• Title/Summary/Keyword: Database for Pattern Recognition

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The Pattern Recognition Methods for Emotion Recognition with Speech Signal (음성신호를 이용한 감성인식에서의 패턴인식 방법)

  • Park Chang-Hyeon;Sim Gwi-Bo
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2006.05a
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    • pp.347-350
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    • 2006
  • In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

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Finger Vein Recognition Using Generalized Local Line Binary Pattern

  • Lu, Yu;Yoon, Sook;Xie, Shan Juan;Yang, Jucheng;Wang, Zhihui;Park, Dong Sun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.5
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    • pp.1766-1784
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    • 2014
  • Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

Real-Time Bus Reconfiguration Strategy for the Fault Restoration of Main Transformer Based on Pattern Recognition Method (자동화된 변전소의 주변압기 사고복구를 위한 패턴인식기법에 기반한 실시간 모선재구성 전략 개발)

  • Ko Yun-Seok
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.53 no.11
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    • pp.596-603
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    • 2004
  • This paper proposes an expert system based on the pattern recognition method which can enhance the accuracy and effectiveness of real-time bus reconfiguration strategy for the transfer of faulted load when a main transformer fault occurs in the automated substation. The minimum distance classification method is adopted as the pattern recognition method of expert system. The training pattern set is designed MTr by MTr to minimize the searching time for target load pattern which is similar to the real-time load pattern. But the control pattern set, which is required to determine the corresponding bus reconfiguration strategy to these trained load pattern set is designed as one table by considering the efficiency of knowledge base design because its size is small. The training load pattern generator based on load level and the training load pattern generator based on load profile are designed, which are can reduce the size of each training pattern set from max L/sup (m+f)/ to the size of effective level. Here, L is the number of load level, m and f are the number of main transformers and the number of feeders. The one reduces the number of trained load pattern by setting the sawmiller patterns to a same pattern, the other reduces by considering only load pattern while the given period. And control pattern generator based on exhaustive search method with breadth-limit is designed, which generates the corresponding bus reconfiguration strategy to these trained load pattern set. The inference engine of the expert system and the substation database and knowledge base is implemented in MFC function of Visual C++ Finally, the performance and effectiveness of the proposed expert system is verified by comparing the best-first search solution and pattern recognition solution based on diversity event simulations for typical distribution substation.

Pattern Recognition Methods for Emotion Recognition with speech signal

  • Park Chang-Hyun;Sim Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.2
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    • pp.150-154
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    • 2006
  • In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition are determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section.

An Automated Search for Design Database by Shape Pattern Recognition (형상 패턴 인식을 이용한 설계자료의 자동 탐색)

  • 차주헌
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1996.04a
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    • pp.670-674
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    • 1996
  • In automated search of a design database to support mechanical design, it is necessaryto recognize a shape pattern which represents a design object. This paper introduces the concept of a surface relation graph (SRG) for recognizing shape patterns from a 3D boundary representation scheme of a solid model(a B-rep model). In SRG, the nodes and arcs correspond to the faces and edges shared by two adjacent faces, respectively. An attribute assigned to an arc is given by an integer which discriminates the relationship between two adjacent faces. The + sign of the integer represents the geometric convexity of the solid, and the -sign the concivity at the shared edge. The input shape is recognized by comparison with the predefined features which are subgraphs of the SRG. A hierarchyof the database for upporting the design is presented. A search for the design database is also discussed. The usefulness of this method is illustrated by some application results.

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Development of Computer Vision System for Individual Recognition and Feature Information of Cow (I) - Individual recognition using the speckle pattern of cow - (젖소의 개체인식 및 형상 정보화를 위한 컴퓨터 시각 시스템 개발 (I) - 반문에 의한 개체인식 -)

  • 이종환
    • Journal of Biosystems Engineering
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    • v.27 no.2
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    • pp.151-160
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    • 2002
  • Cow image processing technique would be useful not only for recognizing an individual but also for establishing the image database and analyzing the shape of cows. A cow (Holstein) has usually the unique speckle pattern. In this study, the individual recognition of cow was carried out using the speckle pattern and the content-based image retrieval technique. Sixty cow images of 16 heads were captured under outdoor illumination, which were complicated images due to shadow, obstacles and walking posture of cow. Sixteen images were selected as the reference image for each cow and 44 query images were used for evaluating the efficiency of individual recognition by matching to each reference image. Run-lengths and positions of runs across speckle area were calculated from 40 horizontal line profiles for ROI (region of interest) in a cow body image after 3 passes of 5$\times$5 median filtering. A similarity measure for recognizing cow individuals was calculated using Euclidean distance of normalized G-frame histogram (GH). normalized speckle run-length (BRL), normalized x and y positions (BRX, BRY) of speckle runs. This study evaluated the efficiency of individual recognition of cow using Recall(Success rate) and AVRR(Average rank of relevant images). Success rate of individual recognition was 100% when GH, BRL, BRX and BRY were used as image query indices. It was concluded that the histogram as global property and the information of speckle runs as local properties were good image features for individual recognition and the developed system of individual recognition was reliable.

A Novel Text Sample Selection Model for Scene Text Detection via Bootstrap Learning

  • Kong, Jun;Sun, Jinhua;Jiang, Min;Hou, Jian
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.2
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    • pp.771-789
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    • 2019
  • Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

Speech Recognition in the Car Noise Environment (자동차 소음 환경에서 음성 인식)

  • 김완구;차일환;윤대희
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.30B no.2
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    • pp.51-58
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    • 1993
  • This paper describes the development of a speaker-dependent isolated word recognizer as applied to voice dialing in a car noise environment. for this purpose, several methods to improve performance under such condition are evaluated using database collected in a small car moving at 100km/h The main features of the recognizer are as follow: The endpoint detection error can be reduced by using the magnitude of the signal which is inverse filtered by the AR model of the background noise, and it can be compensated by using variants of the DTW algorithm. To remove the noise, an autocorrelation subtraction method is used with the constraint that residual energy obtainable by linear predictive analysis should be positive. By using the noise rubust distance measure, distortion of the feature vector is minimized. The speech recognizer is implemented using the Motorola DSP56001(24-bit general purpose digital signal processor). The recognition database is composed of 50 Korean names spoken by 3 male speakers. The recognition error rate of the system is reduced to 4.3% using a single reference pattern for each word and 1.5% using 2 reference patterns for each word.

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A Study on CAD interfaced CAPP System for Turning Operation ( I ) : Automatic Feature Recognition and Process Selection (선삭공정에서 CAD 인터페이스된 자동공정계획시스템개발에 관한 연구( I ) : 형상특징의 자동인식과 공정선정)

  • Cho, Kyu-Kap;Kim, In-Ho
    • Journal of Korean Institute of Industrial Engineers
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    • v.17 no.2
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    • pp.1-16
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    • 1991
  • This paper deals with some critical activities of CAPP system such as generation of part description database, part feature recognition, process and operation selection, and sequencing method for turning operation of symmetric rotational parts. The part description database is generated by data conversion module from CAD data, and the part feature is recognized by using both pattern primitives and feature recognition rules. Machining processes and operations are selected based on machining surface features and its sequence is determined by rules acquired from process planning expert. AutoCAD is employed as CAD system and computer program is developed by using Turbo-C on IBM PC/AT compatible system.

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Facial Expression Recognition Using SIFT Descriptor (SIFT 기술자를 이용한 얼굴 표정인식)

  • Kim, Dong-Ju;Lee, Sang-Heon;Sohn, Myoung-Kyu
    • KIPS Transactions on Software and Data Engineering
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    • v.5 no.2
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    • pp.89-94
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    • 2016
  • This paper proposed a facial expression recognition approach using SIFT feature and SVM classifier. The SIFT was generally employed as feature descriptor at key-points in object recognition fields. However, this paper applied the SIFT descriptor as feature vector for facial expression recognition. In this paper, the facial feature was extracted by applying SIFT descriptor at each sub-block image without key-point detection procedure, and the facial expression recognition was performed using SVM classifier. The performance evaluation was carried out through comparison with binary pattern feature-based approaches such as LBP and LDP, and the CK facial expression database and the JAFFE facial expression database were used in the experiments. From the experimental results, the proposed method using SIFT descriptor showed performance improvements of 6.06% and 3.87% compared to previous approaches for CK database and JAFFE database, respectively.