• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.267-274
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• 2018

This paper describes a head pose estimation method using PTZ(Pan-Tilt-Zoom) camera. When the external parameters of a camera is changed by rotation and translation, the estimated face pose for the same head also varies. In this paper, we propose a new method to estimate the head pose independently on varying the parameters of PTZ camera. The proposed method consists of 3 steps: face detection, feature extraction, and pose estimation. For each step, we respectively use MCT(Modified Census Transform) feature, the facial regression tree method, and the POSIT(Pose from Orthography and Scaling with ITeration) algorithm. The existing POSIT algorithm does not consider the rotation of a camera, but this paper improves the POSIT based on perspective projection in order to estimate the head pose robustly even when the external parameters of a camera are changed. Through experiments, we confirmed that RMSE(Root Mean Square Error) of the proposed method improve $0.6^{\circ}$ less then the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1549-1557
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• 1999

In this paper, we propose a face recognition algorithm based on the differential image method-DCT This algorithm uses neural networks which is flexible for noise. Using the same condition (same luminous intensity and same distance from the fixed CCD camera to human face), we have captured two images. One doesn't contain human face. The other contains human face. Differential image method is used to separate the second image into face region and background region. After that, we have extracted square area from the face region, which is based on the edge distribution. This square region is used as the characteristics region of human face. It contains the eye bows, the eyes, the nose, and the mouth. After executing DCT for this square region, we have extracted the feature vectors. The feature vectors were normalized and used as the input vectors of the neural network. Simulation results show 100% recognition rate when face images were learned and 92.25% recognition rate when face images weren't learned for 30 persons.

• 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.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.61-70
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• 1995

In this paper, the algorithm to extract the height of flat-rooftop structures in stereo aerial image is suggested with an assumption that location, orientation, focal length, and field of view of a camera are known. It can be adapted to stereo aerial or satellite images. For performing feature-based stereo matching, the line segments suitable to describe the shape of general buildings are chosen as the feature. This paper is composed of three categories;the first step is to extract edges of structures with the polygon extraction algorithm which utilizes the edge following method, the second step is to perform the line segment matching with the camera information, and the last step is to calculate the location of each matched line and to estimate heights. The stereo images used in experiments are not real but synthetic ones. The experiment shows good results.

• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.338-344
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• 2015

The national competency standards (NCS) is a systematize and standardize for skills required to perform their job. The NCS has developed a learning module with materialization and standardize by competence unit element, which is the unit of specific job competency. The existing curriculum is material to gab analysis for use in education training with competence unit element. The existing gab analysis has evaluated subjectively by experts. The gab analysis by experts bring up a subject subjective decision, accuracy lack, temporal and spatial inefficiency by psychological factor. This paper is proposed automated evaluation model for problem resolve of subjective evaluation. This paper use index term extraction, term frequency-inverse document frequency for feature value extraction, cosine similarity algorithm for gab analysis between existing curriculum and competence unit element. This paper was presented similarity mapping table between existing curriculum and competence unit element. The evaluation model in this paper should be complemented by an improved algorithm from the structural characteristics and speed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.28-37
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• 2009

In this paper, we propose an obstacle localization method using a road feature based on a V-disparity map binarized by a maximum frequency value. In a conventional method, the detection performance is severely affected by the size, number and type of obstacles. It's especially difficult to extract a large obstacle or a continuous obstacle like a median strip. So we use a road feature as a new decision standard to localize obstacles irrespective of external environments. A road feature is proper to be a new decision standard because it keeps its rough feature very well in V-disparity under environments where many obstacles exist. And first of all, we create a binary V-disparity map using a maximum frequency value to extract a road feature easily. And then we compare the binary V-disparity map with a median value to remove noises. Finally, we use a linear interpolation for rows which have no value. Comparing this road feature with each column value in disparity map, we can localize obstacles robustly. We also propose a post-processing technique to remove noises made in obstacle localization stage. The results in real road tests show that the proposed algorithm has a better performance than a conventional method.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.171-174
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• 2005

본 논문에서는 인가의 인지학적인 두뇌 원리인 대뇌피질과 해마 신경망을 공학적으로 모델링하여 얼굴 영상의 특징 벡터들을 고속 학습하고, 각 영상의 최적의 특징을 구성할 수 있는 해마 학습 알고리즘(Hippocampal Learning Algorithm)을 개발하여 RFID를 이용한 생체인식 시스템을 제안한다. 입력되는 얼굴 영상 데이터들은 NMF(Non-negative Matrix Factorization)를 이용하여 특징이 구성되고, 이러한 특징들은 해마의 치아 이랑 영역에서 호감도 조정에 따라서 반응 패턴으로 이진화 되고, CA3 영역에서 자기 연상 메모리 단계를 거쳐 노이즈를 제거한다. CA3의 정보를 받는 CA1영역에서는 단층 신경망에 의해 단기기억과 장기기억으로 나누어서 저장되고 해당 특징의 누적 개수가 문턱치(threshold)를 만족하면 장기 기억 장소로 저장시키도록 한다. 위와 같은 개념을 바탕으로 구현되는 RFID 생체인식 시스템은 특징의 분별력과 학습속도면에서 우수한 성능을 보일 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1183-1186
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• 2004

In the steel production line, the molten metal of a furnace is transformed into billet and then moves to the heating furnace of the hot rolling mill. This paper describes about the realtime billet characters recognition system in the steel production line. Normally, the billets are mixed at yard so that their identifications are very difficult and very important processing. The character recognition algorithm used in this paper is base on the subspace method by K-L transformation. With this method, we need no special feature extraction steps, which are usually error prone. So the gray character images are directly used as input vectors of the classifier. To train the classifier, we have extracted eigen vectors of each character used in the billet numbers, which consists of 10 arabia numbers and 26 alphabet aharacters, which are gathered from billet images of the production line. We have developed billet characters recognition system using this algorithm and tested this system in the steel production line during the 8-days. The recognition rate of our system in the field test has turned out to be 94.1% (98.6% if the corrupted characters are excluded). In the results, we confirmed that our recognition system has a good performance in the poor environments and ill-conditioned marking system like as steel production plant.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1102-1105
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• 2004

In this paper, the problem of a viewer's head tracking in a desktop-based interactive stereoscopic display system is considered. A fast and low-cost approach to the problem is important for such a computing environment. The system under consideration utilizes a shuttle glass for stereoscopic display. The proposed method makes use of an image taken from a single low-cost video camera. By using a simple feature extraction algorithm, the obtained points corresponding to the image of the user-worn shuttle glass are used to estimate the glass center, its local 'yaw' angle, as measured with respect to the glass center, and its global 'yaw' angle as measured with respect to the camera location. With these estimations, the stereoscopic image synthetic program utilizes those values to interactively adjust the two-view stereoscopic image pair as displayed on a computer screen. The adjustment is carried out such that the so-obtained stereoscopic picture, when viewed from a current user position, provides a close-to-real perspective and depth perception. However, because the algorithm and device used are designed for fast computation, the estimation is typically not precise enough to provide a flicker-free interactive viewing. An error concealment method is thus proposed to alleviate the problem. This concealment method should be sufficient for applications that do not require a high degree of visual realism and interaction.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.174-182
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• 1996

In continuous speech recognition system, it is possible to implement the system which can handle unlimited number of words by using limited number of phonetic units such as phonemes. Dividing continuous speech into the string of tems of phonemes prior to recognition process can lower the complexity of the system. But because of the coarticulations between neiboring phonemes, it is very difficult ot extract exactly their boundaries. In this paper, we propose the algorithm ot extract short terms which can represent each phonemes instead of extracting their boundaries. The short terms of lower spectral change and higher spectral chang eare detcted. Then phoneme changes are detected using distance measure with this lower spectral change terms, and hgher spectral change terms are regarded as transition terms or short phoneme terms. Finally lower spectral change terms and the mid-term of higher spectral change terms are regarded s the represent each phonemes. The cepstral coefficients and weighted cepstral distance are used for speech feature and measuring the distance because of less computational complexity, and the speech data used in this experimetn was recoreded at silent and ordinary in-dorr environment. Through the experimental results, the proposed algorithm showed higher performance with less computational complexity comparing with the conventional segmetnation algorithms and it can be applied usefully in phoneme-based continuous speech recognition.