• 제어로봇시스템학회논문지
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• 제3권5호
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• pp.511-519
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• 1997

This paper presents the automatic recognition algorithm of the license number in on vehicle image. The proposed algorithm uses the correlation coefficient and Hough transform to detect license plate. The m/n ratio reduction is performed to save time and memory. By the correlation coefficient between the standard pattern and the target pattern, licence plate area is roughly extracted. On the extracted local area, preprocessing and binarization is performed. The Hough transform is applied to find the extract outline of the plate. If the detection fails, a smaller or a larger standard pattern is used to compute the correlation coefficient. Through this process, the license plate of different size can be extracted. Two algorithms to each separate number are proposed. One segments each number with projection-histogram, and the other segments each number with the label. After each character is separated, it is recognized by the neural network. This research overlomes the problems in conventional methods, such as the time requirement or failure in extraction of outlines which are due to the processing of the entire image, and by processing in real time, the practical application is possible.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 추계학술발표대회
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• pp.1534-1536
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• 2015

동영상에서 학습을 통한 특정인의 탐지는 한 개의 프레임 당 많은 시간이 소모된다. 따라서 특정인의 위치를 파악하면서도 탐색할 프레임의 수를 줄이는 것이 필요하다. 우리는 관찰되는 장소의 특징을 이용하여 일정 범위를 생략하는 탐색 기법을 제안하고 성능을 측정하였다. 실험결과 제안한 방법에서 어느 정도 유의미한 결과를 얻을 수 있었다.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• 한국인공지능학회지
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• 제11권1호
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• pp.17-24
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• 2023

With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.

• 대한음성학회지:말소리
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• 제52호
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• pp.111-120
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• 2004

This paper focuses on the DSP implementation of an HMM-based speech recognizer that can handle several hundred words of vocabulary size as well as speaker independency. First, we develop an HMM-based speech recognition system on the PC that operates on the frame basis with parallel processing of feature extraction and Viterbi decoding to make the processing delay as small as possible. Many techniques such as linear discriminant analysis, state-based Gaussian selection, and phonetic tied mixture model are employed for reduction of computational burden and memory size. The system is then properly optimized and compiled on the TMS320C6711 DSP for real-time operation. The implemented system uses 486kbytes of memory for data and acoustic models, and 24.5 kbytes for program code. Maximum required time of 29.2 ms for processing a frame of 32 ms of speech validates real-time operation of the implemented system.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2003년도 하계학술대회 논문집
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• pp.102-105
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• 2003

Up to the present studies of the car number recognition system, it is generally known to have serious problems such as relatively long processing time due to the excessive length of data extracted from the number plate based on the current image characteristics, and the image blurring with the physical damage of the brightness and darkness signals of the number plate caused by external impulses with many difficulties in the extraction of the highlighted numbers. In this Paper we used the characteristics firstly having a constant brightness of number plate, and a high density to the horizontal axis, and the influences of highlighted signal could be reduced by making reflections less through the polarized filter on the camera for any highlighted signal. For the more, the data processing time and the noise reduction are effectively implemented by using the wavelet transform of time-space scale with the considerations on the physical loss and processing time.

• 전자공학회논문지CI
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• 제44권5호
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• pp.20-27
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• 2007

본 논문에서는 RFID 시스템에 사용되는 무선 접속 프로토콜 중 UHF 대역 프로토콜인 ISO 18000-6의 Type A와 B 의 에어인터페이스를 분석하고, 프로토콜에서 규정된 충돌방지 알고리즘을 구현하였다. 또한 표준 프로토콜의 성능을 개선한 ISO 18000-6의 Type A와 B 충돌방지 알고리즘을 제안하고, 그 성능을 비교하였다. 개선된 알고리즘과 표준 알고리즘의 성능을 시뮬레이션을 통하여 비교한 결과, 개선된 ISO 18000-6 Type A 알고리즘의 총 인식시간 감소율은 태그 수가 100개 일 때 35.2%, 태그 수 1000개 일 때 24.3 % 감소하였다. 그리고 개선된 ISO 18000-6 Type B 알고리즘의 총 인식시간 감소율은 태그 수 100개 일 때 6.0 %, 태그 수 1000개 일 때 6.6 % 감소하였다. 따라서 본 논문에서 제안한 개선된 충돌방지 알고리즘은 RFID 시스템의 태그 인식성능을 개선하는 방안으로 적용될 수 있다.

• ETRI Journal
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• 제29권3호
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• pp.292-299
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• 2007

In this paper, we present a speech recognition system using a throat microphone. The use of this kind of microphone minimizes the impact of environmental noise. Due to the absence of high frequencies and the partial loss of formant frequencies, previous systems using throat microphones have shown a lower recognition rate than systems which use standard microphones. To develop a high performance automatic speech recognition (ASR) system using only a throat microphone, we propose two methods. First, based on Korean phonological feature theory and a detailed throat signal analysis, we show that it is possible to develop an ASR system using only a throat microphone, and propose conditions of the feature extraction algorithm. Second, we optimize the zero-crossing with peak amplitude (ZCPA) algorithm to guarantee the high performance of the ASR system using only a throat microphone. For ZCPA optimization, we propose an intensification of the formant frequencies and a selection of cochlear filters. Experimental results show that this system yields a performance improvement of about 4% and a reduction in time complexity of 25% when compared to the performance of a standard ZCPA algorithm on throat microphone signals.

• 한국컴퓨터정보학회논문지
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• 제9권4호
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• pp.85-91
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• 2004

최근 들어 인간의 의지를 컴퓨터에 전달하기 위한 수단으로 컴퓨터 시각기반 방식으로 제스처를 인식하고자 하는 연구가 널리 진행되고 있다. 제스처 인식에서 가장 중요한 이슈는 알고리즘의 단순화와 처리 시간의 감소이다. 이러한 문제를 해결하기 위하여 본 연구에서는 기하학적 집합론에 근거하고 있는 수학적 형태론을 적용하였다. 본 논문에서 제안한 알고리즘의 키 아이디어는 형태론적 형상분해를 적용하여 얻은 원시형상 요소들의 중심점을 연결하는 궤적을 추적하는 것이다. 핸드 제스처 시퀀스의 중심점 궤적은 핸드 제스처의 형상에 관련된 중요한 정보를 내포하고 있다. 이러한 특징에 근거하여 본 연구에서는 원시형상 요소들의 중심점 궤적과 직접적으로 관련되는 체인코드 에지벡터로부터 형상의 특징벡터를 계산하여 핸드 제스처 시퀀스를 인식할 수 있는 알고리즘을 제안하고, 실험을 통하여 그 유용성을 증명하였다.