• Title, Summary, Keyword: recognition

Search Result 18,545, Processing Time 0.169 seconds

Noise Removal using a Convergence of the posteriori probability of the Bayesian techniques vocabulary recognition model to solve the problems of the prior probability based on HMM (HMM을 기반으로 한 사전 확률의 문제점을 해결하기 위해 베이시안 기법 어휘 인식 모델에의 사후 확률을 융합한 잡음 제거)

  • Oh, Sang-Yeob
    • Journal of Digital Convergence
    • /
    • v.13 no.8
    • /
    • pp.295-300
    • /
    • 2015
  • In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

Vocabulary Recognition Retrieval Optimized System using MLHF Model (MLHF 모델을 적용한 어휘 인식 탐색 최적화 시스템)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
    • /
    • v.14 no.10
    • /
    • pp.217-223
    • /
    • 2009
  • Vocabulary recognition system of Mobile terminal is executed statistical method for vocabulary recognition and used statistical grammar recognition system using N-gram. If limit arithmetic processing capacity in memory of vocabulary to grow then vocabulary recognition algorithm complicated and need a large scale search space and many processing time on account of impossible to process. This study suggest vocabulary recognition optimize using MLHF System. MLHF separate acoustic search and lexical search system using FLaVoR. Acoustic search feature vector of speech signal extract using HMM, lexical search recognition execution using Levenshtein distance algorithm. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%, represent recognition speed of 1.61 second.

  • PDF

Performance Analysis of Face Recognition by Distance according to Image Normalization and Face Recognition Algorithm (영상 정규화 및 얼굴인식 알고리즘에 따른 거리별 얼굴인식 성능 분석)

  • Moon, Hae-Min;Pan, Sung Bum
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.23 no.4
    • /
    • pp.737-742
    • /
    • 2013
  • The surveillance system has been developed to be intelligent which can judge and cope by itself using human recognition technique. The existing face recognition is excellent at a short distance but recognition rate is reduced at a long distance. In this paper, we analyze the performance of face recognition according to interpolation and face recognition algorithm in face recognition using the multiple distance face images to training. we use the nearest neighbor, bilinear, bicubic, Lanczos3 interpolations to interpolate face image and PCA and LDA to face recognition. The experimental results show that LDA-based face recognition with bilinear interpolation provides performance in face recognition.

Primitive Body Model Encoding and Selective / Asynchronous Input-Parallel State Machine for Body Gesture Recognition (바디 제스처 인식을 위한 기초적 신체 모델 인코딩과 선택적 / 비동시적 입력을 갖는 병렬 상태 기계)

  • Kim, Juchang;Park, Jeong-Woo;Kim, Woo-Hyun;Lee, Won-Hyong;Chung, Myung-Jin
    • The Journal of Korea Robotics Society
    • /
    • v.8 no.1
    • /
    • pp.1-7
    • /
    • 2013
  • Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding, which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data, while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.

Proposed Efficient Architectures and Design Choices in SoPC System for Speech Recognition

  • Trang, Hoang;Hoang, Tran Van
    • Journal of IKEEE
    • /
    • v.17 no.3
    • /
    • pp.241-247
    • /
    • 2013
  • This paper presents the design of a System on Programmable Chip (SoPC) based on Field Programmable Gate Array (FPGA) for speech recognition in which Mel-Frequency Cepstral Coefficients (MFCC) for speech feature extraction and Vector Quantization for recognition are used. The implementing process of the speech recognition system undergoes the following steps: feature extraction, training codebook, recognition. In the first step of feature extraction, the input voice data will be transformed into spectral components and extracted to get the main features by using MFCC algorithm. In the recognition step, the obtained spectral features from the first step will be processed and compared with the trained components. The Vector Quantization (VQ) is applied in this step. In our experiment, Altera's DE2 board with Cyclone II FPGA is used to implement the recognition system which can recognize 64 words. The execution speed of the blocks in the speech recognition system is surveyed by calculating the number of clock cycles while executing each block. The recognition accuracies are also measured in different parameters of the system. These results in execution speed and recognition accuracy could help the designer to choose the best configurations in speech recognition on SoPC.

Low-Quality Banknote Serial Number Recognition Based on Deep Neural Network

  • Jang, Unsoo;Suh, Kun Ha;Lee, Eui Chul
    • Journal of Information Processing Systems
    • /
    • v.16 no.1
    • /
    • pp.224-237
    • /
    • 2020
  • Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

Machine Printed Character Recognition Based on the Combination of Recognition Units Using Multiple Neural Networks (다중 신경망을 이용한 인식단위 결합 기반의 인쇄체 문자인식)

  • Lim, Kil-Taek;Kim, Ho-Yon;Nam, Yun-Seok
    • The KIPS Transactions:PartB
    • /
    • v.10B no.7
    • /
    • pp.777-784
    • /
    • 2003
  • In this Paper. we propose a recognition method of machine printed characters based on the combination of recognition units using multiple neural networks. In our recognition method, the input character is classified into one of 7 character types among which the first 6 types are for Hangul character and the last type is for non-Hangul characters. Hangul characters are recognized by several MLP (multilayer perceptron) neural networks through two stages. In the first stage, we divide Hangul character image into two or three recognition units (HRU : Hangul recognition unit) according to the combination fashion of graphemes. Each recognition unit composed of one or two graphemes is recognized by an MLP neural network with an input feature vector of pixel direction angles. In the second stage, the recognition aspect features of the HRU MLP recognizers in the first stage are extracted and forwarded to a subsequent MLP by which final recognition result is obtained. For the recognition of non-Hangul characters, a single MLP is employed. The recognition experiments had been performed on the character image database collected from 50,000 real letter envelope images. The experimental results have demonstrated the superiority of the proposed method.

Development of a Visitor Recognition System Using Open APIs for Face Recognition (얼굴 인식 Open API를 활용한 출입자 인식 시스템 개발)

  • Ok, Kisu;Kwon, Dongwoo;Kim, Hyeonwoo;An, Donghyeok;Ju, Hongtaek
    • KIPS Transactions on Computer and Communication Systems
    • /
    • v.6 no.4
    • /
    • pp.169-178
    • /
    • 2017
  • Recently, as the interest rate and necessity for security is growing, the demands for a visitor recognition system are being increased. In order to recognize a visitor in visitor recognition systems, the various biometric methods are used. In this paper, we propose a visitor recognition system based on face recognition. The visitor recognition system improves the face recognition performance by integrating several open APIs as a single algorithm and by performing the ensemble of the recognition results. For the performance evaluation, we collected the face data for about five months and measured the performance of the visitor recognition system. As the results of the performance measurement, the visitor recognition system shows a higher face recognition rate than using a single face recognition API, meeting the requirements on performance.

A Study on Influential Variables Related to Home Management Ability of Urban Home Makers (도시 주부의 가정관리 능력의 제 영향 변인에 관한 연구)

  • 이정우;오경희
    • Journal of Korean Home Management Association
    • /
    • v.9 no.2
    • /
    • pp.1-18
    • /
    • 1991
  • The purpose of this study is to find out influential variables related to Home Management Ability of urban home makers. This study focuses on the following aspects; 1) to find out which variables of sociodemographic variables (ie. home maker's age, level of education-husband, wife, job-husband, wife, income, duration of marriage), of psychological variables (ie. degree of resourcefulness recognition, degree of stress recognition, degree of life level recognition) have significant effects on home management ability. 2) to find out which variables of sociodemographic variables have significant effects on degree of resourcefulness recognition, of stress recognition, and of life level recognition. 3) to identify the influence of significant variables related to home management ability. Data was analyzed by frequency. percentage, mean , F-test, t-test, Duncan's multiple range test. regression analysis , path analysis pearson's r. x2-test. Major findings are as follows; 1) The level of education (husband , wife)and occupation of husband were variables to have influences on home management ability. 2) a. The level of education (husband, wife) and income were variable to have influences on degree of resourcefulness recognition. b. The employment of home makers. income, and the form of family were variables to have influences on degree of stress recognition. c. The level of education (husband, wife) occupation of husband , income , and duration of marriage were variables to have influences on degree of life level recognition. 3) There were significant relationships between home management ability and degree of resourcefulness recognition and of stress recognition (r=0.13, r=-0.12, p<.05). a. The higher degree of resourcefulness recognition, the higher home management ability (x2=11.17. df=4. p<.05) b. The higher degree of stress recognition, the lower home n=management ability (x2=14.64. df=4. p<.01) 4) The education level of homemakers (β =0.15) and income (β=0.12) were variables to have indirect influences on home management ability through the medium of the degree of resourcefulness recognition (β =0.13) 5) The employment of home makers (β=-0.17) was a variable to have indirect influence on home management ability through the medium of the degree of stress recognition(β=-0.12) 6) the education level of husband (β=0.16) and income (β=0.32) were variables to have direct influence on degree of life level recognition. 7) The degree of life level recognition (β=0.13) and education level of home makers (β=0.17) were variables to have indirect influences on home management ability through the medium of the degree of resourcefulness recognition (β=0.13) 8)The degree of life level recognition (β=-0.22) the employment of home makers(β=-0.17) and the from of family(β=-0.10) were variables to have indirect influences on home management ability through the medium of the degree of stress recognition.

  • PDF

Improve Digit Recognition Capability of Backpropagation Neural Networks by Enhancing Image Preprocessing Technique

  • Feng, Xiongfeng;Kubik, K.Bogunia
    • 제어로봇시스템학회:학술대회논문집
    • /
    • /
    • pp.49.4-49
    • /
    • 2001
  • Digit recognition based on backpropagation neural networks, as an important application of pattern recognition, was attracted much attention. Although it has the advantages of parallel calculation, high error-tolerance, and learning capability, better recognition effects can only be achieved with some specific fixed format input of the digit image. Therefore, digit image preprocessing ability directly affects the accuracy of recognition. Here using Matlab software, the digit image was enhanced by resizing and neutral-rotating the extracted digit image, which improved the digit recognition capability of the backpropagation neural network under practical conditions. This method may also be helpful for recognition of other patterns with backpropagation neural networks.

  • PDF