• Proceedings of the Korean Information Science Society Conference
• /
• 2000.10b
• /
• pp.431-433
• /
• 2000

본 논문에서는 얼굴인식에 주로 사용되는 PCA를 얼굴포즈판별로 적용해 보았다. 얼굴포즈판별은 개개인의 얼굴특징을 강조해야 하는 얼굴인식과는 달리 일반적인 얼굴특징을 이용하기 때문에 PCA에 적합한 응용분야이다. 그러나, 다양한 얼굴포즈에 대한 영상을 하나의 표본집합으로 사용하면, 표본집합의 분산이 크기 때문에 포즈별로 표본집합을 달리하여 PCA모듈을 구성하는 것이 타당하다. 표본수집의 어려움은 3차원 한국인 표준모형을 이용해 극복하고, 이를 통하여 다양한 조명방향 및 얼굴포즈에 대한 표본을 수집하였다. 5방향의 얼굴포즈에 대한 판별 실험을 통하여 모율화된 PCA의 분류기로서의 가능성을 살펴보고, 조명에 따른 오류를 완하하고자 비 선형적 패턴을 나타내는 각 PCA모듈의 결과를 신경망에 적용하여 보았다.

• Journal of Gifted/Talented Education
• /
• v.21 no.1
• /
• pp.107-122
• /
• 2011

The purpose of this study is to explore the perception of the observation and nomination system for the identification of the gifted and to find out the importance and practice level perceived by the gifted education teachers using the concept mapping approach. For this, twelve gifted education teachers brainstormed and gathered ideas for the ideal ways of observation and nomination system and the gathered statements were solicited. Multidimensional scaling and hierarchical cluster analysis were also used. In addition, 112 gifted education teachers rated the importance of and the practice level for the suggested ideas of observation and nomination system. Results were as follows: First, 36 statements were solicited and as a result of concept mapping the suggested observation and nomination system were categorized as 'attainment of professionality', 'attainment of administrative support', 'attainment of fairness', and 'considering points for recommendation.' Second, there were significant differences between the perceived importance levels and the practice levels. Based on the results, imlications of the study were discussed in depth.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.6
• /
• pp.622-627
• /
• 2014

This paper proposes a new method which improves recognition rate on the gait recognition system using wavelet transform, linear discriminant analysis and genetic algorithm. We use wavelet transform to obtain the four sub-bands from the gait energy image. In order to extract feature data from sub-bands, we use linear discriminant analysis. Distance values between training data and four sub-band data are calculated and four weights which are calculated by genetic algorithm is assigned at each sub-band distance. Based on a new fusion distance value, we conducted recognition experiments using k-nearest neighbors algorithm. Experimental results show that the proposed weight fusion method has higher recognition rate than conventional method.

• Journal of Korea Game Society
• /
• v.13 no.4
• /
• pp.15-24
• /
• 2013

The game of Go, which is originated at least more than 2,500 years ago, is one of the oldest board games in the world. So far the theoretical studies concerning to the Go openings are still insufficient. We applied traditional LDA algorithm to recognize a pro player's opening to a class obtained from the training openings. Both class-independent LDA and class-dependent LDA methods are conducted with the Go game records of the Korean top 10 professional Go players. Experimental result shows that the average recognition rate of class-independent LDA is 14% and class-dependent LDA 12%, respectively. Our research result also shows that in contrary to our common sense the algorithm based on PCA outperforms the algorithm based on LDA and reveals the new fact that the Euclidean distance metric method rarely does not inferior to LDA.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.10B
• /
• pp.1111-1116
• /
• 2009

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.

• Journal of Korea Society of Industrial Information Systems
• /
• v.19 no.4
• /
• pp.53-63
• /
• 2014

Moving context determination is an important issue to be resolved in a mobile computing environment. This paper presents a method for recognizing and classifying a mobile user's moving context by Euclidean distance similarity. In the proposed method, basic data are gathered using Global Positioning System (GPS) and accelerometer sensors, and by using the data, the system decides which moving situation the user is in. The decided situation is one of the four categories: stop, walking, run, and moved by a car. In order to evaluate the effectiveness and feasibility of the proposed scheme, we have implemented applications using several variations of Euclidean distance similarity on the Android system, and measured the accuracies. Experimental results show that the proposed system achieves more than 90% accuracy.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.3
• /
• pp.195-198
• /
• 2013

Many libraries utilize RFID tags for checking in and out of books. However, the recognition rate of this automatic process may depend on the orientation of antennas and RFID tags. Therefore we need supplemental systems to improve the recognition rate. The proposed algorithm sets up the ROI of the book existing area from the input image and then performs Canny edge detection algorithm to extract edges of books. Finally Hough line transform algorithm allows to detect the number of books from the extracted edges. To evaluate the performance of the proposed method, we applied our method to 350 book images under various circumstances. We then analyzed the performance of proposed method from results using recognition and mismatch ratio. The experimental result gave us 97.1% accuracy in book counting.

• Annual Conference on Human and Language Technology
• /
• 2015.10a
• /
• pp.157-162
• /
• 2015

자동통역(Speech-to-speech translation)의 최우선 단계인 음성인식과정에서 발생한 오류문장은 대부분 비문법적 구조를 갖거나 의미를 이해할 수 없는 문장들이다. 이러한 문장으로 자동번역을 할 경우 심각한 통역오류가 발생하게 되어 이에 대한 개선이 반드시 필요한 상황이다. 이에 본 논문에서는 음성인식 오류문장이 정상적인 인식문장에 비해 비문법적이거나 무의미하다는 특징을 이용하여 DNN(Deep Neural Network) 기반 음성인식오류 판별기를 구현하였으며 84.20%의 오류문장 분류성능결과를 얻었다.

• The KIPS Transactions:PartB
• /
• v.11B no.7 s.96
• /
• pp.885-892
• /
• 2004

In this paper, we propose a multi-modal biometrics system based on face and signature recognition system. Here, the face recognition system is designed by fuzzy LDA, and the signature recognition system is implemented with the LDA and segment matching methods. To effectively aggregate two systems, we obtain statistical distribution models based on matching values for genuine and impostor, respectively. And then, the final verification is Performed by the support vector machine. From the various experiments, we find that the proposed method shows high recognition rates comparing with the conventional methods.

• Journal of KIISE:Software and Applications
• /
• v.31 no.6
• /
• pp.799-807
• /
• 2004

In this paper, we deal with an emotion recognition method using facial images and speech signal. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Emotion recognition using the facial expression is performed by using a multi-resolution analysis based on the discrete wavelet transform. And then, the feature vectors are extracted from the linear discriminant analysis method. On the other hand, the emotion recognition from speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and then the final recognition is obtained from a multi-decision making scheme.