• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.256-259
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• 2021

본 논문에서는 사람을 키넥트로 촬영하여 3 차원 아바타로 복원하여 연예인처럼 춤을 추게 하는 기술을 설계 구현하였다. 기존의 순수 딥러닝 기반 방식과 달리 본 기술은 3 차원 인체 모델을 사용하여 안정적이고 자유로운 결과를 얻을 수 있다. 우선 인체 모델의 기하학적 정보는 3 차원 조인트를 사용하여 추정하고 DensePose를 통하여 정교한 텍스쳐를 복원한다. 여기에 3 차원 포인트-클라우드와 ICP 매칭 기법을 사용하여 의상 모델 정보를 복원한다. 이렇게 확보한 신체 모델과 의상 모델을 사용한 아바타는 신체 모델의 rigged 특성을 그대로 유지함으로써 애니메이션에 적합하여 PSY 의 <강남스타일>과 같은 춤을 자연스럽게 표현하였다. 개선할 점으로 인체와 의류 부분의 좀 더 정확한 분할과 분할과정에서 발생할 수 있는 노이즈의 제거 등을 확인되었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.5
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• pp.415-427
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• 1987

This paper presents an experimental model-based vision system which can identify and locate objects in scenes containing multiple occluded parts. The objects are assumed to be rigid and planar parts. In any recognition system the-type of objects that might appear in the image dictates the type of knowledge that is needed to recognize the object. The data is reduced to a sequential list of points or pixels that appear on the boundary of the objects. Next the boundary of the objects is smoothed using a polygonal approximation algorithm. Recognition cosists in finding the prototype that matches model to image. Now the hidden edge is reconstructed by transition model objects into occluded objects. The best match is obtained by optimising some similarity measure.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.73-79
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• 2009

In this paper, a method is proposed that enhances the performance of the facial expression recognition using template matching of Local Binary Pattern(LBP) histogram. In this method, the face image is segmented into blocks, and the LBP histogram is constructed to be used as the feature of the block. Block dissimilarity is calculated between a block of input image and the corresponding block of the model image. Image dissimilarity is defined as the weighted sum of the block dissimilarities. In conventional methods, the block weights are assigned by intuition. In this paper a new method is proposed that optimizes the weights from training samples. An experiment shows the recognition rate is enhanced by the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.734-742
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• 2000

This paper presents a new volumetric approach to 3D object recognition by using PBSM (part-based superquadric model). The assembly part object can be constructed with the set of volumetric primitives and the relationships between them. We describe volumetric characteristics of the model object with superquadric parameters. In addition, our model base has the relationships between volumetric primitives as well as the surface information : the surface type, the junction type between neighboring surfaces. These surface properties and relationships between parts are effectively used in recognition process. Our integrated method is robust to recognition of the identity, position, and orientation of randomly oriented assembly parts. Furthermore, we can reduce the effects of self-occlusion and non-linear shape changes according to viewpoint. In this paper, we show that our integrated method is robust to recognition of the identity, position, and orientation of randomly oriented assembly parts through experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2121-2128
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• 2012

This paper proposed the real time face area detection using Wavelet transform and the strong detection algorithm that satisfies the efficiency of computation and detection performance at the same time was proposed. The detected face image recognizes the face by configuring the low-dimensional face symbol through the principal component analysis. The proposed method is well suited for real-time system construction because it doesn't require a lot of computation compared to the existing geometric feature-based method or appearance-based method and it can maintain high recognition rate using the minimum amount of information. In addition, in order to reduce the wrong recognition or recognition error occurred during face recognition, the input symbol of Hidden Markov Model is used by configuring the feature values projected to the unique space as a certain symbol through clustering algorithm. By doing so, any input face will be recognized as a face model that has the highest probability. As a result of experiment, when comparing the existing method Euclidean and Mahananobis, the proposed method showed superior recognition performance in incorrect matching or matching error.

• 한국벤처창업학회:학술대회논문집
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• 2016.04a
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• pp.67-69
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• 2016

이 연구는 경영학과 MBA교육과정이 세상의 모든 제품과 기업에 공통적으로 적용되듯이, 세상의 어떤 분야의 제품이든 그 시작인 한 개 착안에서부터 시장력 확보를 위해 시도 되는 도전행위의 매개인 모든 착안과 아이디어가 기업이 되는 과정인 제벤처행위에 공통적으로 적용되는 학문이 될, MVA(PH-MVA, master of venture arming, 벤처무장학)학의 개념적 모델링의 시작에 필요로하는 정의론과 개념론으로, MBA에 있어 경영학원론이 개론화되듯, MVA에 있어 그 개론서가 될 벤처학원론서 설계에 있어서의 시작인 1편 기초론을 구성하는 내용을 벤처창업이나 기업의 신제품개발 부서에의 취업을 희망하는 학생들과 제벤처 생태계의 모든 직간접 관련 직무자 들이 공통적으로 무장하면 벤처적 행위에 필요로하는 여러 가지 직무자들 같이 소통력은 물론 직무매칭력 등의 분업 협업력이 매우 높아 질 것이라는 주장을 바탕으로한다. 그리고 제시하는 정의와 개념을 제벤처 생태계 전체의 표준으로 참고 삼아, 누구의 어떤 모델이든, 창조경제의 일부로서 미생들의 벤처(창업, 기업만들기 도전)행위나 완생기업들의 신제품개발 모두에 두루 적용 될 수 있는, 누구나가 이해, 공감, 교감, 습득이 용이한 벤처학 개발이 왕성하게 활성화될 수 있도록 하는 것이 곧 창조경제 활성화를 위한 학자들의 사명이자 새로운 성장동력 모델의 발굴이기도하다는 측면에서의 동참이 될 수 있기를 바라는 마음이다. 아울러, 이러한 정의와 개념을 바탕으로 전개되어, 벤처생태계 전반에서 수행 될 교육이 개인들의 창업(기업만들기)을 목표로하는 공부가 되기도하고 연장에, 완생기업들이 필요로하는 신제품개발에 있어서의 기본적인 능력과 자질을 갖춘 인재선발적 취업준비공부도 될 수 있는 통합적 교육이되어야한다는 취지를 가지고있다. 또 연구를 통해 제벤처(기업의 신제품개발 도전협업행위 포함) 생태계에서의 직무자들의 직무매칭력 및 협업행위에 있어서의 다양한 용어와 정의와 개념들의 제각각으로 부터 발생되는 상치(相馳, 제각각, 불통, 협업마찰, 대립, 갈등, 등)력을 해소하는 것이 역설적으로 협업력을 최적으로 활성화 할 수 있다는 사실을 모두가 인지, 수용토록 하고자 함을 목적으로한다. 이러한 연구를 전벤처단계의 벤처기초학을 바탕으로하는 제벤처생태계 개념을 일원화 통합화하는 연장에 전개되는 공통무장교육 모델화를 목적으로한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.116-122
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• 2010

As the importance of HCI(Human-Computer Interface) caused by dramatically developed computer technology is getting high, the requirement for the design of human friendly systems is also getting high. First of all, the personality and Emotional factor should be considered for implementing more human friendly systems. Many studies on Knowledge, Emotion and personality have been made, but the combined methods connecting these three factors is not so many investigated yet. It is known that memorizing process includes not only knowledge but also the emotion and the emotion state has much effects on the process of reasoning and decision making step. Accordingly, for implementing more human friendly efficient sophisticated intelligent system, the system considering these three factors should be modeled and designed. In this paper, knowledge-emotion reaction model was designed. Five types are defined for representing the personality and emotion reaction mechanism calculating emotion vector based on the extracted Thought threads by Type matching selection was proposed. This system is applied to the virtual memory and its emotional reactions are simulated.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.570-579
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• 2005

Facial expression recognition technology that has potentialities for applying various fields is appling on the man-machine interface development, human identification test, and restoration of facial expression by virtual model etc. Using sequential facial images, this study proposes a simpler method for detecting human facial expressions such as happiness, anger, surprise, and sadness. Moreover the proposed method can detect the facial expressions in the conditions of the sequential facial images which is not rigid motion. We identify the determinant face and elements of facial expressions and then estimates the feature regions of the elements by using information about color, size, and position. In the next step, the direction patterns of feature regions of each element are determined by using optical flows estimated gradient methods. Using the direction model proposed by this study, we match each direction patterns. The method identifies a facial expression based on the least minimum score of combination values between direction model and pattern matching for presenting each facial expression. In the experiments, this study verifies the validity of the Proposed methods.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.341-346
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• 2012

Social network service is service that enables the human network to be built up on web. It is important to record users' information simply and establish the network with people based on the information to provide with the social network service effectively. But it is very troublesome work for the user to input his or her own information on the mobile environment. In this paper we suggested a system which classifies users' behavior using context and creates blogging sentences automatically after inferring the destination. For this, users' behavior is classified and the destination is inferred with the sequence matching method using Naive Bayes classification. Then sentences which are suitable for situation is created by arranging the processed context using the structure of 5W1H. The system was evaluated satisfaction degree by comparing the created sentences based on actually collected data with users' intension and got accuracy rate of 88.73%.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.655-660
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• 2009

The context prediction algorithms are not suitable to provide real-time personalized service for users in context-awareness environment. The algorithms have problems like time delay in training data processing and the difficulties of implementation in real-time environment. In this paper, we propose a prediction algorithm with user modeling to shorten of processing time and to improve the prediction accuracy in the context prediction algorithm. The algorithm uses moving path of user contexts for context prediction and generates user model by time-series analysis of user's moving path. And that predicts the user context with the user model by sequence matching method. We compared our algorithms with the prediction algorithms by processing time and prediction accuracy. As the result, the prediction accuracy of our algorithm is similar to the prediction algorithms, and processing time is reduced by 40% in real time service environment.