• Journal of the Optical Society of Korea
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• 제14권4호
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• pp.388-394
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• 2010

In this paper the object recognition performance of a photon counting integral imaging system is quantitatively compared with that of a conventional gray scale imaging system. For 3D imaging of objects with a small number of photons, the elemental image set of a 3D scene is obtained using the integral imaging set up. We assume that the elemental image detection follows a Poisson distribution. Computational geometrical ray back propagation algorithm and parametric maximum likelihood estimator are applied to the photon counting elemental image set in order to reconstruct the original 3D scene. To evaluate the photon counting object recognition performance, the normalized correlation peaks between the reconstructed 3D scenes are calculated for the varied and fixed total number of photons in the reconstructed sectional image changing the total number of image channels in the integral imaging system. It is quantitatively illustrated that the recognition performance of the photon counting integral imaging system can be similar to that of a conventional gray scale imaging system as the number of image viewing channels in the photon counting integral imaging (PCII) system is increased up to the threshold point. Also, we present experiments to find the threshold point on the total number of image channels in the PCII system which can guarantee a comparable recognition performance with a gray scale imaging system. To the best of our knowledge, this is the first report on comparisons of object recognition performance with 3D photon counting & gray scale images.

• 동력기계공학회지
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• 제5권2호
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• pp.93-99
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• 2001

This paper presents the automatic recognition system for car number plate. In our country, two types of number plate pattern is used. The one is old type of number plate, the other is new type of number plate. To recognize both new and old type number plates, the system must have flexibility. Therefore, in this paper, automatic recognition system is developed by use of the neural network for good adaptation, good generalization, and modulation. And because the number plate is made of three codes, the multi neural network consists of three networks. Neural network is teamed by GDR(Generalized Delta learning Rule) and it is verified the effectiveness of the method through experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1718-1721
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• 1997

An image processing algorithm is developed in order to recognize the type of cars, the position of a number plate and the characters on the plate. to recognize the type of cars, comparison of two images is used. One has a car image, the other is just a background image without car. After that recognition, a vertical line filter is used to find the location of the plate. Finally the simularity mehod is used to recognize the numbers on plates.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.129-131
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• 2006

The performances of neural network systems mainly depend on the kind and the number of input patterns for its training. Hence, the kind of input patterns as well as its number is very important for the character recognition system using back-propagation network. The more input patters are used, the better the system recognizes various characters. However, training is not always successful as the number of input patters increases. Moreover, there exists a limit to consider many input patterns of the recognition system for cursive script characters. In this paper we present a new character recognition system using the back-propagation neural networks. By using an additional neural network, an input pattern generation method is provided for increasing the recognition ratio and a successful training. We firstly introduce the structure of the proposed system. Then, the character recognition system is investigated through some experiments.

• The Journal of the Acoustical Society of Korea
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• 제28권2E호
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• pp.66-71
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• 2009

In practical applications of speech recognition, one of the fundamental questions might be on the number of training data that should be provided for a specific task. Though plenty of training data would undoubtedly enhance the system performance, we are then faced with the problem of heavy cost. Therefore, it is of crucial importance to determine the least number of training data that will afford a certain level of accuracy. For this purpose, we investigate the effect of the number of training data on the speaker-independent speech recognition of isolated words by using FVQ/HMM. The result showed that the error rate is roughly inversely proportional to the number of training data and grows linearly with the vocabulary size.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.47-56
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• 2022

Recently bank card number recognition plays an important role in improving payment efficiency. In this paper we propose a new bank-card number identification algorithm. The proposed algorithm consists of three modules which include edge detection, candidate region generation, and recognition. The module of 'edge detection' is used to obtain the possible digital region. The module of 'candidate region generation' has the role to expand the length of the digital region to obtain the candidate card number regions, i.e. to obtain the final bank card number location. And the module of 'recognition' has Convolutional deep learning Neural Network (CNN) to identify the final bank card numbers. Experimental results show that the identification rate of the proposed algorithm is 95% for the card numbers, which shows 20% better than that of conventional algorithm or method.

• 전기전자학회논문지
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• 제25권1호
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• pp.25-31
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• 2021

일반적인 신용카드 번호 인식 시스템은 정해진 위치에 카드를 배치했을 때에만 올바르게 동작하도록 설계되어 있다. 본 논문은, 저시력 장애인을 포함한 시력 취약 계층에게 보다 쉬운 사용자 경험을 제공하기 위해, 신용카드 내 16자리 숫자의 종횡비 특징을 이용한 자동 번호 인식 알고리즘을 제안한다. 제안하는 알고리즘은 형태학 연산을 통해 종횡비가 4:1 이상인 이미지 후보군을 찾고, 각각의 후보에 OCR과 BIN 번호 매칭 기술을 적용하여 신용카드 번호를 획득한다. OpenCV 및 Firebase ML에 기반한 실험 결과, 카드를 정해진 위치에 두지 않아도 77.75% 정확도로 카드 번호를 인식하였다.

• 한국전자통신학회논문지
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• 제15권2호
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• pp.299-306
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• 2020

신경망을 이용한 영상인식은 여러 분야에 널리 사용되고 있다. 본 연구에서는 차량 번호 인식 및 특정 구역 입출 시 통제에 필요한 인가/비인가 차량 인식 시스템을 연구하였다. 이 시스템은 영상을 인식하는 기능을 갖추고 있어 차량 번호에 대한 모든 정보를 확인하고, 차량 번호판을 정확히 인식할 수 있는 기능을 추가하였다. 그 밖에 신경망을 이용하여 좀 더 빠르게 차량번호를 확인할 수 있도록 하였다.

• The Journal of the Acoustical Society of Korea
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• 제17권2E호
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• pp.26-30
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• 1998

In this paper, we compose neural network predictive HMM(NNPHMM) to provide the dynamic feature of the speech pattern for the HMM. The NNPHMM is the hybrid network of neura network and the HMM. The NNPHMM trained to predict the future vector, varies each time. It is used instead of the mean vector in the HMM. In the experiment, we compared the recognition abilities of the one hundred Korean syllables according to the variation of hidden layer, state number and prediction orders of the NNPHMM. The hidden layer of NNPHMM increased from 10 dimensions to 30 dimensions, the state number increased from 4 to 6 and the prediction orders increased from 10 dimensions to 30 dimension, the state number increased from 4 to 6 and the prediction orders increased from the second oder to the fourth order. The NNPHMM in the experiment is composed of multi-layer perceptron with one hidden layer and CMHMM. As a result of the experiment, the case of prediction order is the second, the average recognition rate increased 3.5% when the state number is changed from 4 to 5. The case of prediction order is the third, the recognition rate increased 4.0%, and the case of prediction order is fourth, the recognition rate increased 3.2%. But the recognition rate decreased when the state number is changed from 5 to 6.

• 한국정보통신학회논문지
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• 제8권4호
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• pp.838-845
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• 2004

실제 주민등록증을 스캐너로 수집한 후 해당 영상에 대하여 주민등록번호를 인식하였다. 인식을 위한 전처리 과정은 처리 속도를 감안하여 대상부분을 포함 주민등록증의 약 1/8 크기만큼 분할한 후 잡음에 해당하는 홀로그램을 제거하였다. 숫자 인식 방법으로는 원형비교법과 학습법을 병행하였으며 대상 숫자의 단순한 특징 추출을 위해 클러스터링 방식을 사용하였고, 외부 환경에 따라 오인식되는 숫자에 대해 세선화 기법을 병행하여 유사한 숫자간의 유일한 특징으로 구분하였다. 인식에 대한 실험 결과, 숫자 인식에 관한 타 논문의 인식률과 비교하여 양호한 인식률이 도출되었다.