NOISE REDUCTION BY USING FUZZY IMAGE FILTERING ABSTRACT The existing system available for fuzzy filters for noise reduction deals with fat-tailed noise like impulse noise and median filter. Only impulse noise reduction uses fuzzy filters. Gaussian noise is not specially concentrated; it does not distinguish local variation due to noise and due to image structure. The proposed system presents a new technique for filtering narrow-tailed and medium narrow-tailed noise by a fuzzy filter. The system
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video sequences. The fourth video sequence reached 97.6% and the third 90.6%. In TEST 2‚ the iris detection was above 96% in all five video sequences with two above 99.7% and two at 100%. Face size estimation is also above 99.9%. The average processing time of our method was 0.02 s per frame. Thus‚ the proposed method can process frames at a rate near to 50 frames/s‚ and therefore‚ is applicable in real time in a standard personal computer (PC 1.8 GHz). Index Terms—Anthropometric templates
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Image Processing‚ Analysis‚ and Machine Vision Second Edition Milan Sonka Vaclav Hlavac Roger Boyle Contents List of algorithms xiii List of symbols and abbreviations xvii Preface xix Course contents xxiii 1 Introduction 1 1.1 Summary 1.2 Exercises 1.3 References 8 8 9 2 The digitized image and its properties 2.1 Basic concepts 2.1.1 Image functions 2.1.2 The Dirac distribution and convolution 2.1.3 The Fourier transform 2.1.4 Images as
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DIGITAL IMAGE PROCESSING (CREATIVE WORLD OF FACE MORPHING) BY ABSTRACT A study on face morphing is proposed.The algorithms explains the extra feature of points on face and based on these feature points‚ images are portioned and morphing is performed. The algorithms has been used to generate morphing between images of face of different people as well as between images of face of individuals. To do face morphing‚ feature points are usually specified manually
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International Journal of Latest Trends in Computing (E-ISSN: 2045-5364) Volume 2‚ Issue 1‚ March 2011 108 Analysis and Comparison of Texture Features for Content Based Image Retrieval S.Selvarajah 1 and S.R. Kodituwakku 2 1 Department of Physical Science‚ Vavuniya Campus of the University of Jaffna‚ Vavuniya‚ Sri Lanka 2 Department of Statistics & Computer Science‚ University of Peradeniya‚ Sri Lanka. {shakeelas@mail.vau.jfn.ac.lk‚ salukak@pdn.ac.lk} Abstract: Texture is one of
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ill-defined as it can adapt to interruptions in the shape¡¦s contour using a pre-set tolerance and is able to scan multiple neighbors of a given point. The algorithm has a low complexity and no restrictions on the type or size of the traced shape. The extracted ordered set of boundary points represents the contour of a given shape and is important for curvature-based shape descriptors. Categories and Subject Descriptors I.4.6 [Image Processing and Computer Vision]: Segmentation ¡V Edge and feature detection
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Lecture 13: Edge Detection c Bryan S. Morse‚ Brigham Young University‚ 1998–2000 Last modified on February 12‚ 2000 at 10:00 AM Contents 13.1 Introduction . . . . . . . . . . . . . . 13.2 First-Derivative Methods . . . . . . . 13.2.1 Roberts Kernels . . . . . . . . . 13.2.2 Kirsch Compass Kernels . . . . 13.2.3 Prewitt Kernels . . . . . . . . . 13.2.4 Sobel Kernels . . . . . . . . . . 13.2.5 Edge Extraction . . . . . . . . . 13.3 Second-Derivative Methods . . . . . . 13.3.1 Laplacian Operators
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amount of data available. Image fusion provides an effective way of reducing this increasing volume of information while at the same time extracting all the useful information from the source images. Multi-sensor data often presents complementary information about the region surveyed‚ so image fusion provides an effective method to enable comparison and analysis of such data. The aim of image fusion‚ apart from reducing the amount of data‚ is to create new images that are more suitable for
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A PROJECT REPORT ON “FINGERPRINT RECOGNITION AND IMAGE ENHANCEMENT USING MATLAB” Submitted in partial fulfillment Of the requirements for the award of the degree in BACHELOR OF TECHNOLOGY IN APPLIED ELECTRONICS AND INSTRUMENTATION ENGINEERING SUBMITTED BY: SHAKTI ABHISHEK- 0803112 SATISH GOYAL - 0803064 ROHIT DASH - 0803086 MD. IRFAN ARIF RAHMAN - 0803117 [pic] DEPARTMENT OF APPLIED ELECTRONICS AND INSTRUMENTATION ENGINEERING GANDHI INSTITUTE OF ENGINEERING AND TECHNOLOGY Biju Patnaik University
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Proceedings of National Conference on New Horizons in IT - NCNHIT 2013 122 Real Time Tracking of Complete Transport System Using GPS Mr. Nilesh Manganakar Mr. Nikhil Pawar Mr. Prathamesh Pulaskar BE-IT BE-IT BE-IT nilesh_manganakar@yahoo.co.in nikhilpawar151@gmail.com Abstract: Due to the high cost of time several methods are proposed to reduce the wastage of time on the bus station or waiting for the bus for more time is not preferred. So‚ we require one tracking
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