• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.138-145
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• 2010

Stereoscopic technology can be regarded as core basis technology which is commonly requested in field of next generation stereoscopic multi media information communication. Realization of stereoscopic image in order to express natural images that are close to reality is a part that human constantly put effort, it first began with visual recognition system, went through stereo picture by using binocular disparity and were conducted as video clip stereoscopic age. Life is changing, a new culture is formed, there were technological development which realized imagination as reality based on expansion of IT industry and core trend, and there is 3D stereoscopic image technology in the center. We will look at technology development tendency and development strategy of 3D stereoscopic image in this essay, and will suggest efficient usage plan of 3D stereoscopic image technology for continuous market expansion.

• Research in Mathematical Education
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• v.4 no.1
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• pp.45-55
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• 2000

The rapid proliferation of wireless networks provides a ubiquitous channel for delivering instructional materials at the convenience of the user. By delivering content through portable devices linked to the Internet, the full spectrum of multimedia capabilities is available for engaging the user's interest. This capability encompasses not only text but images, video, speech generation and voice recognition. Moreover, the incorporation of machine learning capabilities at the source provides the ability to tailor the material to the general level of expertise of the user as well as the immediate needs of the moment: for instance, a request for information regarding a particular city might be covered by a leisurely presentation if solicited from the home, but more tersely if the user happens to be driving a car. This paper presents system architecture to support mobile instruction in conjunction with knowledge-based tutoring capabilities. For concreteress, the general concepts are examined in the context of a system for mathematics education on the Web.

• Journal of The Korean Association For Science Education
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• v.30 no.3
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• pp.337-352
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• 2010

The purpose of this study was to compare the differences between science teachers' preference and their students' awareness for teaching and learning methods according to classroom circumstance, with a focus on the 'Stimulus and Reaction' subject unit in middle school. A survey was given to teachers and students that concentrated mainly on the 8 grade 'stimulus and reaction' unit, it was followed by interviews with 5 students to and in the interpretation of the findings. The questionnaire participants consisted of 192 science teachers and 331 $8^{th}$ grade students. Lecturing was the teaching method which was most favored by teachers and mainly recognized by students followed by questioning, educational software and film/video. We could see difference of recognition between teachers and students from this result in application, review and attitude area. The teaching methods applied by teachers and recognized by students depended on the instructional situation. In addition, it was revealed that teachers were applying various teaching methods to classroom situations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.236-238
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• 2003

Human face detection is often the first step in applications such as video surveillance, human computer interface, fare recognition, and image database management. We have constructed a simple and fast system to detect frontal human faces in complex environment and different illumination. This paper presents a fast segmentation method to combine neighboring pixels with similar hue. The algorithm constructs eye, mouth, and boundary maps for verifying each fare candidate. We test the system on images in complex environment and with confusing objects. The experiment shows a robust detection result with few false detected fates.

• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.782-791
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• 2016

The object segmentation algorithm from the background could be used for object recognition and tracking, and many applications. To segment objects, this paper proposes a method that refer to several initial frames with real-time processing at fixed camera. First we suggest the probability model to segment object and background and we enhance the performance of algorithm analyzing the color consistency and focus characteristic of camera for several initial frames. We compensate the segmentation result by using human skeleton characteristic among extracted objects. Last the proposed method has the applicability for various mobile application as we minimize computing complexity for real-time video processing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.111-113
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• 2012

KINECT is a gesture recognition camera produced by Microsoft Corp. KINECT SDK are widely available and many applications are actively being developed. Especially, KIET (Kinect Image Extraction Technique) has been used mainly for extracting moving objects from the input image. However, KIET has difficulty in extracting the human head due to the absorption of light. In order to overcome this problem, this paper proposes a new method for improving the KIET performance by using both color-image and depth image. Through experimental results, it is shown that the proposed method performs better than the conventional KIET algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.327-330
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• 2011

기계학습 기술이 발달함에 따라 기계학습은 제한된 상황에서 벗어나, 실생활과 비슷한 복잡하고 다양한 상황에서의 학습이 중요한 이슈가 되었다. 본고에서는 현실과 비슷한 상황을 도입하기 위하여 드라마를 사용한다. 드라마 내의 등장인물들은 말투, 어조, 관심주제와 같이 다양한 특성을 내재하고 있다. 등장인물들의 다양한 특성 중 관심주제는 대본 안에 글로 드러나 있으므로 기계학습을 통해 등장 인물의 인식에 활용할 수 있다. 최근, 확률그래프모델 분야에서 문서의 주제를 다루는 기법으로 자주 거론되는 토픽 모델 중 하나인 Author-Topic (AT) 모델은 등장인물의 관심주제를 학습하는 데에 적합하다. 본 논문에서는 AT 모델로 대본을 학습하고, 학습된 데이터 분포를 이용하여 장면에 등장하는 인물들을 인식하는 방법을 제시한다. 이 방법의 성능을 측정하기 위해, 미국 TV 드라마 'Friends' 대본 39편을 학습시키고, 장면에 대해 등장인물을 인식하는 실험을 수행하였다. 이 실험을 통해 본고에서 Author-Topic 모델을 이용한 인물 인식 방법이 다수의 인물이 참여한 담화의 인물들을 인식하는데 강점이 있음을 확인할 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.58-61
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• 2011

방송 콘텐츠 제작에는 카메라, VCR, NLE, 인코더 등의 장비가 사용되고 있으며, VCR 헤더 불량, 테이프 노후화/보관불량, NLE 편집 오류, 인코더 장비 불량 등의 다양한 이유로 콘텐츠에 예기치 않은 비디오 및 오디오 오류가 발생할 수 있다. 이러한 문제점을 해결하기 위하여 콘텐츠에 포함된 다양한 비디오 및 오디오 오류를 자동으로 검사할 수 있는 자동 검사 시스템이 요구된다. 본 논문에서는 이러한 다양한 오류를 자동으로 검사할 수 있는 방법 중 특히 비디오 내에 종종 포함되는 블록 오류를 대상으로 하는 고속 오류 검출 방법을 설명한다. 제안한 방법은 비디오 내의 매 프레임의 코너 수를 계산하고, 시간 증가에 따른 코너 수의 변화량을 검사하여 블록 오류가 포함될 것으로 예상되는 후보 프레임을 찾는 1단계 과정과, 후보 프레임을 대상으로 Adaboost 인식 기술을 사용하여 학습한 분류기를 통해 최종 블록 오류가 포함된 프레임을 검출하는 2단계 과정으로 구성된다. 시스템 구현 실험 결과, 비디오 내에 포함된 블록 오류를 프레임 단위로 정확하게 고속 검출 하는 것이 가능함을 확인하였다. SD급의 경우 실시간 대비 2.3배속 가량의 고속 검사가 가능하고 HD의 경우에도 0.8배속 수준의 고속 검사가 가능하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.26-33
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• 2005

In this paper, an algorithm is presented to recognize road based on unpaved test courses image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, gray level slicing, masking and identification of unpaved test courses. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing unpaved road. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning or assistance system.