• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.111-118
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• 2004

In this paper, an adaptive multilevel successive algorithm is presented. The algorithm introduces an adaptive initial level scheme to the conventional multilevel successive algorithm (MSEA). It efficiently removes the unnecessary computations required for judging the invalid candidate blocks at redundant level. The simulation results show that the proposed algorithm obtains the optimal motion vector with reduced computations compared to MSEA.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.377-382
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• 2012

In this paper, the echo noise robust CHMM learning model using echo cancellation average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise. For improving the performance of a continuous speech recognition, CHMM models were constructed using echo noise cancellation average estimator LMS algorithm. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 1.93dB, recognition rate improved as 2.1%.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.98-113
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• 2002

In this paper, we present advanced algorithms to reduce the computations of block matching algorithms for motion estimation in video coding. Advanced multi-level successive elimination algorithms(AMSEA) are based on the Multi-level successive elimination algorithm(MSEA)[1]. The first algorithm is that when we calculate the sum of absolute difference (SAD) between the sum norms of sub-blocks in MSEA, we use the partial distortion elimination technique. By using the first algorithm, we can reduce the computations of MSEA further. In the second algorithm, we calculate SAD adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in SAD calculation can occur early. So, the computations of MSEA can be reduced. In the third algorithm, we can estimate the elimination level of MSEA. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is a very fast block matching algorithm with nearly 100% motion estimation accuracy. Experimental results show that AMSEA are very efficient algorithms for the estimation of motion vectors.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.1043-1052
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• 2003

In this paper, we present a fast motion estimation algorithm to reduce the computations of block matching algorithm for motion estimation in video coding. The proposed algorithm is based on Multi-level Successive Elimination Algorithm and Efficient Multi-level Successive Elimination Algorithms. The best estimate of the motion vectors can be obtained by hierarchical search point sampling and thus the proposed algorithm can decrease the number of matching evaluations that require very intensive computations. The efficiency of the proposed algorithm was verified by experimental results.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.755-760
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• 2004

In this paper, an advanced successive elimination algorithm was proposed using mean absolute difference of neighboring search points. By using mean absolute difference of neighboring search points, the search point in motion estimation can be eliminated effeciently without matching evaluation that requires very intensive computations. By using adaptive MAD calculation algorithm, the candidate matching block can be eliminated early. So, the number of the proposed algrorithm was verified by experimental results.

• Journal of Korea Multimedia Society
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• v.8 no.7
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• pp.889-897
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• 2005

This paper presents a new fast motion estimation algorithm for multi-reference frames. We first analyze the experimental results of the successive elimination algorithm, which is a fast version of full search algorithm, being applied to Multi-reference frames. Based on the analysis, a new scheme for alleviating its computational burden is introduced. In the proposed method, the motion vector for the immediately previous reference frame is found by applying the successive elimination algorithm, while the motion vector for other reference frames is estimated by extrapolation of the already obtained motion vector. Adaptively restricting the motion search area to the local area centered on the estimated motion vector, the proposed method provides dramatic computational complexity reduction but slight quality degradation. The proposed method is evaluated by experiments for some image sequences.

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.761-767
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• 2003

In this paper, A Fast Multi-level Successive Elimination Algorithm (FMSEA) is presented for block matching motion estimation in video coding. Motion estimation accuracy of FMSEA is equal to that of Multilevel Successive Elimination Algorithm(MSEA). FMSEA can reduce the computations for motion estimation of MSEA by using partial distortion elimination technique. The efficiency of the proposed algorithm was verified by experimental results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.561-564
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• 2006

오늘날 컴퓨터와 데이터 통신의 급속한 발달로 인해 멀티미디어 정보통신 기술이 비약적으로 발전하고 있다. 이러한 멀티미디어 데이터 중에서 동영상은 다른 데이터 형태에 비해 정보량이 매우 방대하다. 따라서 동영상을 처리하는 시스템에서는 압축 기법이 매우 중요한 역할을 차지한다. 이에 본 논문은 연속제거 알고리즘을 기반으로 이전블록 초기 움직임 벡터 사용 및 strip단위 블록 합을 통하여 고속의 움직임 추정을 통해 영상을 복호화 하였고, 기존의 완전탐색 블록 정합방식과 영상 복원 능력 및 연산량을 비교 평가하였다. 뿐만 아니라 이후 이를 바탕으로 고속 움직임 추정 모듈을 VHDL로 구현하여 본 논문의 프로그램을 성능평가의 기준으로 이용할 것이다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.180-183
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• 2012

본 논문에서는 고속 움직임 예측(Fast Motion Estimation)방법의 일종인 다단계 연속 제거 알고리즘(MSEA : Multi-level Successive Elimination Algorithm)에 움직임의 역동성 정도를 고려하여 적응적인 가중치를 적용하는 방안에 대해 제안하였다. 움직임을 예측하는 과정에서 영상의 화질 손상이 발생하는 방식(Lossy Motion Estimation Algorithm)에서 모든 단위 블록(Macro Block)에 고정된 가중치만을 적용하는 기존의 방식과 달리 주위 블록의 움직임 벡터(Motion Vector)를 통해 움직임의 정도를 가정하여 적응적인 가중치를 적용함으로써 화질 손상을 줄이는 것이 목적이다. 제안하는 알고리즘으로 설계한 실험으로부터 MSEA에 적응적 가중치를 사용할 경우의 효율성을 확인하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.275-281
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• 1993

본 발표에서는 신경회로망을 이용하여 연속음성중에서 키워드를 인식하는 방법을 설명한다. 연속음성에서 파형소편 및 음절을 식별하는 휴리스틱 알고리즘을 개발하였고, 연속음성을 음절단위로 파형소편 스펙트럼분석(선형예측법)으로 특성치를 추출하였다. 음절의 특성치는 코호넨 신경회로망을 통하여 학습을 시켰으며, 연속음성중 키워드인식은 먼저 음절을 인식하여 단어를 찾고, 인식된 단어가 키워드와 일치하는가를 확인한다. 본 연구의 의의는 파형소편 및 음절식별 알고리즘을 통하여, 크기불변성(Scaling invariance), 시간불변성(Time warping 및 Time-shift invariance), 중복성제거의 문제점을 해결하였고, 신경회로망의 학습을 통하여 화자독립적인 연속음성인식시스템 구축의 기반을 확립한데 있다. 본 음성인식모델은 학교구내 전화번호 안내시스템으로 활용단계에 있으며 전화번호뿐만아니라 주소안내시스템으로도 활용될 예정이다. 또한 자동차 운전보조시스템 및 주행안내시스템의 음성명령에 응용될 수 있는데, 예로 음성명령은 "핸들 좌로 20도", "시청까지 주행", "시청 지도안내"등이 될 수 있다. 현재 자동차 운전보조시스템은 컴퓨터 화면상 모의동작시스템으로 운영되고 있다. 본 음성인식모델은 화자종속시 90%이상, 화자독립시 70%의 인식결과를 보였다.시 90%이상, 화자독립시 70%의 인식결과를 보였다.