• 대한전자공학회논문지SP
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• 제46권2호
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• pp.78-88
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• 2009

본 논문에서는 LDA를 이용한 얼굴 인식에서 발생하는 small sample size 문제를 해결하기 위한 효율적인 방법인 resampling 방법을 제안한다. 기존에는 regularization method를 사용하여 small sample size 문제를 해결하였는데, 이 방법을 사용하면 클래스내 분산행렬의 특이성을 없앨 수 있지만, 클래스내 분산행렬과 상수를 곱하는 과정에서 상수 값을 임의로 정해 주어야 하고, 이 상수 값에 따라 인식률이 개선되지 않을 수 있다는 문제점이 발생한다. 제안된 resampling 방법을 이용하여 학습 데이터의 수를 늘리면, regularization method보다 개선된 인식률을 얻을 수 있고, 또한 경험적으로 상수 값을 지정해 주는 과정을 거치지 않아도 되는 장점이 있다.

• 대한기계학회논문집B
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• 제26권10호
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• pp.1445-1450
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• 2002

In the present study, a new pattern recognition algorithm was proposed to size spray particles using the boundary curvature information. Conceptually, this algorithm has an advantage over the others because it can identify the particle size and shape simultaneously, and also can separate the overlapped particles more effectively. Curvature of a boundary was obtained from the change of the slopes of two neighboring segments at the corresponding part. The algorithm developed in this study was tested by using an artificially prepared image of a group of spherical particles which were either isolated or overlapped. Particle sizes obtained from the measured curvatures agreed well with the true values. By detecting abrupt changes of the curvature along the image boundary, the element particles could be separated out from their overlapped images successfully.

• 융합보안논문지
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• 제19권1호
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• pp.67-78
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• 2019

본 논문에서는 가변 크기 블록 기반의 새로운 얼굴 특징 표현 방법을 제안한다. 기존 외형 기반의 얼굴 표정 인식 방법들은 얼굴 특징을 표현하기 위해 얼굴 영상 전체를 균일한 블록으로 분할하는 uniform grid 방법을 사용하는데, 이는 다음 두가지 문제를 가지고 있다. 얼굴 이외의 배경이 포함될 수 있어 표정을 구분하는 데 방해 요소로 작용하고, 각 블록에 포함된 얼굴의 특징은 입력영상 내 얼굴의 위치, 크기 및 방위에 따라 달라질 수 있다. 본 논문에서는 이러한 문제를 해결하기 위해 유의미한 표정변화가 가장 잘 나타내는 블록의 크기와 위치를 결정하는 가변 크기 블록 방법을 제안한다. 이를 위해 얼굴의 특정점을 추출하여 표정인식에 기여도가 높은 얼굴부위에 대하여 블록 설정을 위한 기준점을 결정하고 AdaBoost 방법을 이용하여 각 얼굴부위에 대한 최적의 블록 크기를 결정하는 방법을 제시한다. 제안된 방법의 성능평가를 위해 LDTP를 이용하여 표정특징벡터를 생성하고 SVM 기반의 표정 인식 시스템을 구성하였다. 실험 결과 제안된 방법이 기존의 uniform grid 기반 방법보다 우수함을 확인하였다. 특히, 제안된 방법이 형태와 방위 등의 변화가 상대적으로 큰 MMI 데이터베이스에서 기존의 방법보다 상대적으로 우수한 성능을 보여줌으로써 입력 환경의 변화에 보다 효과적으로 적응할 수 있음을 확인하였다.

• 지능정보연구
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• 제9권2호
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• pp.1-18
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• 2003

The automatic recognition of transport containers using image processing is very hard because of the irregular size and position of identifiers, diverse colors of background and identifiers, and the impaired shapes of identifiers caused by container damages and the bent surface of container, etc. We proposed and evaluated the novel recognition algorithm of container identifiers that overcomes effectively the hardness and recognizes identifiers from container images captured in the various environments. The proposed algorithm, first, extracts the area including only all identifiers from container images by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper and by applying contour tracking method to the binarized area, container identifiers which are targets of recognition are extracted. We proposed and applied the ART2-based RBF network for recognition of container identifiers. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm has more improved performance in the extraction and recognition of container identifiers than the previous algorithms.

• 전자공학회논문지B
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• 제32B권8호
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• pp.1110-1118
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• 1995

Over the past few years, a wide variety of techniques have been developed which make a reliable recognition of speech signal. Multi-layer perceptron(MLP) which has excellent pattern recognition properties is one of the most versatile networks in the area of speech recognition. This paper describes an automatic speech recognition system which use both MLP and lexicon. In this system., the recognition is performed by a network search algorithm which matches words in lexicon to MLP output scores. We also suggest a recognition algorithm which incorperat durational information of each phone, whose performance is comparable to that of conventional continuous HMM(CHMM). Performance of the system is evaluated on the database of 26 vocabulary size from 9 speakers. The experimental results show that the proposed algorithm achieves error rate of 7.3% which is 5.3% lower rate than 12.6% of CHMM.

• 전자공학회논문지C
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• 제35C권8호
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• pp.60-67
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• 1998

We studied the effects of fractional fourier transform in face recognition, in which only the amplitude spectra of transformed facial images were used.We used two recently developed face recognition methods, the most effective feature (MEF) method (i.e., eigenface method) and most discriminating feature (MDF) method, and the effects of th etransform for th etwo methods were consistent. We confirmed that the recognition rate by the use of MDF method is better than that consistent. We confirmed that the recognition rate by the use of MDF method is better than that by MEF regardless of the order to transform, these methods provided slightly better results when the order was 1 than for any other order values. Only when the order was close to 1, the recognition rates were robust to the shift of the input images, and the trend that the recognition rates decreased as the input size varied was independent of the order. From these results, we fond that it is most advantageous to use the amplitude spectra of the conventional fourier transform whose order is 1.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2001년도 추계학술발표대회 논문집 제20권 2호
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• pp.57-60
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• 2001

This paper reports on speaker-Recognition of context independence-speaker recognition in the field of the speech recognition. It is important to select the parameter reflecting the characteristic of each single person because speaker-recognition is to identify who speaks in the database. We used Mel Frequency Cesptrum Coefficient and Vector Quantization to identify in this paper. Specially, it considered to find characteristic-vector of the speaker in different from known method; this paper used the characteristic-vector which is selected in MFCC Parameter Space. Also, this paper compared the recognition rate according to size of codebook from this database and the time needed for operation with the existing one. The results is more improved $3\sim4\%$ for recognition rate than established Vector Quantization Algorithm.

• Journal of information and communication convergence engineering
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• 제2권3호
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• pp.177-181
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• 2004

In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.807-818
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• 2022

In order to further improve the accuracy and time efficiency of behavior recognition in intelligent monitoring scenarios, a human behavior recognition algorithm based on YOLO combined with LSTM and CNN is proposed. Using the real-time nature of YOLO target detection, firstly, the specific behavior in the surveillance video is detected in real time, and the depth feature extraction is performed after obtaining the target size, location and other information; Then, remove noise data from irrelevant areas in the image; Finally, combined with LSTM modeling and processing time series, the final behavior discrimination is made for the behavior action sequence in the surveillance video. Experiments in the MSR and KTH datasets show that the average recognition rate of each behavior reaches 98.42% and 96.6%, and the average recognition speed reaches 210ms and 220ms. The method in this paper has a good effect on the intelligence behavior recognition.

• 대한전자공학회논문지SP
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• 제42권1호
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• pp.17-23
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• 2005

본 논문에서는 얼굴영역에 존재하는 특정영역인 분할된 머리, 이마, 눈, 귀, 코, 입, 턱의 슈퍼 상태에서 임베디드 데이터를 이용하여 얼굴인식 방법을 제안한다. 제안된 방법에서는 정규화된 크기(92×112)에서 특정영역인 슈퍼 상태를 정의하고, 분할된 슈퍼 상태의 내부요소인 임베디드 데이터만을 추출하여 PCA 알고리듬으로 얼굴인식을 수행한다. 제안된 방법에서는 원래영상을 모두 학습하는 것이 아니라 분할된 임베디드 데이터만을 학습시키기 때문에 제안된 영상의 크기(92×112)에서 특정 데이터를 받아들일 수 있다. 그리고 평균적으로 92×112 크기의 영상에서는 99.05%, 단계1은 99.05%, 단계2는 98.93%, 단계3은 98.54%, 단계4는 97.85%의 얼굴인식률을 보였다. 따라서 실험을 통하여 제안된 방법은 얼굴영상의 정보를 축소할 뿐만 아니라 처리속도도 향상됨을 보였다.