.141-150
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• 2013

This study proposes technology using Active Shape Model to track the object separating it by depth-sensors. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust object can be extracted. The proposed algorithm removes the horizontal component from the information of the initial depth map and separates the object using the vertical component. In addition, it is also a more efficient morphology, and labeling to perform image correction and object extraction. By applying Active Shape Model to the information of an extracted object, it can track the object more robustly. Active Shape Model has a robust feature-to-object occlusion phenomenon. In comparison to visual camera-based object tracking algorithms, the proposed technology, using the existing depth of the sensor, is more efficient and robust at object tracking. Experimental results, show that the proposed ASM-based algorithm using depth sensor can robustly track objects in real-time.

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.45-55
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• 2013

In this study, we propose a tongue diagnosis system which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue fur ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas and the distribution of tongue coating of six areas is examined by SVM. For SVM, we use a 3-dimensional vector calculated by PCA from a 12-dimensional vector consisting of RGB, HSV, Lab, and Luv. As a result, we stably detected the tongue area using ASM. Furthermore, we recognized that PCA and SVM helped to raise the ratio of tongue coating detection.