• Title/Summary/Keyword: Recognition Comparison

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Detection of Low-Level Human Action Change for Reducing Repetitive Tasks in Human Action Recognition (사람 행동 인식에서 반복 감소를 위한 저수준 사람 행동 변화 감지 방법)

  • Noh, Yohwan;Kim, Min-Jung;Lee, DoHoon
    • Journal of Korea Multimedia Society
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    • v.22 no.4
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    • pp.432-442
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    • 2019
  • Most current human action recognition methods based on deep learning methods. It is required, however, a very high computational cost. In this paper, we propose an action change detection method to reduce repetitive human action recognition tasks. In reality, simple actions are often repeated and it is time consuming process to apply high cost action recognition methods on repeated actions. The proposed method decides whether action has changed. The action recognition is executed only when it has detected action change. The action change detection process is as follows. First, extract the number of non-zero pixel from motion history image and generate one-dimensional time-series data. Second, detecting action change by comparison of difference between current time trend and local extremum of time-series data and threshold. Experiments on the proposed method achieved 89% balanced accuracy on action change data and 61% reduced action recognition repetition.

A Study on the License Plate Recognition Based on Direction Normalization and CNN Deep Learning (방향 정규화 및 CNN 딥러닝 기반 차량 번호판 인식에 관한 연구)

  • Ki, Jaewon;Cho, Seongwon
    • Journal of Korea Multimedia Society
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    • v.25 no.4
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    • pp.568-574
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    • 2022
  • In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

Performance Comparison of Various Features for Off-line Handwritten Numerals Recognition and Suggestions for Improving Recognition Rate (오프라인 필기체 슷자 인식을 위한 다양한 특징들의 성능 비교 및 인식률 개선 방안)

  • Park, Chang-Sun;Kim, Du-Yeong
    • The Transactions of the Korea Information Processing Society
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    • v.3 no.4
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    • pp.915-925
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    • 1996
  • In this paper, in order to find effective features which can handle variations in off-line handwritten numerals, we performed a comparative study on various sets of features. Results of experimental performance comparison shows that 4- directional features using contours and features which combined cross distance, cross, mesh and projection features are very effective for off-line handwritten numerals recognition in terms of recognition rates and recognition time. And in order to surmount limitation of recognition rate by a single neural network. we proposed a modularized neural network using majority voting and reliability factor with complex feature that mix effective features together. In order to verify the performance of the proposed method, the handwritten numeral databases of Concordia University of Canada and Dong-A University of Korea are used in the experiments. With the database of Concordia University, the recognition rate of 97.1%, the rejection rate of 1.5%, the error rate of 1.4% and the reliability of 98.5% are obtained ; and with the database of Dong-A University, there cognition rate of 98%, the rejection rate of 1.2%, the error rate of 0.8%, the reliability o99.1% are obtained.

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A Survey of Shape Descriptors in Computer Vision (컴퓨터비전에서 사용되는 모양표시자의 현황)

  • 유헌우;장동식
    • Journal of Institute of Control, Robotics and Systems
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    • v.9 no.2
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    • pp.131-139
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    • 2003
  • Shape descriptors play an important role in systems for object recognition, retrieval, registration, and analysis. Seven well-known descriptors including MPEG-7 visual descriptors arebriefly reviewed and a new robust pattern recognition descriptor is proposed. Performance comparison among descriptors are presented. Experiments show that the newly proposed descriptor yields better performance results than Fourier, invariant moment, and edge histogram descriptors.

도로영상에서 차량 특성 곡선을 이용한 차종 구분 알고리즘 개발

  • 김희식;이호재;이평원
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1995.10a
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    • pp.423-426
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    • 1995
  • 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 af 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 similarity method is used to recognize the numbers on the plates.

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Performance Comparison of Neural Network Algorithm for Shape Recognition of Welding Flaws (용접결함의 형상인식을 위한 신경회로망 알고리즘의 성능 비교)

  • 김재열;심재기;이동기;김창현;송경석;양동조
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2003.10a
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    • pp.271-276
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    • 2003
  • In this study, we compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to two algorithm. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we comfirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

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Comparison of the Dynamic Time Warping Algorithm for Spoken Korean Isolated Digits Recognition (한국어 단독 숫자음 인식을 위한 DTW 알고리즘의 비교)

  • 홍진우;김순협
    • The Journal of the Acoustical Society of Korea
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    • v.3 no.1
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    • pp.25-35
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    • 1984
  • This paper analysis the Dynamic Time Warping algorithms for time normalization of speech pattern and discusses the Dynamic Programming algorithm for spoken Korean isolated digits recognition. In the DP matching, feature vectors of the reference and test pattern are consisted of first three formant frequencies extracted by power spectrum density estimation algorithm of the ARMA model. The major differences in the various DTW algorithms include the global path constrains, the local continuity constraints on the path, and the distance weighting/normalization used to give the overall minimum distance. The performance criterias to evaluate these DP algorithms are memory requirement, speed of implementation, and recognition accuracy.

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Comparison of Speech Intelligibility & Performance of Speech Recognition in Real Driving Environments (자동차 주행 환경에서의 음성 전달 명료도와 음성 인식 성능 비교)

  • Lee Kwang-Hyun;Choi Dae-Lim;Kim Young-Il;Kim Bong-Wan;Lee Yong-Ju
    • MALSORI
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    • no.50
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    • pp.99-110
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    • 2004
  • The normal transmission characteristics of sound are hardly obtained due to the various noises and structural factors in a running car environment. It is due to the channel distortion of the original source sound recorded by microphones, and it seriously degrades the performance of the speech recognition in real driving environments. In this paper we analyze the degree of intelligibility under the various sound distortion environments by channels according to driving speed with respect to speech transmission index(STI) and compare the STI with rates of speech recognition. We examine the correlation between measures of intelligibility depending on sound pick-up patterns and performance in speech recognition. Thereby we consider the optimal location of a microphone in single channel environment. In experimentation we find that high correlation is obtained between STI and rates of speech recognition.

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A Comparison of Effective Feature Vectors for Speech Emotion Recognition (음성신호기반의 감정인식의 특징 벡터 비교)

  • Shin, Bo-Ra;Lee, Soek-Pil
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.10
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    • pp.1364-1369
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    • 2018
  • Speech emotion recognition, which aims to classify speaker's emotional states through speech signals, is one of the essential tasks for making Human-machine interaction (HMI) more natural and realistic. Voice expressions are one of the main information channels in interpersonal communication. However, existing speech emotion recognition technology has not achieved satisfactory performances, probably because of the lack of effective emotion-related features. This paper provides a survey on various features used for speech emotional recognition and discusses which features or which combinations of the features are valuable and meaningful for the emotional recognition classification. The main aim of this paper is to discuss and compare various approaches used for feature extraction and to propose a basis for extracting useful features in order to improve SER performance.