EE640 FINAL PROJECT HEADS OR TAILS
|
|
- Roy Nichols
- 5 years ago
- Views:
Transcription
1 EE640 FINAL PROJECT HEADS OR TAILS By Laurence Hassebrook Initiated: April 2015, updated April 27 Contents 1. SUMMARY EXPECTATIONS INPUT DATA BASE PREPROCESSING Surface Image Z Smoothing Filters PSD ESTIMATION AND CONTROL DATA DEFINTION... 7 APPENDIX... 7 A. MAT5 formats SUMMARY The final project is a practical application of noise synthesis, analysis and stochastic systems implementation. The primary goal is to simulate a small set of test data and then demonstrate the ability to classify the test data as well as perform parameter estimation. A data base of 16 pennies are grouped as 16 heads and 16 tails. The penny rotation is random and known as in-plane rotation distortion. The penny data is in the form of 3-D scans having x w, y w and z w world coordinates for each points. The data files are stored in a bitmap format, (i.e., mat5 F format ) whose intensity pixels are distributed on a uniform XY displacement. In this way, the bitmap image intensity and z w values are distributed accurately in the XY plane and thus be processed using 2-D image processing techniques including edge enhancement and in-plane rotation. We chose coins as a data base because it is readily identifiable, available and precisely manufactured. The techniques we use are application to numerous applications in industrial inspection, automatic pattern recognition and artificial intelligence. We chose the penny because of its low value and the understanding that we must respect the importance and integrity of currency. 1
2 The random variable within the data is the rotation angle and the random process is the surface noise. There is also some feature variation between coins. When we test the data we will be evaluating our ability to discriminate between heads and tails as well as estimating the rotation angle of the test data. We will first select a single head and a single tail scan to be used to develop our classifiers and angle estimators. From these two coin scans, we will select a preprocessing algorithm that tends to orthogonalize the data. From that we will estimate the PSDs of the coins. Using white Gaussian noise images and a filter based on the PSD results, we synthesize a training set of control images representing the rotation distortion and the PSD of the heads and tails data. Using the training set synthesis, we determine the maximum angle between adjacent rotations which in turn determines the minimum size of the training set. Given the training set correlation matrix, we form a filter bank based on the correlation matrix. From that we form a maximum likelihood ration based filter bank for discriminating between heads and tails based on the Rayleigh quotient. After classification we use the associated filters to estimate the rotation angle. 2. EXPECTATIONS The students are expected to turn in a single report per group. Each section starts with a definition of what the student needs to show or implement. This may include mathematical derivation and/or MATLAB implementation. The student will be provided with existing MATLAB code to minimize development of software not directly related to the study of stochastic systems. 3. INPUT DATA BASE The student should select one mat5 data set for heads and one for tails to represent that particular class. The data was scanned using the multi-frequency Phase Measuring Profilometry (PMP) [1] method. The format of the data is in MAT5 format [2] described in Appendix A. Use PreviewDataSet.m to preview the data set. There are several controls in PreviewDataSet.m: Targetselect: Index of selected target which varies between 0 and (N-1) TurnOnImage: 0 for original images, 1 for median normalized gain and 2 for surface normal. TurnOnEdge: 0 for Z depth, 1 for Sobel with clipping threshold, and 2 for Moving Least Squares filter. SobelClip: The Sobel edge enhancement clips result based on SobelClip times Sobel peak. So any values greater than SobelClip are set to the value of SobelClip. TurnOnZSmoothing: 0 for no smoothing of Z coordinates, 1 for smoothing Z using filter. FilterType: Smoothing filter type. 0 for none, 1 for Moving Average Rectangle and 2 for Gaussian. MAfilter: Width of MA filter and should be an odd number to be symmetric. GausfilterSTD: Gaussian filter parameter which controls shape width. 2
3 two controls, and which control if the images are original, metallic, depth, sobel edgehance Z and Moving Least Square Filtering. Figure 1: Data set of penny heads with TurnOnImage=0. 3
4 Figure 2: Data set of penny tails, C matrix, with TurnOnImage=0. 4. PREPROCESSING The data is pre-processed before viewing and usage. The student should indicate and show their enhanced images selected as the training images for the target (i.e., heads) and clutter (i.e., tails) classes. Indicate what filtering parameters are used and which enhancement methods are used. 4.1 Surface Image For viewing, the C matrix can be used in original form (TurnOnImage=0) or median normalized (TurnOnImage=1). Both options for both heads and tails of mat5 C matrix 0, are shown in Fig. 3. For the median normalization, we select all pixels for I > ithresh and find the median value. All pixels are scaled such that the median value is 128. The image is clipped to be between 0 and
5 Figure 3: Image C of mat5 index 0. (top, left) Original heads image. (bottom, left) Median normalized head. (top, right) Original tail. (bottom, right) Median normalized tail. 4.2 Z Smoothing Filters The input Z data can be smoothed with two types of linear filters. By controlling the filter parameter, the smoothing of the Z surface can improve the results of the edge enhancement processes. In the mathematical equations we ignore the circular wrap that the finite image space requires. In implementation, the origin is in the corner and is then wrapped around. For FilterType=1, a Moving Average (MA) filter is convolved with the Z data matrix. The MA filter is a M a x M a rectangular filter where M a = MAfilter. The MA filter is defined as h rect m M rect M a m, n 2 a n M a and is convolved with the input Z matrix such that m, n Zm, n hm n Z s,. For FilterType=2, a Gaussian filter is convolved with the Z data matrix. 5
6 h 1 E 2 2 m, n exp1 m n where E is E 2 M y 1 N x 1 m0 n0 h m, n 2 Preview data offers two types of edge enhancement, Sobel edge enhancement and a differencing Moving Least Squares filter. Both enhancement methods tend to orthogonalize the data which should improve discrimination performance. Both are also sensitive to noise which are affected by the smoothing process. The trade off in smoothing is too much will blur the features needed for discrimination versus allowing surface noise that reduces performance. Results for both methods and both classes are shown in Fig. 4. Figure 4: (left column) Target image. (right column) Clutter image. (Top row) Sobel edge enhancement. (Bottom row) MLS filtering. 6
7 5. PSD ESTIMATION AND CONTROL DATA DEFINTION Based on the selected target and clutter preprocessed images, we synthesize control data to be colored stationary noise having the same PSD as the target and clutter classes, respectively. RANDOM INPLANE ROTATION AND THE CORRELATION MATRIX COMPOSITE CORRELATION FILTER BANK DETECTION AND DISCRIMINATION ROTATION ANGLE ESTIMATION REFERENCES 1. Jie-lin Li, L.G. Hassebrook and Chun Guan, Optimized Two-Frequency Phase-Measuring-Profilometry Light-Sensor Temporal-Noise Sensitivity, JOSA A, 20(1), , (2003). 2. MAT5 format description: 3. W. J. Chimitt and L.G. Hassebrook, Scene reconstruction from partially overlapping images with use of composite filters, JOSA A, 16(9), , September (1999). 4. L. G. Hassebrook, B.V.K. Vijaya Kumar and L. Hostetler, "Linear Phase Coefficient Composite Filter Banks for Distortion-Invariant Optical Pattern Recognition," Optical Engineering, 29, , (Sept. 1990). 5. L. G. Hassebrook, M. E. Lhamon, M. Wang and J. Chatterjee, "Postprocessing of Correlation for Orientation Estimation," Optical Engineering, 36(10), , October, (1997). APPENDIX A. MAT5 formats The mat5 format consists of 5 matrices embedded into a single 32 bit Bitmap file. The mat5 image is My rows by Nx columns. The matrices are a color matrix C, an indicator matrix I, X world coordinate matrix X, Y world coordinate matrix Y and Z world coordinate matrix Z. Matrix Dimensions Element format C My x Nx x 3 3 bytes representing RGB I My x Nx 1 byte representing quality X My x Nx 4 byte float representing X world coordinates Y My x Nx 4 byte float representing Y world coordinates Z My x Nx 4 byte float representing Z world coordinates Given a threshold ithresh the valid elements are where I(m,n) >=ithresh where I(m,n) varies between 0 and
Improving the Discrimination Capability with an Adaptive Synthetic Discriminant Function Filter
Improving the Discrimination Capability with an Adaptive Synthetic Discriminant Function Filter 83 J. Ángel González-Fraga 1, Víctor H. Díaz-Ramírez 1, Vitaly Kober 1, and Josué Álvarez-Borrego 2 1 Department
More informationFiltering Images. Contents
Image Processing and Data Visualization with MATLAB Filtering Images Hansrudi Noser June 8-9, 010 UZH, Multimedia and Robotics Summer School Noise Smoothing Filters Sigmoid Filters Gradient Filters Contents
More informationMultimedia Computing: Algorithms, Systems, and Applications: Edge Detection
Multimedia Computing: Algorithms, Systems, and Applications: Edge Detection By Dr. Yu Cao Department of Computer Science The University of Massachusetts Lowell Lowell, MA 01854, USA Part of the slides
More informationCoE4TN4 Image Processing. Chapter 5 Image Restoration and Reconstruction
CoE4TN4 Image Processing Chapter 5 Image Restoration and Reconstruction Image Restoration Similar to image enhancement, the ultimate goal of restoration techniques is to improve an image Restoration: a
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 WRI C225 Lecture 04 130131 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Histogram Equalization Image Filtering Linear
More informationEdge Detection. Announcements. Edge detection. Origin of Edges. Mailing list: you should have received messages
Announcements Mailing list: csep576@cs.washington.edu you should have received messages Project 1 out today (due in two weeks) Carpools Edge Detection From Sandlot Science Today s reading Forsyth, chapters
More informationMultiple target detection in video using quadratic multi-frame correlation filtering
Multiple target detection in video using quadratic multi-frame correlation filtering Ryan Kerekes Oak Ridge National Laboratory B. V. K. Vijaya Kumar Carnegie Mellon University March 17, 2008 1 Outline
More informationELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Review of Motion Modelling and Estimation Introduction to Motion Modelling & Estimation Forward Motion Backward Motion Block Motion Estimation Motion
More informationImage Processing. BITS Pilani. Dr Jagadish Nayak. Dubai Campus
Image Processing BITS Pilani Dubai Campus Dr Jagadish Nayak Image Segmentation BITS Pilani Dubai Campus Fundamentals Let R be the entire spatial region occupied by an image Process that partitions R into
More information2D Image Processing INFORMATIK. Kaiserlautern University. DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
2D Image Processing - Filtering Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 What is image filtering?
More informationMidterm Examination CS 534: Computational Photography
Midterm Examination CS 534: Computational Photography November 3, 2016 NAME: Problem Score Max Score 1 6 2 8 3 9 4 12 5 4 6 13 7 7 8 6 9 9 10 6 11 14 12 6 Total 100 1 of 8 1. [6] (a) [3] What camera setting(s)
More informationAuto-Digitizer for Fast Graph-to-Data Conversion
Auto-Digitizer for Fast Graph-to-Data Conversion EE 368 Final Project Report, Winter 2018 Deepti Sanjay Mahajan dmahaj@stanford.edu Sarah Pao Radzihovsky sradzi13@stanford.edu Ching-Hua (Fiona) Wang chwang9@stanford.edu
More informationIllumination invariant face recognition and impostor rejection using different MINACE filter algorithms
Illumination invariant face recognition and impostor rejection using different MINACE filter algorithms Rohit Patnaik and David Casasent Dept. of Electrical and Computer Engineering, Carnegie Mellon University,
More informationFinal Project Report: Filterbank-Based Fingerprint Matching
Sabanci University TE 407 Digital Image Processing Final Project Report: Filterbank-Based Fingerprint Matching June 28, 2004 Didem Gözüpek & Onur Sarkan 5265 5241 1 1. Introduction The need for security
More informationComputer Vision 2. SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung. Computer Vision 2 Dr. Benjamin Guthier
Computer Vision 2 SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung Computer Vision 2 Dr. Benjamin Guthier 1. IMAGE PROCESSING Computer Vision 2 Dr. Benjamin Guthier Content of this Chapter Non-linear
More informationAnno accademico 2006/2007. Davide Migliore
Robotica Anno accademico 6/7 Davide Migliore migliore@elet.polimi.it Today What is a feature? Some useful information The world of features: Detectors Edges detection Corners/Points detection Descriptors?!?!?
More informationFPGA IMPLEMENTATION FOR REAL TIME SOBEL EDGE DETECTOR BLOCK USING 3-LINE BUFFERS
FPGA IMPLEMENTATION FOR REAL TIME SOBEL EDGE DETECTOR BLOCK USING 3-LINE BUFFERS 1 RONNIE O. SERFA JUAN, 2 CHAN SU PARK, 3 HI SEOK KIM, 4 HYEONG WOO CHA 1,2,3,4 CheongJu University E-maul: 1 engr_serfs@yahoo.com,
More informationCHAPTER 2 TEXTURE CLASSIFICATION METHODS GRAY LEVEL CO-OCCURRENCE MATRIX AND TEXTURE UNIT
CHAPTER 2 TEXTURE CLASSIFICATION METHODS GRAY LEVEL CO-OCCURRENCE MATRIX AND TEXTURE UNIT 2.1 BRIEF OUTLINE The classification of digital imagery is to extract useful thematic information which is one
More informationAdvanced phase retrieval: maximum likelihood technique with sparse regularization of phase and amplitude
Advanced phase retrieval: maximum likelihood technique with sparse regularization of phase and amplitude A. Migukin *, V. atkovnik and J. Astola Department of Signal Processing, Tampere University of Technology,
More informationDigital Image Processing
Digital Image Processing Part 9: Representation and Description AASS Learning Systems Lab, Dep. Teknik Room T1209 (Fr, 11-12 o'clock) achim.lilienthal@oru.se Course Book Chapter 11 2011-05-17 Contents
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 WRI C225 Lecture 02 130124 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Basics Image Formation Image Processing 3 Intelligent
More informationEXAM SOLUTIONS. Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006,
School of Computer Science and Communication, KTH Danica Kragic EXAM SOLUTIONS Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006, 14.00 19.00 Grade table 0-25 U 26-35 3 36-45
More informationBioimage Informatics
Bioimage Informatics Lecture 13, Spring 2012 Bioimage Data Analysis (IV) Image Segmentation (part 2) Lecture 13 February 29, 2012 1 Outline Review: Steger s line/curve detection algorithm Intensity thresholding
More informationAlgorithms for Recognition of Low Quality Iris Images. Li Peng Xie University of Ottawa
Algorithms for Recognition of Low Quality Iris Images Li Peng Xie University of Ottawa Overview Iris Recognition Eyelash detection Accurate circular localization Covariance feature with LDA Fourier magnitude
More informationNoise Model. Important Noise Probability Density Functions (Cont.) Important Noise Probability Density Functions
Others -- Noise Removal Techniques -- Edge Detection Techniques -- Geometric Operations -- Color Image Processing -- Color Spaces Xiaojun Qi Noise Model The principal sources of noise in digital images
More informationSECTION 5 IMAGE PROCESSING 2
SECTION 5 IMAGE PROCESSING 2 5.1 Resampling 3 5.1.1 Image Interpolation Comparison 3 5.2 Convolution 3 5.3 Smoothing Filters 3 5.3.1 Mean Filter 3 5.3.2 Median Filter 4 5.3.3 Pseudomedian Filter 6 5.3.4
More informationLearning and Inferring Depth from Monocular Images. Jiyan Pan April 1, 2009
Learning and Inferring Depth from Monocular Images Jiyan Pan April 1, 2009 Traditional ways of inferring depth Binocular disparity Structure from motion Defocus Given a single monocular image, how to infer
More informationCS4442/9542b Artificial Intelligence II prof. Olga Veksler
CS4442/9542b Artificial Intelligence II prof. Olga Veksler Lecture 2 Computer Vision Introduction, Filtering Some slides from: D. Jacobs, D. Lowe, S. Seitz, A.Efros, X. Li, R. Fergus, J. Hayes, S. Lazebnik,
More informationEdge detection. Convert a 2D image into a set of curves. Extracts salient features of the scene More compact than pixels
Edge Detection Edge detection Convert a 2D image into a set of curves Extracts salient features of the scene More compact than pixels Origin of Edges surface normal discontinuity depth discontinuity surface
More informationCamera Calibration. Schedule. Jesus J Caban. Note: You have until next Monday to let me know. ! Today:! Camera calibration
Camera Calibration Jesus J Caban Schedule! Today:! Camera calibration! Wednesday:! Lecture: Motion & Optical Flow! Monday:! Lecture: Medical Imaging! Final presentations:! Nov 29 th : W. Griffin! Dec 1
More informationOutline 7/2/201011/6/
Outline Pattern recognition in computer vision Background on the development of SIFT SIFT algorithm and some of its variations Computational considerations (SURF) Potential improvement Summary 01 2 Pattern
More informationFinal Exam Assigned: 11/21/02 Due: 12/05/02 at 2:30pm
6.801/6.866 Machine Vision Final Exam Assigned: 11/21/02 Due: 12/05/02 at 2:30pm Problem 1 Line Fitting through Segmentation (Matlab) a) Write a Matlab function to generate noisy line segment data with
More informationRobot vision review. Martin Jagersand
Robot vision review Martin Jagersand What is Computer Vision? Computer Graphics Three Related fields Image Processing: Changes 2D images into other 2D images Computer Graphics: Takes 3D models, renders
More informationEstimating the wavelength composition of scene illumination from image data is an
Chapter 3 The Principle and Improvement for AWB in DSC 3.1 Introduction Estimating the wavelength composition of scene illumination from image data is an important topics in color engineering. Solutions
More informationComparative Study of ROI Extraction of Palmprint
251 Comparative Study of ROI Extraction of Palmprint 1 Milind E. Rane, 2 Umesh S Bhadade 1,2 SSBT COE&T, North Maharashtra University Jalgaon, India Abstract - The Palmprint region segmentation is an important
More informationFinal Review CMSC 733 Fall 2014
Final Review CMSC 733 Fall 2014 We have covered a lot of material in this course. One way to organize this material is around a set of key equations and algorithms. You should be familiar with all of these,
More informationCorner Detection. Harvey Rhody Chester F. Carlson Center for Imaging Science Rochester Institute of Technology
Corner Detection Harvey Rhody Chester F. Carlson Center for Imaging Science Rochester Institute of Technology rhody@cis.rit.edu April 11, 2006 Abstract Corners and edges are two of the most important geometrical
More informationChapter 3 Image Registration. Chapter 3 Image Registration
Chapter 3 Image Registration Distributed Algorithms for Introduction (1) Definition: Image Registration Input: 2 images of the same scene but taken from different perspectives Goal: Identify transformation
More informationImage Processing Fundamentals. Nicolas Vazquez Principal Software Engineer National Instruments
Image Processing Fundamentals Nicolas Vazquez Principal Software Engineer National Instruments Agenda Objectives and Motivations Enhancing Images Checking for Presence Locating Parts Measuring Features
More informationLecture 8 Object Descriptors
Lecture 8 Object Descriptors Azadeh Fakhrzadeh Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University 2 Reading instructions Chapter 11.1 11.4 in G-W Azadeh Fakhrzadeh
More informationECG782: Multidimensional Digital Signal Processing
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spatial Domain Filtering http://www.ee.unlv.edu/~b1morris/ecg782/ 2 Outline Background Intensity
More informationSchool of Computing University of Utah
School of Computing University of Utah Presentation Outline 1 2 3 4 Main paper to be discussed David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, IJCV, 2004. How to find useful keypoints?
More informationImage Enhancement Techniques for Fingerprint Identification
March 2013 1 Image Enhancement Techniques for Fingerprint Identification Pankaj Deshmukh, Siraj Pathan, Riyaz Pathan Abstract The aim of this paper is to propose a new method in fingerprint enhancement
More informationEdge and local feature detection - 2. Importance of edge detection in computer vision
Edge and local feature detection Gradient based edge detection Edge detection by function fitting Second derivative edge detectors Edge linking and the construction of the chain graph Edge and local feature
More informationWikipedia - Mysid
Wikipedia - Mysid Erik Brynjolfsson, MIT Filtering Edges Corners Feature points Also called interest points, key points, etc. Often described as local features. Szeliski 4.1 Slides from Rick Szeliski,
More informationPupil Localization Algorithm based on Hough Transform and Harris Corner Detection
Pupil Localization Algorithm based on Hough Transform and Harris Corner Detection 1 Chongqing University of Technology Electronic Information and Automation College Chongqing, 400054, China E-mail: zh_lian@cqut.edu.cn
More informationIntroduction to Medical Imaging (5XSA0)
1 Introduction to Medical Imaging (5XSA0) Visual feature extraction Color and texture analysis Sveta Zinger ( s.zinger@tue.nl ) Introduction (1) Features What are features? Feature a piece of information
More informationCS4442/9542b Artificial Intelligence II prof. Olga Veksler
CS4442/9542b Artificial Intelligence II prof. Olga Veksler Lecture 8 Computer Vision Introduction, Filtering Some slides from: D. Jacobs, D. Lowe, S. Seitz, A.Efros, X. Li, R. Fergus, J. Hayes, S. Lazebnik,
More informationClassification of image operations. Image enhancement (GW-Ch. 3) Point operations. Neighbourhood operation
Image enhancement (GW-Ch. 3) Classification of image operations Process of improving image quality so that the result is more suitable for a specific application. contrast stretching histogram processing
More informationPhoto-realistic Renderings for Machines Seong-heum Kim
Photo-realistic Renderings for Machines 20105034 Seong-heum Kim CS580 Student Presentations 2016.04.28 Photo-realistic Renderings for Machines Scene radiances Model descriptions (Light, Shape, Material,
More informationRigid ICP registration with Kinect
Rigid ICP registration with Kinect Students: Yoni Choukroun, Elie Semmel Advisor: Yonathan Aflalo 1 Overview.p.3 Development of the project..p.3 Papers p.4 Project algorithm..p.6 Result of the whole body.p.7
More informationMOVING OBJECT DETECTION USING BACKGROUND SUBTRACTION ALGORITHM USING SIMULINK
MOVING OBJECT DETECTION USING BACKGROUND SUBTRACTION ALGORITHM USING SIMULINK Mahamuni P. D 1, R. P. Patil 2, H.S. Thakar 3 1 PG Student, E & TC Department, SKNCOE, Vadgaon Bk, Pune, India 2 Asst. Professor,
More informationBCC Rays Ripply Filter
BCC Rays Ripply Filter The BCC Rays Ripply filter combines a light rays effect with a rippled light effect. The resulting light is generated from a selected channel in the source image and spreads from
More informationUNIVERSITY OF OSLO. Faculty of Mathematics and Natural Sciences
UNIVERSITY OF OSLO Faculty of Mathematics and Natural Sciences Exam: INF 4300 / INF 9305 Digital image analysis Date: Thursday December 21, 2017 Exam hours: 09.00-13.00 (4 hours) Number of pages: 8 pages
More informationCITS 4402 Computer Vision
CITS 4402 Computer Vision A/Prof Ajmal Mian Adj/A/Prof Mehdi Ravanbakhsh, CEO at Mapizy (www.mapizy.com) and InFarm (www.infarm.io) Lecture 02 Binary Image Analysis Objectives Revision of image formation
More informationEE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm
EE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm Group 1: Mina A. Makar Stanford University mamakar@stanford.edu Abstract In this report, we investigate the application of the Scale-Invariant
More information5. Feature Extraction from Images
5. Feature Extraction from Images Aim of this Chapter: Learn the Basic Feature Extraction Methods for Images Main features: Color Texture Edges Wie funktioniert ein Mustererkennungssystem Test Data x i
More informationFace Tracking : An implementation of the Kanade-Lucas-Tomasi Tracking algorithm
Face Tracking : An implementation of the Kanade-Lucas-Tomasi Tracking algorithm Dirk W. Wagener, Ben Herbst Department of Applied Mathematics, University of Stellenbosch, Private Bag X1, Matieland 762,
More informationSegmentation and Grouping
Segmentation and Grouping How and what do we see? Fundamental Problems ' Focus of attention, or grouping ' What subsets of pixels do we consider as possible objects? ' All connected subsets? ' Representation
More informationLecture 7: Most Common Edge Detectors
#1 Lecture 7: Most Common Edge Detectors Saad Bedros sbedros@umn.edu Edge Detection Goal: Identify sudden changes (discontinuities) in an image Intuitively, most semantic and shape information from the
More informationA Low Power, High Throughput, Fully Event-Based Stereo System: Supplementary Documentation
A Low Power, High Throughput, Fully Event-Based Stereo System: Supplementary Documentation Alexander Andreopoulos, Hirak J. Kashyap, Tapan K. Nayak, Arnon Amir, Myron D. Flickner IBM Research March 25,
More informationA Novel Image Super-resolution Reconstruction Algorithm based on Modified Sparse Representation
, pp.162-167 http://dx.doi.org/10.14257/astl.2016.138.33 A Novel Image Super-resolution Reconstruction Algorithm based on Modified Sparse Representation Liqiang Hu, Chaofeng He Shijiazhuang Tiedao University,
More informationThe SIFT (Scale Invariant Feature
The SIFT (Scale Invariant Feature Transform) Detector and Descriptor developed by David Lowe University of British Columbia Initial paper ICCV 1999 Newer journal paper IJCV 2004 Review: Matt Brown s Canonical
More informationHISTOGRAMS OF ORIENTATIO N GRADIENTS
HISTOGRAMS OF ORIENTATIO N GRADIENTS Histograms of Orientation Gradients Objective: object recognition Basic idea Local shape information often well described by the distribution of intensity gradients
More informationStereo imaging ideal geometry
Stereo imaging ideal geometry (X,Y,Z) Z f (x L,y L ) f (x R,y R ) Optical axes are parallel Optical axes separated by baseline, b. Line connecting lens centers is perpendicular to the optical axis, and
More informationSIMULATIVE ANALYSIS OF EDGE DETECTION OPERATORS AS APPLIED FOR ROAD IMAGES
SIMULATIVE ANALYSIS OF EDGE DETECTION OPERATORS AS APPLIED FOR ROAD IMAGES Sukhpreet Kaur¹, Jyoti Saxena² and Sukhjinder Singh³ ¹Research scholar, ²Professsor and ³Assistant Professor ¹ ² ³ Department
More informationProject Title: Welding Machine Monitoring System Phase II. Name of PI: Prof. Kenneth K.M. LAM (EIE) Progress / Achievement: (with photos, if any)
Address: Hong Kong Polytechnic University, Phase 8, Hung Hom, Kowloon, Hong Kong. Telephone: (852) 3400 8441 Email: cnerc.steel@polyu.edu.hk Website: https://www.polyu.edu.hk/cnerc-steel/ Project Title:
More informationSUMMARY: DISTINCTIVE IMAGE FEATURES FROM SCALE- INVARIANT KEYPOINTS
SUMMARY: DISTINCTIVE IMAGE FEATURES FROM SCALE- INVARIANT KEYPOINTS Cognitive Robotics Original: David G. Lowe, 004 Summary: Coen van Leeuwen, s1460919 Abstract: This article presents a method to extract
More informationFourier Transform and Texture Filtering
Fourier Transform and Texture Filtering Lucas J. van Vliet www.ph.tn.tudelft.nl/~lucas Image Analysis Paradigm scene Image formation sensor pre-processing Image enhancement Image restoration Texture filtering
More informationECG782: Multidimensional Digital Signal Processing
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spring 2014 TTh 14:30-15:45 CBC C313 Lecture 03 Image Processing Basics 13/01/28 http://www.ee.unlv.edu/~b1morris/ecg782/
More informationImage processing. Reading. What is an image? Brian Curless CSE 457 Spring 2017
Reading Jain, Kasturi, Schunck, Machine Vision. McGraw-Hill, 1995. Sections 4.2-4.4, 4.5(intro), 4.5.5, 4.5.6, 5.1-5.4. [online handout] Image processing Brian Curless CSE 457 Spring 2017 1 2 What is an
More informationFeature Tracking and Optical Flow
Feature Tracking and Optical Flow Prof. D. Stricker Doz. G. Bleser Many slides adapted from James Hays, Derek Hoeim, Lana Lazebnik, Silvio Saverse, who 1 in turn adapted slides from Steve Seitz, Rick Szeliski,
More informationProblem definition Image acquisition Image segmentation Connected component analysis. Machine vision systems - 1
Machine vision systems Problem definition Image acquisition Image segmentation Connected component analysis Machine vision systems - 1 Problem definition Design a vision system to see a flat world Page
More informationImage features. Image Features
Image features Image features, such as edges and interest points, provide rich information on the image content. They correspond to local regions in the image and are fundamental in many applications in
More informationComputer Vision I - Basics of Image Processing Part 1
Computer Vision I - Basics of Image Processing Part 1 Carsten Rother 28/10/2014 Computer Vision I: Basics of Image Processing Link to lectures Computer Vision I: Basics of Image Processing 28/10/2014 2
More informationAutonomous Navigation for Flying Robots
Computer Vision Group Prof. Daniel Cremers Autonomous Navigation for Flying Robots Lecture 7.1: 2D Motion Estimation in Images Jürgen Sturm Technische Universität München 3D to 2D Perspective Projections
More informationSkew Detection and Correction of Document Image using Hough Transform Method
Skew Detection and Correction of Document Image using Hough Transform Method [1] Neerugatti Varipally Vishwanath, [2] Dr.T. Pearson, [3] K.Chaitanya, [4] MG JaswanthSagar, [5] M.Rupesh [1] Asst.Professor,
More informationInvariant Recognition of Hand-Drawn Pictograms Using HMMs with a Rotating Feature Extraction
Invariant Recognition of Hand-Drawn Pictograms Using HMMs with a Rotating Feature Extraction Stefan Müller, Gerhard Rigoll, Andreas Kosmala and Denis Mazurenok Department of Computer Science, Faculty of
More informationJNTUWORLD. 4. Prove that the average value of laplacian of the equation 2 h = ((r2 σ 2 )/σ 4 ))exp( r 2 /2σ 2 ) is zero. [16]
Code No: 07A70401 R07 Set No. 2 1. (a) What are the basic properties of frequency domain with respect to the image processing. (b) Define the terms: i. Impulse function of strength a ii. Impulse function
More informationScanner Parameter Estimation Using Bilevel Scans of Star Charts
ICDAR, Seattle WA September Scanner Parameter Estimation Using Bilevel Scans of Star Charts Elisa H. Barney Smith Electrical and Computer Engineering Department Boise State University, Boise, Idaho 8375
More informationCEE598 - Visual Sensing for Civil Infrastructure Eng. & Mgmt.
CEE598 - Visual Sensing for Civil Infrastructure Eng. & Mgmt. Section 10 - Detectors part II Descriptors Mani Golparvar-Fard Department of Civil and Environmental Engineering 3129D, Newmark Civil Engineering
More informationA Keypoint Descriptor Inspired by Retinal Computation
A Keypoint Descriptor Inspired by Retinal Computation Bongsoo Suh, Sungjoon Choi, Han Lee Stanford University {bssuh,sungjoonchoi,hanlee}@stanford.edu Abstract. The main goal of our project is to implement
More informationDigital Image Processing. Image Enhancement - Filtering
Digital Image Processing Image Enhancement - Filtering Derivative Derivative is defined as a rate of change. Discrete Derivative Finite Distance Example Derivatives in 2-dimension Derivatives of Images
More informationLogical Templates for Feature Extraction in Fingerprint Images
Logical Templates for Feature Extraction in Fingerprint Images Bir Bhanu, Michael Boshra and Xuejun Tan Center for Research in Intelligent Systems University of Califomia, Riverside, CA 9252 1, USA Email:
More informationRepresenting Moving Images with Layers. J. Y. Wang and E. H. Adelson MIT Media Lab
Representing Moving Images with Layers J. Y. Wang and E. H. Adelson MIT Media Lab Goal Represent moving images with sets of overlapping layers Layers are ordered in depth and occlude each other Velocity
More informationArtifacts and Textured Region Detection
Artifacts and Textured Region Detection 1 Vishal Bangard ECE 738 - Spring 2003 I. INTRODUCTION A lot of transformations, when applied to images, lead to the development of various artifacts in them. In
More informationImage pyramids and their applications Bill Freeman and Fredo Durand Feb. 28, 2006
Image pyramids and their applications 6.882 Bill Freeman and Fredo Durand Feb. 28, 2006 Image pyramids Gaussian Laplacian Wavelet/QMF Steerable pyramid http://www-bcs.mit.edu/people/adelson/pub_pdfs/pyramid83.pdf
More informationWorking with the BCC Bump Map Generator
Working with the BCC Bump Map Generator Bump mapping is used to create three dimensional detail on an image based on the luminance information in the image. The luminance value of each pixel of the image
More informationImage Fusion Using Double Density Discrete Wavelet Transform
6 Image Fusion Using Double Density Discrete Wavelet Transform 1 Jyoti Pujar 2 R R Itkarkar 1,2 Dept. of Electronics& Telecommunication Rajarshi Shahu College of Engineeing, Pune-33 Abstract - Image fusion
More informationChapter 3: Intensity Transformations and Spatial Filtering
Chapter 3: Intensity Transformations and Spatial Filtering 3.1 Background 3.2 Some basic intensity transformation functions 3.3 Histogram processing 3.4 Fundamentals of spatial filtering 3.5 Smoothing
More informationDistortion-invariant Kernel Correlation Filters for General Object Recognition
Distortion-invariant Kernel Correlation Filters for General Object Recognition Dissertation by Rohit Patnaik Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy
More informationBiomedical Image Analysis. Point, Edge and Line Detection
Biomedical Image Analysis Point, Edge and Line Detection Contents: Point and line detection Advanced edge detection: Canny Local/regional edge processing Global processing: Hough transform BMIA 15 V. Roth
More informationComputer Vision I. Announcements. Fourier Tansform. Efficient Implementation. Edge and Corner Detection. CSE252A Lecture 13.
Announcements Edge and Corner Detection HW3 assigned CSE252A Lecture 13 Efficient Implementation Both, the Box filter and the Gaussian filter are separable: First convolve each row of input image I with
More informationBME I5000: Biomedical Imaging
1 Lucas Parra, CCNY BME I5000: Biomedical Imaging Lecture 11 Point Spread Function, Inverse Filtering, Wiener Filtering, Sharpening,... Lucas C. Parra, parra@ccny.cuny.edu Blackboard: http://cityonline.ccny.cuny.edu/
More informationRange Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation
Obviously, this is a very slow process and not suitable for dynamic scenes. To speed things up, we can use a laser that projects a vertical line of light onto the scene. This laser rotates around its vertical
More informationDetection of deformable objects in a nonstationary
Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 5 Detection of deformable objects in a nonstationary scene Sherif Azary Follow this and additional works at: http://scholarworks.rit.edu/theses
More informationEECS150 - Digital Design Lecture 14 FIFO 2 and SIFT. Recap and Outline
EECS150 - Digital Design Lecture 14 FIFO 2 and SIFT Oct. 15, 2013 Prof. Ronald Fearing Electrical Engineering and Computer Sciences University of California, Berkeley (slides courtesy of Prof. John Wawrzynek)
More informationReduced Image Noise on Shape Recognition Using Singular Value Decomposition for Pick and Place Robotic Systems
Reduced Image Noise on Shape Recognition Using Singular Value Decomposition for Pick and Place Robotic Systems Angelo A. Beltran Jr. 1, Christian Deus T. Cayao 2, Jay-K V. Delicana 3, Benjamin B. Agraan
More informationImage processing and features
Image processing and features Gabriele Bleser gabriele.bleser@dfki.de Thanks to Harald Wuest, Folker Wientapper and Marc Pollefeys Introduction Previous lectures: geometry Pose estimation Epipolar geometry
More informationChapter 4 Face Recognition Using Orthogonal Transforms
Chapter 4 Face Recognition Using Orthogonal Transforms Face recognition as a means of identification and authentication is becoming more reasonable with frequent research contributions in the area. In
More information