Lectures 6&7: Image Enhancement

Size: px
Start display at page:

Download "Lectures 6&7: Image Enhancement"

Transcription

1 Lectures 6&7: Image Enhancement Leena Ikonen Pattern Recognition (MVPR) Lappeenranta University of Technology (LUT) 1

2 Content Background Spatial domain methods Frequency domain methods Enhancement by point processing Spatial filtering Enhancement in the frequency domain 2

3 Background: Motivation For preprocessing to make the image look better, i.e., more suitable for further processing. Problems with contrast sharpness smoothness noise distortions etc 3

4 Background: Spatial domain methods Method: Slide the mask though the image and compute new pixel values Image processing function: g(x,y) = T[f(x,y)] f(x,y) the input image g(x,y) T the processed image an operator on f, defined over some neighborhood of (x,y) Gray-level transformation (mapping) function: s = T(r) r denotes f(x,y) and s denotes g(x,y) 4

5 Background: Frequency domain methods Method: Multiply the Fourier transforms of the image and the mask, and apply the inverse transform to the multiplication Convolution: g(x,y) = h(x,y)*f(x,y) h(x,y) Fourier transform: G(u,v) = H(u,v)F(u,v) a linear, postion invariant operator H(u,v) the transfer function of the process Inverse Fourier transform: g(x,y) = F -1 [H(u,v)F(u,v)] 5

6 Enhancement by point processing: Some simple intensity transformations Image negatives: s = ((L-1) r) where L = number of gray-levels Contrast stretching: Poor illumination, lack of dynamic range in the imaging sensor, wrong setting of a lens aperture during image acquisition To increase the dynamic range of the gray-levels Piecewise linear function Thresholding function => binary image (two values only) 6

7 Contrast stretching 7

8 Enhancement by point processing: Some simple intensity transformations (cont.) Compression of dynamic range: The dynamic range exceeds the capability of the display device. The need of brighter pixels s = c log(1 + abs(r)) where c is a scaling constant Gray-level slicing: Highlighting a specific range of gray-levels with removing or preserving other pixels 8

9 Gray-level slicing Original image (top) Thresholded (left) Gray-level slicing (right) 9

10 Enhancement by point processing: Some simple intensity transformations (cont.) Bit-plane slicing: Select the specific bit planes For example: the image of eight 1-bit planes Plane 7 contains all the high-order bits: Higher planes contain visually significant data. Note: digital watermarking! To select the plane 7 only corresponds to the image thresholded at gray-level

11 Enhancement by point processing: Histogram processing Histogram of the image: p(r k ) = n k /n where r k is the kth gray-level n k is the number of pixels with that gray-level n is the total number of pixels in the image k = 0, 1, 2,, L-1 L is the number of gray-levels 11

12 Histogram of an image 12

13 Histogram equalization 13

14 Enhancement by point processing: Histogram processing (cont.) Histogram equalization (or histogram linearization) to obtain the uniform histogram Gray-level transformation function and its inverse function r represents gray-level values normalized to interval [0,1] (r=0=black, r=1=white) s = T(r) is the new equalized gray-value for gray-value r where 0<=T(r)<=1 and T(r) is single-valued and monotonically increasing in 0<=r<=1 r = T -1 (s) where 0<=s<=1 14

15 Enhancement by point processing: Histogram processing (cont.) 15

16 Enhancement by point processing: Histogram processing (cont.) 16

17 Enhancement by point processing: Histogram processing (cont.) Example: p r (r) = -2r + 2 when 0<=r<=1 0 elsewhere What transformation function creates uniform density? r s T ( r) ( 2w 2) dw r 2 2r 0 r T 1( s) 1 1 s,0 r 1 r 1 1 s 17

18 Enhancement by point processing: Histogram processing (cont.) In discrete form, probabilities: p r (r k ) = n k /n where 0 r k 1, k = 0, 1,, L-1 n is the total number of pixels in the image n k is the number of pixels with gray-value r k L is the total number of possible gray-levels in the image Transformation function: s k = T(r k ) = p r (r j ) = n j /n for j=0,...,k where 0 r k 1 and k=0,1,,l-1 Note that probability p r (r j ) is simply the fraction of pixels with grayvalue r j out of the total number of pixels The new gray-value is the gray-level closest to the sum of probabilities up to the original value k: round((l-1) s k ) 18

19 Enhancement by point processing: Histogram processing (cont.) Histogram specification: To apply another transformation function than an approximation to a uniform histogram Local enhancement: Local processing instead of the whole image For example, histogram equalization of a 7x7 neighborhood around each pixel 19

20 Enhancement by point processing: Image subtraction The difference between two images f(x,y) and h(x,y): g(x,y) = f(x,y) h(x,y) The use of a mask image (pixelwise subtraction) Applications in medical image processing: The mask is a normal image which is subtracted from a sample image to point out regions of interest, e.g. object that has moved between frames/images (see next slide) Remember also the regular image subtracted from the original to detect irregularities (e.g. missing dots) 20

21 Image subtraction 21

22 Enhancement by point processing: Image averaging Consider a noisy image g(x,y) formed by the addition of noise η(x,y) to an original image image f(x,y): g(x,y) = f(x,y) + η(x,y) By averaging noisy images, noise is reduced Noise must be uncorrelated and must have zero average value! Do NOT use averaging for salt and pepper noise! Example: noisy microscope images 22

23 Spatial filtering: Background Spatial filtering: the use of spatial filters Spatial filters: Lowpass filters Highpass filters Bandpass filters The mask: w1 w2 w3 w4 w5 w6 w7 w8 w9 Smoothing filters, sharpening filters 23

24 Spatial filtering: Smoothing filters For blurring and noise reduction Lowpass spatial filtering: /9 x Neighborhood averaging Median filtering: replace the gray-level of each pixel by the median of the gray-levels in a neighborhood of that pixel Removes noise, but preserves details such as edges Filter size? Weighted median filtering? 24

25 Spatial filtering: Averaging vs. median Original image (upper left) Original + noise (upper right) Smoothed image (lower right) Median smoothing (lower left) 25

26 Spatial filtering: Sharpening filters For highlighting fine detail in an image or enhance detail that has been blurred Filters: Basic highpass spatial filter High-boost filtering Derivative filters 26

27 Spatial filtering: Basic highpass spatial filtering Positive coefficients near the center of a filter, negative coefficients in the outer periphery 3 x 3 sharpening filter: /9 x The sum of the coefficients is zero The filter eliminates the zero frequency term => reduced global contrast of the image Scaling and/or clipping for negative values to map the range [0, L-1] 27

28 Spatial filtering: High-boost filtering Highpass = Original Lowpass Low frequencies are lost High-boost or high-frequency-emphasis filter: High boost = (A)(Original) Lowpass = (A-1)(Original) + Original Lowpass = (A-1)(Original) + Highpass. Looks like original image, with edge enhancement by A fourier.eng.hmc.edu/e161/lectures/gradient/node2.htm l 28

29 Spatial filtering: High-boost filtering (cont.) Unsharp masking: to subtract a blurred image from an original image In the printing and publishing industry The mask with w = 9A -1 (with A 1): /9 x -1 w

30 Spatial filtering: Derivative filters For sharpening an image (averaging vs. differentiation) The gradient of f(x,y): df = f/ x f/ y The magnitude is the basis for image differentiation methods: mag(df)= (( f/ x) 2 + ( f/ y) 2 ) (-1/2) 30

31 Spatial filtering: Derivate filters (cont.) Roberts: Prewitt: Sobel:

32 Enhancement in the frequency domain The use of image frequencies for enhancement Convolution: f(x)*g(x) F(u) G(u) The filtered image g(x,y) using the Discrete Fourier transforms of an original image f(x,y) and a mask h(x,y): g(x,y) = F -1 [H(u,v)F(u,v)] Lowpass filtering Highpass filtering 32

33 Fourier transform: Image power Radius (pixels) % Image power Distance from point (u,v) to the origin: D(u,v) = (u 2 + v 2 ) (-1/2) 33

34 Enhancement in the Frequency Domain: Lowpass filter G(u,v) = H(u,v) F(u,v) Ideal lowpass filter: H(u,v) = 1 if D(u,v) D 0, or 0 if D(u,v) > D 0 Original (left) and filtered image (right) 34

35 Enhancement in the Frequency Domain: Butterworth lowpass filter The transfer function: H(u,v) = 1/(1 + (D(u,v)/D 0 ) 2n ) where n is the order of the filter D 0 is the cutoff frequency locus (select!) H(u,v) from 1 to 0. When D(u,v) = D 0, H(u,v) = 0.5. H(u,v) = 1/ 2 commonly used. 35

36 Enhancement in the Frequency Domain: Highpass filter Ideal high pass filter: H(u,v) = 0 if D(u,v) D 0, or 1 if D(u,v) > D 0 Original (left) and filtered image (right). 36

37 Enhancement in the Frequency Domain: Butterworth highpass filter The transfer function: H(u,v) = 1/(1 + (D 0 /D(u,v)) 2n ) where n is the order of the filter D 0 is the cutoff frequency locus H(u,v) from 0 to 1. When D(u,v) = D 0, H(u,v) = 0.5. H(u,v) = 1/ 2 commonly used. 37

38 Summary For preprocessing to make the image look better, i.e., more suitable for further processing Approaches: Spatial domain methods Frequency domain methods Enhancement by point processing Spatial filtering Enhancement in the frequency domain 38

Sharpening through spatial filtering

Sharpening through spatial filtering Sharpening through spatial filtering Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Elaborazione delle immagini (Image processing I) academic year 2011 2012 Sharpening The term

More information

Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition

Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition 1. Image Pre-Processing - Pixel Brightness Transformation - Geometric Transformation - Image Denoising 1 1. Image Pre-Processing

More information

Frequency domain filtering fundamentals

Frequency domain filtering fundamentals Frequency domain filtering fundamentals by Gleb V. Tcheslavski: gleb@ee.lamar.edu http://ee.lamar.edu/gleb/dip/index.htm Spring 2008 ELEN 4304/5365 DIP 1 Preliminaries For a digital image f(x,y) the basic

More information

Linear Filtering Part II

Linear Filtering Part II Linear Filtering Part II Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Fourier theory Jean Baptiste Joseph Fourier had a crazy idea: Any periodic function can

More information

Lecture 14. Point Spread Function (PSF)

Lecture 14. Point Spread Function (PSF) Lecture 14 Point Spread Function (PSF), Modulation Transfer Function (MTF), Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Receiver Operating Curves (ROC) Point Spread Function (PSF) Recollect

More information

Fourier Transform and Image Filtering. CS/BIOEN 6640 Lecture Marcel Prastawa Fall 2010

Fourier Transform and Image Filtering. CS/BIOEN 6640 Lecture Marcel Prastawa Fall 2010 Fourier Transform and Image Filtering CS/BIOEN 6640 Lecture Marcel Prastawa Fall 2010 The Fourier Transform Fourier Transform Forward, mapping to frequency domain: Backward, inverse mapping to time domain:

More information

Admin stuff. 4 Image Pyramids. Spatial Domain. Projects. Fourier domain 2/26/2008. Fourier as a change of basis

Admin stuff. 4 Image Pyramids. Spatial Domain. Projects. Fourier domain 2/26/2008. Fourier as a change of basis Admin stuff 4 Image Pyramids Change of office hours on Wed 4 th April Mon 3 st March 9.3.3pm (right after class) Change of time/date t of last class Currently Mon 5 th May What about Thursday 8 th May?

More information

Imageprocessing. Errors of measurements

Imageprocessing. Errors of measurements Imageprocessing Errors of measurements Many source of errors affect observations, like CCD images: - Diffraction and seeing blur the image. - The imaging optics causes geometric distortions. - The imaging

More information

(Refer Slide Time: 06:10)

(Refer Slide Time: 06:10) Computer Graphics Prof. Sukhendu Das Dept. of Computer Science and Engineering Indian Institute of Technology, Madras Lecture - 43 Digital Image Processing Welcome back to the last part of the lecture

More information

Digital Image Processing

Digital Image Processing GONZ_FMv3.qxd 7/26/07 9:05 AM Page i Digital Image Processing Third Edition Rafael C. Gonzalez University of Tennessee Richard E. Woods MedData Interactive Upper Saddle River, NJ 07458 GONZ_FMv3.qxd 7/26/07

More information

Intensity transformations

Intensity transformations Intensity transformations Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Elaborazione delle immagini (Image processing I) academic year 2011 2012 Spatial domain The spatial domain

More information

Digital image processing

Digital image processing 746A27 Remote Sensing and GIS Lecture 4 Digital image processing Chandan Roy Guest Lecturer Department of Computer and Information Science Linköping University Digital Image Processing Most of the common

More information

DIGITAL IMAGE PROCESSING AND ANALYSIS

DIGITAL IMAGE PROCESSING AND ANALYSIS DIGITAL IMAGE PROCESSING AND ANALYSIS Human and Computer Vision Applications with CVIPtools SECOND EDITION SCOTT E UMBAUGH Uffi\ CRC Press Taylor &. Francis Group Boca Raton London New York CRC Press is

More information

EECS 556 Image Processing W 09. Interpolation. Interpolation techniques B splines

EECS 556 Image Processing W 09. Interpolation. Interpolation techniques B splines EECS 556 Image Processing W 09 Interpolation Interpolation techniques B splines What is image processing? Image processing is the application of 2D signal processing methods to images Image representation

More information

Digital Imaging and Multimedia. Filters. Ahmed Elgammal Dept. of Computer Science Rutgers University

Digital Imaging and Multimedia. Filters. Ahmed Elgammal Dept. of Computer Science Rutgers University Digital Imaging and Multimedia Filters Ahmed Elgammal Dept. of Computer Science Rutgers University Outlines What are Filters Linear Filters Convolution operation Properties of Linear Filters Application

More information

MATLAB-based Applications for Image Processing and Image Quality Assessment Part I: Software Description

MATLAB-based Applications for Image Processing and Image Quality Assessment Part I: Software Description RADIOENGINEERING, VOL. 20, NO. 4, DECEMBER 2011 1009 MATLAB-based Applications for Image Processing and Image Quality Assessment Part I: Software Description Lukáš KRASULA, Miloš KLÍMA, Eric ROGARD, Edouard

More information

Digital Image Fundamentals. Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr

Digital Image Fundamentals. Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Imaging process Light reaches surfaces in 3D. Surfaces reflect. Sensor element receives

More information

Basic Image Processing (using ImageJ)

Basic Image Processing (using ImageJ) Basic Image Processing (using ImageJ) Dr. Arne Seitz Swiss Institute of Technology (EPFL) Faculty of Life Sciences Head of BIOIMAGING AND OPTICS BIOP arne.seitz@epfl.ch Overview File formats (data storage)

More information

Time series analysis Matlab tutorial. Joachim Gross

Time series analysis Matlab tutorial. Joachim Gross Time series analysis Matlab tutorial Joachim Gross Outline Terminology Sampling theorem Plotting Baseline correction Detrending Smoothing Filtering Decimation Remarks Focus on practical aspects, exercises,

More information

An Experimental Study of the Performance of Histogram Equalization for Image Enhancement

An Experimental Study of the Performance of Histogram Equalization for Image Enhancement International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Special Issue-2, April 216 E-ISSN: 2347-2693 An Experimental Study of the Performance of Histogram Equalization

More information

Computer Vision. Image math. Copyright 2001 2016 by NHL Hogeschool and Van de Loosdrecht Machine Vision BV All rights reserved

Computer Vision. Image math. Copyright 2001 2016 by NHL Hogeschool and Van de Loosdrecht Machine Vision BV All rights reserved Computer Vision: Image math and geometric Computer Vision Image math Copyright 2001 2016 by NHL Hogeschool and Van de Loosdrecht Machine Vision BV All rights reserved j.van.de.loosdrecht@nhl.nl, jaap@vdlmv.nl

More information

Convolution. 1D Formula: 2D Formula: Example on the web: http://www.jhu.edu/~signals/convolve/

Convolution. 1D Formula: 2D Formula: Example on the web: http://www.jhu.edu/~signals/convolve/ Basic Filters (7) Convolution/correlation/Linear filtering Gaussian filters Smoothing and noise reduction First derivatives of Gaussian Second derivative of Gaussian: Laplacian Oriented Gaussian filters

More information

Computational Foundations of Cognitive Science

Computational Foundations of Cognitive Science Computational Foundations of Cognitive Science Lecture 15: Convolutions and Kernels Frank Keller School of Informatics University of Edinburgh keller@inf.ed.ac.uk February 23, 2010 Frank Keller Computational

More information

Resolution Enhancement of images with Interpolation and DWT-SWT Wavelet Domain Components

Resolution Enhancement of images with Interpolation and DWT-SWT Wavelet Domain Components Resolution Enhancement of images with Interpolation and DWT-SWT Wavelet Domain Components Mr. G.M. Khaire 1, Prof. R.P.Shelkikar 2 1 PG Student, college of engg, Osmanabad. 2 Associate Professor, college

More information

Enhancement of scanned documents in Besov spaces using wavelet domain representations

Enhancement of scanned documents in Besov spaces using wavelet domain representations Enhancement of scanned documents in Besov spaces using wavelet domain representations Kathrin Berkner 1 Ricoh Innovations, Inc., 2882 Sand Hill Road, Suite 115, Menlo Park, CA 94025 ABSTRACT After scanning,

More information

Transmitter Characteristics (83D.3.1) Ryan Latchman, Mindspeed

Transmitter Characteristics (83D.3.1) Ryan Latchman, Mindspeed Transmitter haracteristics (83D.3.) Ryan Latchman, Mindspeed Transmit equalizer Transmitter equalizer range The AUI-4 chip-to-chip transmitter includes programmable equalization to compensate for the frequency-dependent

More information

jorge s. marques image processing

jorge s. marques image processing image processing images images: what are they? what is shown in this image? What is this? what is an image images describe the evolution of physical variables (intensity, color, reflectance, condutivity)

More information

Purpose of Time Series Analysis. Autocovariance Function. Autocorrelation Function. Part 3: Time Series I

Purpose of Time Series Analysis. Autocovariance Function. Autocorrelation Function. Part 3: Time Series I Part 3: Time Series I Purpose of Time Series Analysis (Figure from Panofsky and Brier 1968) Autocorrelation Function Harmonic Analysis Spectrum Analysis Data Window Significance Tests Some major purposes

More information

Digital Image Processing

Digital Image Processing Digital Image Processing Using MATLAB Second Edition Rafael C. Gonzalez University of Tennessee Richard E. Woods MedData Interactive Steven L. Eddins The MathWorks, Inc. Gatesmark Publishing A Division

More information

Basics of Image and data analysis in 3D

Basics of Image and data analysis in 3D Basics of Image and data analysis in 3D outline Why image processing, and how? Image processing in 2D What is an ideal image? Histogram tells stories! Before taking the image: the right imaging conditions!

More information

Edge detection. (Trucco, Chapt 4 AND Jain et al., Chapt 5) -Edges are significant local changes of intensity in an image.

Edge detection. (Trucco, Chapt 4 AND Jain et al., Chapt 5) -Edges are significant local changes of intensity in an image. Edge detection (Trucco, Chapt 4 AND Jain et al., Chapt 5) Definition of edges -Edges are significant local changes of intensity in an image. -Edges typically occur on the boundary between two different

More information

Reading.. IMAGE COMPRESSION- I IMAGE COMPRESSION. Image compression. Data Redundancy. Lossy vs Lossless Compression. Chapter 8.

Reading.. IMAGE COMPRESSION- I IMAGE COMPRESSION. Image compression. Data Redundancy. Lossy vs Lossless Compression. Chapter 8. Reading.. IMAGE COMPRESSION- I Week VIII Feb 25 Chapter 8 Sections 8.1, 8.2 8.3 (selected topics) 8.4 (Huffman, run-length, loss-less predictive) 8.5 (lossy predictive, transform coding basics) 8.6 Image

More information

A System for Capturing High Resolution Images

A System for Capturing High Resolution Images A System for Capturing High Resolution Images G.Voyatzis, G.Angelopoulos, A.Bors and I.Pitas Department of Informatics University of Thessaloniki BOX 451, 54006 Thessaloniki GREECE e-mail: pitas@zeus.csd.auth.gr

More information

Analog and Digital Filters Anthony Garvert November 13, 2015

Analog and Digital Filters Anthony Garvert November 13, 2015 Analog and Digital Filters Anthony Garvert November 13, 2015 Abstract In circuit analysis and performance, a signal transmits some form of information, such as a voltage or current. However, over a range

More information

Personal Identity Verification (PIV) IMAGE QUALITY SPECIFICATIONS FOR SINGLE FINGER CAPTURE DEVICES

Personal Identity Verification (PIV) IMAGE QUALITY SPECIFICATIONS FOR SINGLE FINGER CAPTURE DEVICES Personal Identity Verification (PIV) IMAGE QUALITY SPECIFICATIONS FOR SINGLE FINGER CAPTURE DEVICES 1.0 SCOPE AND PURPOSE These specifications apply to fingerprint capture devices which scan and capture

More information

Forensic Image Processing. www.martinojerian.com

Forensic Image Processing. www.martinojerian.com Forensic Image Processing www.martinojerian.com Forensic Image Processing Lesson 1 An introduction on digital images Purpose of the course What is a digital image? What use can images have for investigative

More information

A New Robust Algorithm for Video Text Extraction

A New Robust Algorithm for Video Text Extraction A New Robust Algorithm for Video Text Extraction Pattern Recognition, vol. 36, no. 6, June 2003 Edward K. Wong and Minya Chen School of Electrical Engineering and Computer Science Kyungpook National Univ.

More information

MODULATION TRANSFER FUNCTION MEASUREMENT METHOD AND RESULTS FOR THE ORBVIEW-3 HIGH RESOLUTION IMAGING SATELLITE

MODULATION TRANSFER FUNCTION MEASUREMENT METHOD AND RESULTS FOR THE ORBVIEW-3 HIGH RESOLUTION IMAGING SATELLITE MODULATION TRANSFER FUNCTION MEASUREMENT METHOD AND RESULTS FOR THE ORBVIEW-3 HIGH RESOLUTION IMAGING SATELLITE K. Kohm ORBIMAGE, 1835 Lackland Hill Parkway, St. Louis, MO 63146, USA kohm.kevin@orbimage.com

More information

T = 1 f. Phase. Measure of relative position in time within a single period of a signal For a periodic signal f(t), phase is fractional part t p

T = 1 f. Phase. Measure of relative position in time within a single period of a signal For a periodic signal f(t), phase is fractional part t p Data Transmission Concepts and terminology Transmission terminology Transmission from transmitter to receiver goes over some transmission medium using electromagnetic waves Guided media. Waves are guided

More information

Frequency Response and Continuous-time Fourier Transform

Frequency Response and Continuous-time Fourier Transform Frequency Response and Continuous-time Fourier Transform Goals Signals and Systems in the FD-part II I. (Finite-energy) signals in the Frequency Domain - The Fourier Transform of a signal - Classification

More information

chapter Introduction to Digital Signal Processing and Digital Filtering 1.1 Introduction 1.2 Historical Perspective

chapter Introduction to Digital Signal Processing and Digital Filtering 1.1 Introduction 1.2 Historical Perspective Introduction to Digital Signal Processing and Digital Filtering chapter 1 Introduction to Digital Signal Processing and Digital Filtering 1.1 Introduction Digital signal processing (DSP) refers to anything

More information

Sachin Patel HOD I.T Department PCST, Indore, India. Parth Bhatt I.T Department, PCST, Indore, India. Ankit Shah CSE Department, KITE, Jaipur, India

Sachin Patel HOD I.T Department PCST, Indore, India. Parth Bhatt I.T Department, PCST, Indore, India. Ankit Shah CSE Department, KITE, Jaipur, India Image Enhancement Using Various Interpolation Methods Parth Bhatt I.T Department, PCST, Indore, India Ankit Shah CSE Department, KITE, Jaipur, India Sachin Patel HOD I.T Department PCST, Indore, India

More information

REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING

REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING Ms.PALLAVI CHOUDEKAR Ajay Kumar Garg Engineering College, Department of electrical and electronics Ms.SAYANTI BANERJEE Ajay Kumar Garg Engineering

More information

Improving Quality of Satellite Image by Wavelet Transforming & Morphological Filtering

Improving Quality of Satellite Image by Wavelet Transforming & Morphological Filtering Improving Quality of Satellite Image by Wavelet Transforming & Morphological Filtering Anumolu Lasmika 1, K. Raveendra 2 P.G. Student, Department of ECE, S. V. Engineering College for Women, Tirupati,

More information

Image-Based Transfer Function Design for Data Exploration in Volume Visualization

Image-Based Transfer Function Design for Data Exploration in Volume Visualization Image-Based Transfer Function esign for ata Exploration in Volume Visualization Shiaofen Fang Tom Biddlecome Mihran Tuceryan epartment of Computer and Information Science Indiana University Purdue University

More information

Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections

Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Maximilian Hung, Bohyun B. Kim, Xiling Zhang August 17, 2013 Abstract While current systems already provide

More information

Correlation and Convolution Class Notes for CMSC 426, Fall 2005 David Jacobs

Correlation and Convolution Class Notes for CMSC 426, Fall 2005 David Jacobs Correlation and Convolution Class otes for CMSC 46, Fall 5 David Jacobs Introduction Correlation and Convolution are basic operations that we will perform to extract information from images. They are in

More information

Digital Filter Design

Digital Filter Design Digital Filter Design Objective - Determination of a realiable transfer function G() approximating a given frequency response specification is an important step in the development of a digital filter If

More information

Image Content-Based Email Spam Image Filtering

Image Content-Based Email Spam Image Filtering Image Content-Based Email Spam Image Filtering Jianyi Wang and Kazuki Katagishi Abstract With the population of Internet around the world, email has become one of the main methods of communication among

More information

Introduction to Digital Resolution

Introduction to Digital Resolution Introduction to Digital Resolution 2011 Copyright Les Walkling 2011 Adobe Photoshop screen shots reprinted with permission from Adobe Systems Incorporated. Version 2011:02 CONTENTS Pixels of Resolution

More information

Aliasing, Image Sampling and Reconstruction

Aliasing, Image Sampling and Reconstruction Aliasing, Image Sampling and Reconstruction Recall: a pixel is a point It is NOT a box, disc or teeny wee light It has no dimension It occupies no area It can have a coordinate More than a point, it is

More information

Lecture 12: Cameras and Geometry. CAP 5415 Fall 2010

Lecture 12: Cameras and Geometry. CAP 5415 Fall 2010 Lecture 12: Cameras and Geometry CAP 5415 Fall 2010 The midterm What does the response of a derivative filter tell me about whether there is an edge or not? Things aren't working Did you look at the filters?

More information

Noise Reduction in Video Images Using Coring on QMF Pyramids by. Arthur J. Kalb

Noise Reduction in Video Images Using Coring on QMF Pyramids by. Arthur J. Kalb Noise Reduction in Video Images Using Coring on QMF Pyramids by Arthur J. Kalb Submitted to the Department of Electrical Engineering and Computer Science on May 20, 1991, in partial fulfillment of the

More information

Computational Optical Imaging - Optique Numerique. -- Deconvolution --

Computational Optical Imaging - Optique Numerique. -- Deconvolution -- Computational Optical Imaging - Optique Numerique -- Deconvolution -- Winter 2014 Ivo Ihrke Deconvolution Ivo Ihrke Outline Deconvolution Theory example 1D deconvolution Fourier method Algebraic method

More information

CHAPTER 6 Frequency Response, Bode Plots, and Resonance

CHAPTER 6 Frequency Response, Bode Plots, and Resonance ELECTRICAL CHAPTER 6 Frequency Response, Bode Plots, and Resonance 1. State the fundamental concepts of Fourier analysis. 2. Determine the output of a filter for a given input consisting of sinusoidal

More information

Color Image Processing

Color Image Processing Color Image Processing What is color? Selective emission/reflectance of different wavelengths What is color? Illumination Reflectance What is color stimuli? X Illumination Reflectance What is perceived

More information

LIST OF CONTENTS CHAPTER CONTENT PAGE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK

LIST OF CONTENTS CHAPTER CONTENT PAGE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK vii LIST OF CONTENTS CHAPTER CONTENT PAGE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK LIST OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF NOTATIONS LIST OF ABBREVIATIONS LIST OF APPENDICES

More information

GETTING STARTED WITH LABVIEW POINT-BY-POINT VIS

GETTING STARTED WITH LABVIEW POINT-BY-POINT VIS USER GUIDE GETTING STARTED WITH LABVIEW POINT-BY-POINT VIS Contents Using the LabVIEW Point-By-Point VI Libraries... 2 Initializing Point-By-Point VIs... 3 Frequently Asked Questions... 5 What Are the

More information

Euler Vector: A Combinatorial Signature for Gray-Tone Images

Euler Vector: A Combinatorial Signature for Gray-Tone Images Euler Vector: A Combinatorial Signature for Gray-Tone Images Arijit Bishnu, Bhargab B. Bhattacharya y, Malay K. Kundu, C. A. Murthy fbishnu t, bhargab, malay, murthyg@isical.ac.in Indian Statistical Institute,

More information

Project 3: Image Enhancement - Spatial vs. Frequency Domain Filters. Steven Young: ECE 572

Project 3: Image Enhancement - Spatial vs. Frequency Domain Filters. Steven Young: ECE 572 Project 3: Image Enhancement - Spatial vs. Frequency Domain Filters Steven Young: ECE 572 Due: October 3, 20 Abstract The purpose of this project is to explore some simple image enhancement algorithms.

More information

Color to Grayscale Conversion with Chrominance Contrast

Color to Grayscale Conversion with Chrominance Contrast Color to Grayscale Conversion with Chrominance Contrast Yuting Ye University of Virginia Figure 1: The sun in Monet s Impression Sunrise has similar luminance as the sky. It can hardly be seen when the

More information

STATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI

STATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI STATS8: Introduction to Biostatistics Data Exploration Babak Shahbaba Department of Statistics, UCI Introduction After clearly defining the scientific problem, selecting a set of representative members

More information

Image Normalization for Illumination Compensation in Facial Images

Image Normalization for Illumination Compensation in Facial Images Image Normalization for Illumination Compensation in Facial Images by Martin D. Levine, Maulin R. Gandhi, Jisnu Bhattacharyya Department of Electrical & Computer Engineering & Center for Intelligent Machines

More information

Image Compression Using Gabor Filter

Image Compression Using Gabor Filter Mr.B.H.Deokate, Dr. P. M. Patil and Mr.S.S. Majgaonkar 28 Image Compression Using Gabor Filter Mr.B.H.Deokate, Dr. P. M. Patil and Mr.S.S. Majgaonkar Abstract: The data is the basic component of information.

More information

L9: Cepstral analysis

L9: Cepstral analysis L9: Cepstral analysis The cepstrum Homomorphic filtering The cepstrum and voicing/pitch detection Linear prediction cepstral coefficients Mel frequency cepstral coefficients This lecture is based on [Taylor,

More information

Lecture 9. Poles, Zeros & Filters (Lathi 4.10) Effects of Poles & Zeros on Frequency Response (1) Effects of Poles & Zeros on Frequency Response (3)

Lecture 9. Poles, Zeros & Filters (Lathi 4.10) Effects of Poles & Zeros on Frequency Response (1) Effects of Poles & Zeros on Frequency Response (3) Effects of Poles & Zeros on Frequency Response (1) Consider a general system transfer function: zeros at z1, z2,..., zn Lecture 9 Poles, Zeros & Filters (Lathi 4.10) The value of the transfer function

More information

Laboratory #5: RF Filter Design

Laboratory #5: RF Filter Design EEE 194 RF Laboratory Exercise 5 1 Laboratory #5: RF Filter Design I. OBJECTIVES A. Design a third order low-pass Chebyshev filter with a cutoff frequency of 330 MHz and 3 db ripple with equal terminations

More information

Introduction to Robotics Analysis, Systems, Applications

Introduction to Robotics Analysis, Systems, Applications Introduction to Robotics Analysis, Systems, Applications Saeed B. Niku Mechanical Engineering Department California Polytechnic State University San Luis Obispo Technische Urw/carsMt Darmstadt FACHBEREfCH

More information

Frequency Domain Characterization of Signals. Yao Wang Polytechnic University, Brooklyn, NY11201 http: //eeweb.poly.edu/~yao

Frequency Domain Characterization of Signals. Yao Wang Polytechnic University, Brooklyn, NY11201 http: //eeweb.poly.edu/~yao Frequency Domain Characterization of Signals Yao Wang Polytechnic University, Brooklyn, NY1121 http: //eeweb.poly.edu/~yao Signal Representation What is a signal Time-domain description Waveform representation

More information

Analog Filters. A common instrumentation filter application is the attenuation of high frequencies to avoid frequency aliasing in the sampled data.

Analog Filters. A common instrumentation filter application is the attenuation of high frequencies to avoid frequency aliasing in the sampled data. Analog Filters Filters can be used to attenuate unwanted signals such as interference or noise or to isolate desired signals from unwanted. They use the frequency response of a measuring system to alter

More information

Low Contrast Image Enhancement Based On Undecimated Wavelet Transform with SSR

Low Contrast Image Enhancement Based On Undecimated Wavelet Transform with SSR International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Issue-02 E-ISSN: 2347-2693 Low Contrast Image Enhancement Based On Undecimated Wavelet Transform with SSR

More information

Armstrong Atlantic State University Engineering Studies MATLAB Marina Image Processing Primer

Armstrong Atlantic State University Engineering Studies MATLAB Marina Image Processing Primer Armstrong Atlantic State University Engineering Studies MATLAB Marina Image Processing Primer Prerequisites The Image Processing Primer assumes nowledge of the MATLAB IDE, MATLAB help, arithmetic operations,

More information

Determining optimal window size for texture feature extraction methods

Determining optimal window size for texture feature extraction methods IX Spanish Symposium on Pattern Recognition and Image Analysis, Castellon, Spain, May 2001, vol.2, 237-242, ISBN: 84-8021-351-5. Determining optimal window size for texture feature extraction methods Domènec

More information

Analog Signal Conditioning

Analog Signal Conditioning Analog Signal Conditioning Analog and Digital Electronics Electronics Digital Electronics Analog Electronics 2 Analog Electronics Analog Electronics Operational Amplifiers Transistors TRIAC 741 LF351 TL084

More information

CHAPTER 3: DIGITAL IMAGING IN DIAGNOSTIC RADIOLOGY. 3.1 Basic Concepts of Digital Imaging

CHAPTER 3: DIGITAL IMAGING IN DIAGNOSTIC RADIOLOGY. 3.1 Basic Concepts of Digital Imaging Physics of Medical X-Ray Imaging (1) Chapter 3 CHAPTER 3: DIGITAL IMAGING IN DIAGNOSTIC RADIOLOGY 3.1 Basic Concepts of Digital Imaging Unlike conventional radiography that generates images on film through

More information

Agilent Time Domain Analysis Using a Network Analyzer

Agilent Time Domain Analysis Using a Network Analyzer Agilent Time Domain Analysis Using a Network Analyzer Application Note 1287-12 0.0 0.045 0.6 0.035 Cable S(1,1) 0.4 0.2 Cable S(1,1) 0.025 0.015 0.005 0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Frequency (GHz) 0.005

More information

High Quality Image Magnification using Cross-Scale Self-Similarity

High Quality Image Magnification using Cross-Scale Self-Similarity High Quality Image Magnification using Cross-Scale Self-Similarity André Gooßen 1, Arne Ehlers 1, Thomas Pralow 2, Rolf-Rainer Grigat 1 1 Vision Systems, Hamburg University of Technology, D-21079 Hamburg

More information

Probability and Random Variables. Generation of random variables (r.v.)

Probability and Random Variables. Generation of random variables (r.v.) Probability and Random Variables Method for generating random variables with a specified probability distribution function. Gaussian And Markov Processes Characterization of Stationary Random Process Linearly

More information

MATLAB-based Applications for Image Processing and Image Quality Assessment Part II: Experimental Results

MATLAB-based Applications for Image Processing and Image Quality Assessment Part II: Experimental Results 154 L. KRASULA, M. KLÍMA, E. ROGARD, E. JEANBLANC, MATLAB BASED APPLICATIONS PART II: EXPERIMENTAL RESULTS MATLAB-based Applications for Image Processing and Image Quality Assessment Part II: Experimental

More information

Chapter 3 Joint Distributions

Chapter 3 Joint Distributions Chapter 3 Joint Distributions 3.6 Functions of Jointly Distributed Random Variables Discrete Random Variables: Let f(x, y) denote the joint pdf of random variables X and Y with A denoting the two-dimensional

More information

Image Gradients. Given a discrete image Á Òµ, consider the smoothed continuous image ܵ defined by

Image Gradients. Given a discrete image Á Òµ, consider the smoothed continuous image ܵ defined by Image Gradients Given a discrete image Á Òµ, consider the smoothed continuous image ܵ defined by ܵ Ü ¾ Ö µ Á Òµ Ü ¾ Ö µá µ (1) where Ü ¾ Ö Ô µ Ü ¾ Ý ¾. ½ ¾ ¾ Ö ¾ Ü ¾ ¾ Ö. Here Ü is the 2-norm for the

More information

Signature Region of Interest using Auto cropping

Signature Region of Interest using Auto cropping ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 1 Signature Region of Interest using Auto cropping Bassam Al-Mahadeen 1, Mokhled S. AlTarawneh 2 and Islam H. AlTarawneh 2 1 Math. And Computer Department,

More information

Tutorial. Filtering Images F I L T E R I N G. Filtering Images. with. TNTmips. page 1

Tutorial. Filtering Images F I L T E R I N G. Filtering Images. with. TNTmips. page 1 F I L T E R I N G Tutorial Filtering Images Filtering Images with TNTmips page 1 Filtering Images Before Getting Started In working with digital forms of aerial photographs or satellite imagery, you will

More information

Some elements of photo. interpretation

Some elements of photo. interpretation Some elements of photo Shape Size Pattern Color (tone, hue) Texture Shadows Site Association interpretation Olson, C. E., Jr. 1960. Elements of photographic interpretation common to several sensors. Photogrammetric

More information

Object Recognition and Template Matching

Object Recognition and Template Matching Object Recognition and Template Matching Template Matching A template is a small image (sub-image) The goal is to find occurrences of this template in a larger image That is, you want to find matches of

More information

A Comparison of Five Methods for Signal Intensity Standardization in MRI

A Comparison of Five Methods for Signal Intensity Standardization in MRI A Comparison of Five Methods for Signal Intensity Standardization in MRI Jan-Philip Bergeest, Florian Jäger Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität Erlangen-Nürnberg jan.p.bergeest@informatik.stud.uni-erlangen.de

More information

Simultaneous Gamma Correction and Registration in the Frequency Domain

Simultaneous Gamma Correction and Registration in the Frequency Domain Simultaneous Gamma Correction and Registration in the Frequency Domain Alexander Wong a28wong@uwaterloo.ca William Bishop wdbishop@uwaterloo.ca Department of Electrical and Computer Engineering University

More information

Defect detection of gold-plated surfaces on PCBs using Entropy measures

Defect detection of gold-plated surfaces on PCBs using Entropy measures Defect detection of gold-plated surfaces on PCBs using ntropy measures D. M. Tsai and B. T. Lin Machine Vision Lab. Department of Industrial ngineering and Management Yuan-Ze University, Chung-Li, Taiwan,

More information

Chapter 4 Image Enhancement in the Frequency Domain. Chapter 4 Image Enhancement in the Frequency Domain

Chapter 4 Image Enhancement in the Frequency Domain. Chapter 4 Image Enhancement in the Frequency Domain Fourier Transform Filtering Low-pass, High-pass, Butterworth, Gaussian Laplacian, High-boost, Homomorphic Properties of FT and DFT Transforms 4.1 4. 1 u 4.3 Fourier series states that a periodic function

More information

A BRIEF STUDY OF VARIOUS NOISE MODEL AND FILTERING TECHNIQUES

A BRIEF STUDY OF VARIOUS NOISE MODEL AND FILTERING TECHNIQUES Volume 4, No. 4, April 2013 Journal of Global Research in Computer Science REVIEW ARTICLE Available Online at www.jgrcs.info A BRIEF STUDY OF VARIOUS NOISE MODEL AND FILTERING TECHNIQUES Priyanka Kamboj

More information

Optical Design for Automatic Identification

Optical Design for Automatic Identification s for Automatic Identification s design tutors: Prof. Paolo Bassi, eng. Federico Canini cotutors: eng. Gnan, eng. Bassam Hallal Outline s 1 2 3 design 4 design Outline s 1 2 3 design 4 design s : new Techniques

More information

Numerical Methods For Image Restoration

Numerical Methods For Image Restoration Numerical Methods For Image Restoration CIRAM Alessandro Lanza University of Bologna, Italy Faculty of Engineering CIRAM Outline 1. Image Restoration as an inverse problem 2. Image degradation models:

More information

Comparative Analysis of various Illumination Normalization Techniques for Face Recognition

Comparative Analysis of various Illumination Normalization Techniques for Face Recognition Comparative Analysis of various Illumination Normalization Techniques for Face Recognition Tripti Goel GPMCE, Delhi Vijay Nehra BPSMV, Khanpur Virendra P.Vishwakarma JIIT, Noida ABSTRACT The change in

More information

Em bedded DSP : I ntroduction to Digital Filters

Em bedded DSP : I ntroduction to Digital Filters Embedded DSP : Introduction to Digital Filters 1 Em bedded DSP : I ntroduction to Digital Filters Digital filters are a important part of DSP. In fact their extraordinary performance is one of the keys

More information

Chapter 2. Point transformation. Look up Table (LUT) Fundamentals of Image processing

Chapter 2. Point transformation. Look up Table (LUT) Fundamentals of Image processing Chapter 2 Fundamentals of Image processing Point transformation Look up Table (LUT) 1 Introduction (1/2) 3 Types of operations in Image Processing - m: rows index - n: column index Point to point transformation

More information

Linear and Non-linear Contrast Enhancement Image

Linear and Non-linear Contrast Enhancement Image IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.2, February 2010 139 Linear and Non-linear Contrast Enhancement Image Mr. Salem Saleh Al-amri 1,Dr.N.V.Kalyankar 2,Dr.S.D.Khamitkar

More information

Transformations and Expectations of random variables

Transformations and Expectations of random variables Transformations and Epectations of random variables X F X (): a random variable X distributed with CDF F X. Any function Y = g(x) is also a random variable. If both X, and Y are continuous random variables,

More information

Sampling Theorem Notes. Recall: That a time sampled signal is like taking a snap shot or picture of signal periodically.

Sampling Theorem Notes. Recall: That a time sampled signal is like taking a snap shot or picture of signal periodically. Sampling Theorem We will show that a band limited signal can be reconstructed exactly from its discrete time samples. Recall: That a time sampled signal is like taking a snap shot or picture of signal

More information

Basics of Quantitative Image Analysis

Basics of Quantitative Image Analysis QuickTime_ and a decompressor areneeded to seethispicture. Basics of Quantitative Image Analysis What you need to know about Image Processing but never thought to ask Before you start writing... See these

More information

Jitter Measurements in Serial Data Signals

Jitter Measurements in Serial Data Signals Jitter Measurements in Serial Data Signals Michael Schnecker, Product Manager LeCroy Corporation Introduction The increasing speed of serial data transmission systems places greater importance on measuring

More information