• Title/Summary/Keyword: invariant moment

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A New Approach to Fingerprint Detection Using a Combination of Minutiae Points and Invariant Moments Parameters

  • Basak, Sarnali;Islam, Md. Imdadul;Amin, M.R.
    • Journal of Information Processing Systems
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    • v.8 no.3
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    • pp.421-436
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    • 2012
  • Different types of fingerprint detection algorithms that are based on extraction of minutiae points are prevalent in recent literature. In this paper, we propose a new algorithm to locate the virtual core point/centroid of an image. The Euclidean distance between the virtual core point and the minutiae points is taken as a random variable. The mean, variance, skewness, and kurtosis of the random variable are taken as the statistical parameters of the image to observe the similarities or dissimilarities among fingerprints from the same or different persons. Finally, we verified our observations with a moment parameter-based analysis of some previous works.

Color Component Analysis For Image Retrieval (이미지 검색을 위한 색상 성분 분석)

  • Choi, Young-Kwan;Choi, Chul;Park, Jang-Chun
    • The KIPS Transactions:PartB
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    • v.11B no.4
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    • pp.403-410
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    • 2004
  • Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.

Fast Computation of Zernike Moments Using Three Look-up Tables

  • Kim, Sun-Gi;Kim, Whoi-Yul;Kim, Young-Sum;Park, Chee-Hang
    • Journal of Electrical Engineering and information Science
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    • v.2 no.6
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    • pp.156-161
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    • 1997
  • Zernike moments have been one of the most commonly used feature vectors for recognizing rotated patterns due to its rotation invariant characteristics. In order to reduce its expensive computational cost, several methods have been proposed to lower the complexity. One of the methods proposed by mukundan and K. R. Ramakrishnan[1], however, is not rotation invariant. In this paper, we propose another method that not only reduces the computational cost but preserves the rotation invariant characteristics. In the experiment, we compare our method with others, in terms of computing time and the accuracy of moment feature at different rotational angle of an object in image.

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Rotation-Invariant Iris Recognition Method Based on Zernike Moments (Zernike 모멘트 기반의 회전 불변 홍채 인식)

  • Choi, Chang-Soo;Seo, Jeong-Man;Jun, Byoung-Min
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.2
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    • pp.31-40
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    • 2012
  • Iris recognition is a biometric technology which can identify a person using the iris pattern. It is important for the iris recognition system to extract the feature which is invariant to changes in iris patterns. Those changes can be occurred by the influence of lights, changes in the size of the pupil, and head tilting. In this paper, we propose a novel method based on Zernike Moment which is robust to rotations of iris patterns. we utilized a selection of Zernike moments for the fast and effective recognition by selecting global optimum moments and local optimum moments for optimal matching of each iris class. The proposed method enables high-speed feature extraction and feature comparison because it requires no additional processing to obtain the rotation invariance, and shows comparable performance to the well-known previous methods.

The Inspection Algorithm using Invariant Moment for the Detection of Lead Faults of Semiconductor IC (불변 모멘트를 이용한 반도체 IC 리드 불량 검사 알고리즘)

  • Rhee, Kil-Whi;Kim, Joon-Seek
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.10
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    • pp.2737-2749
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    • 1998
  • Recently, vision system is widely used in factory automation processes. In this paper, the method which detects the badness in the position, slop, and the lead of chips is proposed for the inspection of semiconductor chips. The conventional methods mainly inspect semiconductor IC with the features which is extracted in image. But we propose the method which segments the lead part by the morphology and inspects the lead faults by the invariant moment. In the simulation. the results of the proposed method is better than those of the conventional method for the noisy and noiseless images .

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Content-based Rotation Invariant Retrieval of Trademarks (내용기반 회전불변 상표검색)

  • Park, Jin-Geun;Jo, Sang-Hyeon;Choe, Heung-Mun
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.39 no.1
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    • pp.60-66
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    • 2002
  • In this paper, an efficient content-based rotation-invariant retrieval of the trademarks is proposed using the edge-direction histogram for a principal symmetry axis and the moment invariants. Rotation invariant retrieval of trademarks is difficult for the conventional retrieval systems because their feature vectors are not rotation-invariant. In this paper, to obtain rotation invariant feature vectors, in addition to invariant moments, the edge-direction histogram for a principal symmetry axis is introduced and is used to solve the bin shift problem of the histogram resulted from the rotated trademark. Performance evaluation has been carried out for a database of 300 kinds of trademarks including 20 kinds of typical trademarks which are reported to be difficult to retrieve when rotated, and the proposed scheme is proved to retrieve trademarks more efficiently, especially for the rotated trademarks, than the conventional methods.

3-D Object Recognition and Restoration for Packing Administration System Using Ultrasonic Sensors and Neural Networks (주차관리 시스템 응용을 위한 신경회로망과 연계된 초음파 센서의 3차원 물체인식과 복원)

  • 조현철;이기성;사공건
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.10 no.4
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    • pp.78-84
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    • 1996
  • In this study, 3-D object recognition and restoration independent of the object translation for automotive kind recognition in parking administration system using an ultrasonic sensor array, neural networks and invariant moments are presented. Using invariant moment vectors of the acquired data 16$\times$8 pixels, 3-D objects could be classified by SCL (Simple Competitive Learning) neural networks. Modified SCL neural networks using the 16$\times$8 low resolution image was used for object restoration of 32$\times$32 high resolution image. Invariant moment vectors kept constant independent of the object translation. The recognition rates for the training and the testing data were 98[%] and 95[%], respectively. The experimental results have shown that ultrasonic sensor array with the neural networks could be applied for the detection of the automobiles and classification of the automotive kind.

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Real-time Sign Object Detection in Subway station using Rotation-invariant Zernike Moment (회전 불변 제르니케 모멘트를 이용한 실시간 지하철 기호 객체 검출)

  • Weon, Sun-Hee;Kim, Gye-Young;Choi, Hyung-Il
    • Journal of Digital Contents Society
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    • v.12 no.3
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    • pp.279-289
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    • 2011
  • The latest hardware and software techniques are combined to give safe walking guidance and convenient service of realtime walking assistance system for visually impaired person. This system consists of obstacle detection and perception, place recognition, and sign recognition for pedestrian can safely walking to arrive at their destination. In this paper, we exploit the sign object detection system in subway station for sign recognition that one of the important factors of walking assistance system. This paper suggest the adaptive feature map that can be robustly extract the sign object region from complexed environment with light and noise. And recognize a sign using fast zernike moment features which is invariant under translation, rotation and scale of object during walking. We considered three types of signs as arrow, restroom, and exit number and perform the training and recognizing steps through adaboost classifier. The experimental results prove that our method can be suitable and stable for real-time system through yields on the average 87.16% stable detection rate and 20 frame/sec of operation time for three types of signs in 5000 images of sign database.

A PSRI Feature Extraction and Automatic Target Recognition Using a Cooperative Network and an MLP. (Cooperative network와 MLP를 이용한 PSRI 특징추출 및 자동표적인식)

  • 전준형;김진호;최흥문
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.6
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    • pp.198-207
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    • 1996
  • A PSRI (position, scale, and rotation invariant ) feature extraction and automatic target recognition system using a cooperative network and an MLP is proposed. We can extract position invarient features by obtaining the target center using the projection and the moment in preprocessing stage. The scale and rotation invariant features are extracted from the contour projection of the number of edge pixels on each of the concentric circles, which is input to the cooperative network. By extracting the representative PSRI features form the features and their differentiations using max-net and min-net, we can rdduce the number of input neurons of the MLP, and make the resulted automatic target recognition system less sensitive to input variances. Experiments are conduted on various complex images which are shifted, rotated, or scaled, and the results show that the proposed system is very efficient for PSRI feature extractions and automatic target recognitions.

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