• Journal of the KSME
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• v.44 no.4
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• pp.63-68
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• 2004

이 글에서는 로봇에 사용할 수 있는 상호작용 기술들이 아직 초기연구 단계에 머무르고 있어 기존의 얼굴인식과 음성인식 기술동향에 대해 간략하게 소개하고 미국 CMU의 Human-Computer Interaction Institute(HCII)에서 진행 중인 ACT-R(Adaptive Character of Thought)' 프로젝트를 통해 보다 자연스러운 인간-로봇 상호작용의 개념을 소개해한다.

• Journal of IKEEE
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• v.18 no.1
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• pp.1-7
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• 2014

In this paper, we suggest an improved Viola-Jones detection framework for the efficient feature selection and the fast rejection method of the sub-window. Our object detector has low computational complexity because it rejects sub-windows until specific threshold. Owing to using same framework, detection performance is same with the existing Viola-Jones detector. We measure the number of average feature calculation about MIT-CMU test set. As a result of the experiment, the number of average feature calculation is reduced to 45.5% and the detection speed is improved about 58.5% compared with the previous algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.455-457
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• 2003

Pervasive 컴퓨팅은 다양한 분야에서 다양한 방향으로 연구가 진행 중에 있다. 제안된 모델 중에 CMU에서 제안한 위탁형 컴퓨팅 모델은 앞으로의 연구에 대한 한 방향을 제시하고 있다. 이 모델은 사용자가 요청하는 작업을 휴대하는 컴퓨터에서 처리하는 것이 아니라 주위에 뛰어난 성능을 가진 컴퓨터에게 작업을 위탁하는 방법이다. 이 방법은 기존 단말에서 작업을 처리하는 것에 비해 뛰어난 성능을 보이지만 위탁 컴퓨터에 의한 공격에 취약한 단점을 가지고 있다. 본 논문에서는 이러한 단점을 보완하기 위해서 Virtual Private Computing이라고 하는 개념을 제안 한다.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.68-71
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• 1988

The purpose of this paper is to compare with the simulated results of two control algorithms control algorithm in the real time, based upon the model. These control schemes are "Computed-torque" and "Feedforward-Dynamics compensation", and have been simulated on the CMU DD Arm II.

• Electronics and Telecommunications Trends
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• v.5 no.2
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• pp.63-77
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• 1990

HMM(Hidden Markov Model)은 음성을 기술하는데 적합한 model이다. 본 고는 최근 CMU에서 개발한 HMM에 기반을 둔 화자독립, 연속음성 system인 SPIHNX에 대하여 기술한다. SPHINX는 단순한 음소의 HMM model을 적용한 baseline SPHINX로부터 시작하여 새로운 지식의 추가 및 음성단위의 조정 등을 통하여 지속적으로 그 성능이 개선되어 왔다. SPHINX의 최종 version은 어휘 약 1000단어 정도의 재원 관리에 관한 질문 형태의 문장을 인식하는데 96%의 높은 인식율을 보인다. SPHINX는 가장 발전된 음성인식 시스템의 하나이며 이는 화자독립, 대용량어휘의 연속음성 인식 시스템의 실현 가능성을 제시한다.

• TTA Journal
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• s.85
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• pp.138-148
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• 2003

본 고에서는 유비쿼터스, 퍼베이시브(pervasive), invisible computing 등 연구기관과 연구자 사이에서 서로 다르게 명명되는 용어를 퍼베이시브 컴퓨팅으로 통일하여 명명하였다. DARPA에서 지원하는 퍼베이시브 컴퓨팅 관련 미국 내 주요 대학의 프로젝트는 MIT의 Oxygen, UC Berkeley의 Endeavour, Washington 대학의 Portolano, Georgia Tech & OGI 의 Infosphere, CMU의 Aura 프로젝트 등이 있다. 산업체에서의 가장 대표적인 경우가 IBM의 Websphere 제품군과 HP의 Cooltown이라 할 수 있다. 본 고에서는 주요 대학들의 퍼베이시브 프로젝트들을 분석하였고, 퍼베이시브 컴퓨팅의 상업적인 모델인 IBM 제품들을 분석하였다.

• Journal of KIISE
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• v.44 no.3
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• pp.295-305
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• 2017

Facial appearance is greatly influenced by illumination conditions, and therefore illumination variation is one of the factors that degrades performance of face recognition systems. In this paper, we propose a robust method for face representation under varying illumination conditions, combining non-alpha Weberface (non-alpha WF) and histogram equalization. We propose a two-step method: (1) for a given face image, non-alpha WF, which is not applied a parameter for adjusting the intensity difference between neighboring pixels in WF, is computed; (2) histogram equalization is performed to non-alpha WF, to make a uniform histogram distribution globally and to enhance the contrast. $(2D)^2PCA$ is applied to extract low-dimensional discriminating features from the preprocessed face image. Experimental results on the extended Yale B face database and the CMU PIE face database show that the proposed method yielded better recognition rates than several illumination processing methods as well as the conventional WF, achieving average recognition rates of 93.31% and 97.25%, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.91-96
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• 2015

In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.61-74
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• 2012

In this paper, we propose a illumination-robust face recognition system using MCS-LBP and 2D-PCA algorithm. A binary pattern transform which has been used in the field of the face recognition and facial expression, has a characteristic of robust to illumination. Thus, this paper propose MCS-LBP which is more robust to illumination than previous LBP, and face recognition system fusing 2D-PCA algorithm. The performance evaluation of proposed system was performed by using various binary pattern images and well-known face recognition features such as PCA, LDA, 2D-PCA and ULBP histogram of gabor images. In the process of performance evaluation, we used a YaleB face database, an extended YaleB face database, and a CMU-PIE face database that are constructed under varying lighting condition, and the proposed system which consists of MCS-LBP image and 2D-PCA feature show the best recognition accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.205-208
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• 2013

Generally, binary pattern transforms have been used in the field of the face recognition and facial expression, since they are robust to illumination. Thus, this paper proposes an illumination-robust face recognition system combining an MLDP, which improves the texture component of the LDP, and a 2D-PCA algorithm. Unlike that binary pattern transforms such as LBP and LDP were used to extract histogram features, the proposed method directly uses the MLDP image for feature extraction by 2D-PCA. The performance evaluation of proposed method was carried out using various algorithms such as PCA, 2D-PCA and Gabor wavelets-based LBP on Yale B and CMU-PIE databases which were constructed under varying lighting condition. From the experimental results, we confirmed that the proposed method showed the best recognition accuracy.