• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.253-256
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• 2011

본 논문은 기존의 디지털 설진 시스템이 아닌 임베디드 환경에서의 실시간 설진 진단 방법을 제안한다. 임의의 환경에서 얻어낸 이미지에서 혀 영역의 추출과 추출된 영역에서의 혀의 상태를 진단하는데는 많은 어려움이 있다. 다양한 조명환경에서의 영상으로부터 혀 영역을 추출해 내는 방법으로는 ASM을 이용하는 방법이 있는데 이는 검출률이 낮아 정확도가 떨어진다. 이를 보완하기 위해 본 논문에서는 ASM과 물체 외곽 정보 복원에 기반을 둔 컬러 분산 스테이크 기법을 사용하여 정확도를 개선하는 방법을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1891-1896
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• 2006

In this paper, we propose an improved Active Shape Model for extracting lip contour. Lip deformation is modeled by a statistically deformable model based Active Shape Model. Because each point is moved independently using local profile information in Active Shape Model, many error may happen. To use a global information, we define an energy function similar to an energy function in Active Contour Model, and points are moved to positions at which the total energy is minimized. The experiments have been performed for many lip images of Tulip 1 database, and show that our method extracts lip shape than a traditional ASM more exactly.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.1-7
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• 2014

Hippocampus is an important part of brain which is related with early memory storage and spatial navigation. By observing the anatomy of hippocampus, some brain diseases effecting human memory (e.g. Alzheimer, schizophrenia, etc.) can be diagnosed and predicted earlier. The diagnosis process is highly related with hippocampus segmentation. In this paper, hippocampus segmentation using Active Shape Model, which not only works based on image intensity, but also by using prior knowledge of hippocampus shape and intensity from the training images, is proposed. The results show that ASM is applicable in segmenting hippocampus from whole brain MR image. It also shows that adding more images in the training set results in better accuracy of hippocampus segmentation.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.444-446
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• 2011

본 논문은 실시간으로 카메라 영상으로부터 얼굴의 각 특징점을 검출하고, 검출된 특징점을 이용하여 웃음 표정을 인식하는 시스템을 제안한다. 제안된 시스템은 ASM(Active Shape Model)을 이용하여 실시간 검출부에서 얼굴 영상을 획득한 다음 ASM 학습부에서 학습된 결과를 가지고 얼굴의 특징을 찾는다. 얼굴 특징의 영상으로부터 입술 영역을 검출한다. 이렇게 검출된 입술 영역과 얼굴 특징점을 이용하여 사용자의 웃음 표정을 검출하고 인식하는 방법을 사용함으로써 웃음 표정 인식의 정확도를 높힐 수 있음을 알 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.395-400
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• 2011

In this paper, we propose an study of the facial emotion recognition considering the dynamical variation of emotional state in facial image sequences. The proposed system consists of two main step: facial image based emotional feature extraction and emotional state classification/recognition. At first, we propose a method for extracting and analyzing the emotional feature region using a combination of Active Shape Model (ASM) and Facial Action Units (FAUs). And then, it is proposed that emotional state classification and recognition method based on Hidden Markov Model (HMM) type of dynamic Bayesian network. Also, we adopt a Harmony Search (HS) algorithm based heuristic optimization procedure in a parameter learning of HMM in order to classify the emotional state more accurately. By using all these methods, we construct the emotion recognition system based on variations of the dynamic facial image sequence and make an attempt at improvement of the recognition performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.139-146
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• 2014

This paper proposes an efficient method for automatically distinguishing various facial expressions. To recognize the emotions from facial expressions, the facial images are obtained by digital cameras, and a number of feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by statistical pattern classifiers such Naive Bayes, MLP (multi-layer perceptron), and SVM (support vector machine). Based on the experimental results with 5-fold cross validation, SVM was the best among the classifiers, whose performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions.

• 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.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1373-1382
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• 2011

In this study, we propose an automated smile analysis system for self smile training. The proposed system detects the face area from the input image with the AdaBoost algorithm, followed by identifying facial features based on the face shape model generated by using an ASM(active shpae model). Once facial features are identified, the lip line and teeth area necessary for smile analysis are detected. It is necessary to judge the relationship between the lip line and teeth for smiling degree analysis, and to this end, the second differentiation of the teeth image is carried out, and then individual the teeth areas are identified by means of histogram projection on the vertical axis and horizontal axis. An analysis of the lip line and individual the teeth areas allows for an automated analysis of smiling degree of users, enabling users to check their smiling degree on a real time basis. The developed system in this study exhibited an error of 8.6% or below, compared to previous smile analysis results released by dental clinics for smile training, and it is expected to be used directly by users for smile training.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.2
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• pp.92-99
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• 2014

This paper proposes the method of facial expression recognition based on decision tree structure. In the image of facial expression, ASM(Active Shape Model) and LBP(Local Binary Pattern) make the local features of a facial expressions extracted. The discriminant features gotten from local features make the two facial expressions of all combination classified. Through the sum of true related to classification, the combination of facial expression and local region are decided. The integration of branch classifications generates decision tree. The facial expression recognition based on decision tree shows better recognition performance than the method which doesn't use that.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.1-11
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• 2004

In this paper we proposed a hierarchical ASM for real-time tracking of non-rigid objects. For tracking an object we used ASM for estimating object contour possibly with occlusion. Moreover, to reduce the processing time we used hierarchical approach for real-time tacking. In the next frame we estimated the initial feature point by using Kalman filter. We also added block matching algorithm for increasing accuracy of the estimation. The proposed hierarchical, prediction-based approach was proven to out perform the exiting non-hierarchical, non-prediction methods.