Exercise questions for Machine vision
|
|
- Isabel Hoover
- 5 years ago
- Views:
Transcription
1 Exercise questions for Machine vision This is a collection of exercise questions. These questions are all examination alike which means that similar questions may appear at the written exam. I ve divided the questions and related them to the topics covered in Lecture 1 to 6. Lecture 1: 1. Consider a machine vision system for Original Character Recognition (OCR). List and also explain shortly the fundamental steps of the image processing for this system. 2. Motivate shortly why a machine vision system should be used for optical quality inspection of items produced at a factory. Also give drawbacks, pros and cons.
2 Lecture 2 and 3: 3. Compare the following three different architectures for CCD sensors, Full frame CCD, Frame transfer CCD, Interline transfer CCD. Draw simple architectural layouts for each sensor, explain differences, advantages and drawbacks with the different architectures. 4. How is the Signal to Noise Ratio (SNR) related to the architectural design of an area scan image sensor? What can be done to improve SNR for a given architecture? 5. Explain the two most common modes for readout of area scan sensors, progressive- and interlaced scan. Explain advantages and disadvantages with the two different modes. 6. What is a telecentric lens, what is its most important feature and what purpose can it be used for? Draw a sketch of the basic light rays for a telecentric lens and explain its function in comparison with a standard Gaussian optics. 7. Explain shortly the following characteristics of illumination. Diffuse light Directed light Telecentric light Front light Back light Bright field Dark field
3 A) B) C) 8. Combine pictures A to B above with the following choices, F=2 F=16 and Telecentric lens. Each picture matches only one choice. Motivate shortly why you think a picture matches your choice. 9. I will first list a number of properties related to the illumination for a machine vision system: Diffuse light Directed light Telecentric light Front light Back light Bright field Dark field The following four pictures show different types of illumination systems with cameras. Assign the right properties to each of the illumination system. There can be more than one relevant property for an illumination. A) B) C) D) 10. A Gaussian lens is used to project an image of a car at far distance. It must be possible to resolve the car s two headlights with spacing of 1.5 meters and at a distance of 600 meters from the camera. What will be the absolute maximum pixel size of an area scan sensor if the focal length of the lens is 8 mm? 1 1 = s' s 1 f ' β = h ' s' = h s 11. Explain shortly the effects of reducing the aperture of a lens system in front of a pixel area sensor in terms of depth of field, signal to noise ratio and image resolution.
4 12. a) I will now list five different sources of noise that can appear in images made with for example CMOS or CCD sensors. Noise sources: - Shot noise (In amplifiers) - Sensitivity variations between pixels - Photon noise - Dark signal variations - Thermal noise Assign the above five sources of noise to the two following classes of noise. Each class will have more than one noise source and both classes will have total of five noise sources. Classes of noise: - Temporal noise - Spatial noise b) Suggest a method how to suppress the temporal noise.
5 Lecture 4: 13. Figure 1 depicts a diagram for the amplitude characteristic of a 2D linear filter. What kind of filter is this, High Pass, Low Pass or Band Pass? What do you expect to be the visual effect on an image if this filter where applied on it? Figure 1. Amplitude characteristic for a 2D filter. r r 14. The three pictures above show the amplitude transfer function for a 2D Butterworth filter. The amplitude characteristics is illustrated as a mesh plot, an intensity image and as a radial plot for different orders n of the filter. a) What class of filter is this, Low Pass, High Pass, Band Pass or Band Stop filter? b) Which one of the pictures labeled A to E is filtered using the smallest value for r as defined in the amplitude characteristics above.
6 Original image A B C D E 15. Explain shortly what kind of image processing operations is necessary for high quality downscaling of an image? 16. Figure 2 shows a picture of the silhouette of a screw taken at back lightening. The silhouette is highlighted at subpixel precision by image processing. Suggest a method for how this image processing can be done. Figure 2. Screw thread. 17. Assume a gray-level image f(r,c) and its smoothened correspondence g(r,c). The region of interest is R. Then the dynamic thresholding of brighter objects on a dark background can S = ( r, c) R f ( r, c) g( r, c) be defined as, { } Where gdiff is a fixed constant. Pictures A and B both have bright spots on a darker background. If compared with using simple global thresholding, which one of the pictures A or B will require the use of dynamic thresholding in order to successfully segment the bright spots from the background? Motivate your answer shortly. If you answer with a long and not precise story, your credits will be reduced. g diff Pic. A Pic. B
7 18. Image (2) was processed by Histogram equalisation to create image (1). a) Which one of the histograms A and B correspond to image (1) and (2)? Explain and motivate b) How is the graylevel transformation function computed for Histogram equalisation? Image (1) Image (2) Histogram A Histogram B
8 Lecture 5: 19. The following equation defines a morphological operation, OP = { x ( B) A } x. a) What is the name of this operation? b) Which one of the following pictures below is the correct graphical illustration of the effect of that operation for a binary image A and a structural element B? ) Illustration A) Illustration B) A Illustration C) Illustration D) 20. Two image points (x1,y1), (x2,y2), lying on a single line are shown. The corresponding lines in a parameter space are also shown. This transformation can be utilized in the Hough transform to find lines in an image. Explain shortly how this detection of lines works for the Hough transform and how the line parameters for that line can be measured.
9 21. Figure 3 depicts an image object A and a structural element B used for the morphological ) A B = x ( B) A. M ) is the reflection of operation Dilate. Dilate is defined as, { } a region M and M x is the translation of region M by a vector x. Draw a nice picture and show how A B will look like. x Figure 3. Image object A and the structural element B used for morphological operations. 22. Explain how Template matching is working and suggest also a method how to cope with the increasing execution times for Template matching as the resolution of the image is increased. 23. The following drawing shows a square shaped region A of pixels belonging to one single image component in a binary image. Region B is a circular shaped structural element having the diameter 2 and with its origin at the center, indicated with a dark spot. A 3 B 2 A morphological operation OP is defined as, OP = { x ( B) A }. a) What is the name of this operation as known in all reference litterature? b) Make a simple sketch having the right proportions showing the visual effect on region A after applying this morphological operation OP using structural element B. Also make an indication in your drawing on what the size of the processed region will be. I want just the sketch as an answer, nothing else. x
10 24. The following binary picture to the left shows vertical and horizontal lines having a width of 5 pixels. Distances between lines are at least 30 pixels. Consider lines as belonging to region A. The drawing to the right shows a structuring element B. B 10 pixels A morphological operation OP1 is defined as, OP = { x ( B) A } 1 x. Another morphological operation OP2 is defined as, OP = { x ( B) A } a) What are the names for operations OP1 and OP2? 1 pixel b) Apply firstly OP1 on region A and then apply OP2 such that C = OP2(OP1(A)). Make a drawing and show how region C will look like. 25. An Edge Histogram Descriptor EHD is computed on the two pictures shown below. 2 ) x Picture A Picture B Estimate and illustrate EHD for the two pictures A and B. Explain what the diagrams show and why they look like they do.
11 Lecture 6: 26. Explain shortly how a minimum distance classifier works. What kind of priori statistics is computed for the trainings sets? 27. Assume that you are going to apply a segmentation algorithm in a machine vision system that is built to inspect colours, sizes and shapes of cookies on a conveyer belt before packaging. Damaged cookies, burnt cookies, cookies with non circular shapes or cookies out of size range must be disposed by the system. The intensity of the illumination for this machine vision system is inhomogeneous distributed (not constant intensity) over the observation area. What kind of segmentation algorithm would you select for this task, discuss and motivate your selection of algorithm and also discuss properties of other system components that might be important to consider for this selection?
12 Lecture 7: 28. Draw a picture and explain how a sheet of light laser can be used together with an area scan sensor for acquisition of a 3D-surface and based on triangulation techniques. Just explain the measurement principle how it works. 29. Figure 4 depicts a schematic setup for stereo imaging based on two image sensors and an λb object W at distance Z given by Z = λ. The object W is projected onto the image x 2 x 1 sensors 1 and 2 at position (x1,y1) and (x2,y2) respectively. Explain what kind of image processing is necessary in order to measure the distance Z from the two sensors to the object W. Relate your explanation to the given expression for Z. Figure 4. Stereo imaging. 30. The position of a laser line projected onto an image detector versus height of object is shown in Figure 5. One curve is representing measured values used for calibration and second curve shows a computed transfer function. These curves comes from a setup for laser scanning used to capture a 3D surface. It shows almost a perfect linear relation between pixels and height. From measurements and it was shown that the standard deviation of computed position of laser line was 0.2 pixels. a) Explain shortly what property of captured images is limiting precision of laser line position to 0.2 pixels? b) What is the precision of height measurement that this scanner can achieve? height [mm] Comparison of computed and measured levels Measured slope = Computed slope = Calibration reference level = mm Deviation in slopes = mm Measured Heights Computed Heights pixels Figure 5. Height versus position on image detector.
13 31. The intensity profile of an imaged laser line is shown in Figure 6. When Center Of Gravity (COG) is computed to find position of laser line in one of the spatial dimensions, a threshold can be used. a) Explain and motivate why this threshold is used for a laser scanner. 250 Gray level Threshold [Pixels] Figure 6. Gray level versus pixels for an imaged laser line. 32. A laser scanner is using a step size of 0.5 mm. What is the highest frequency along the scanning dimension that can be resolved? 33. A laser scanner is using a telecentric lens having an optical amplification of 0,25. Pixel size of image detector is 10 µm. What is the highest frequency along the laser line that can be resolved?
ME 6406 MACHINE VISION. Georgia Institute of Technology
ME 6406 MACHINE VISION Georgia Institute of Technology Class Information Instructor Professor Kok-Meng Lee MARC 474 Office hours: Tues/Thurs 1:00-2:00 pm kokmeng.lee@me.gatech.edu (404)-894-7402 Class
More informationBASLER A601f / A602f
Camera Specification BASLER A61f / A6f Measurement protocol using the EMVA Standard 188 3rd November 6 All values are typical and are subject to change without prior notice. CONTENTS Contents 1 Overview
More information8.2 IMAGE PROCESSING VERSUS IMAGE ANALYSIS Image processing: The collection of routines and
8.1 INTRODUCTION In this chapter, we will study and discuss some fundamental techniques for image processing and image analysis, with a few examples of routines developed for certain purposes. 8.2 IMAGE
More informationBasler aca640-90gm. Camera Specification. Measurement protocol using the EMVA Standard 1288 Document Number: BD Version: 02
Basler aca64-9gm Camera Specification Measurement protocol using the EMVA Standard 1288 Document Number: BD584 Version: 2 For customers in the U.S.A. This equipment has been tested and found to comply
More informationApplying Automated Optical Inspection Ben Dawson, DALSA Coreco Inc., ipd Group (987)
Applying Automated Optical Inspection Ben Dawson, DALSA Coreco Inc., ipd Group bdawson@goipd.com (987) 670-2050 Introduction Automated Optical Inspection (AOI) uses lighting, cameras, and vision computers
More informationSolution Set #2
05-78-0 Solution Set #. For the sampling function shown, analyze to determine its characteristics, e.g., the associated Nyquist sampling frequency (if any), whether a function sampled with s [x; x] may
More informationBasler aca gm. Camera Specification. Measurement protocol using the EMVA Standard 1288 Document Number: BD Version: 01
Basler aca5-14gm Camera Specification Measurement protocol using the EMVA Standard 188 Document Number: BD563 Version: 1 For customers in the U.S.A. This equipment has been tested and found to comply with
More informationOptical design of a high resolution vision lens
Optical design of a high resolution vision lens Paul Claassen, optical designer, paul.claassen@sioux.eu Marnix Tas, optical specialist, marnix.tas@sioux.eu Prof L.Beckmann, l.beckmann@hccnet.nl Summary:
More informationComputer Vision. Howie Choset Introduction to Robotics
Computer Vision Howie Choset http://www.cs.cmu.edu.edu/~choset Introduction to Robotics http://generalrobotics.org What is vision? What is computer vision? Edge Detection Edge Detection Interest points
More informationCamera Test Protocol. Introduction TABLE OF CONTENTS. Camera Test Protocol Technical Note Technical Note
Technical Note CMOS, EMCCD AND CCD CAMERAS FOR LIFE SCIENCES Camera Test Protocol Introduction The detector is one of the most important components of any microscope system. Accurate detector readings
More informationThe Noise about Noise
The Noise about Noise I have found that few topics in astrophotography cause as much confusion as noise and proper exposure. In this column I will attempt to present some of the theory that goes into determining
More informationBasler aca km. Camera Specification. Measurement protocol using the EMVA Standard 1288 Document Number: BD Version: 03
Basler aca-18km Camera Specification Measurement protocol using the EMVA Standard 188 Document Number: BD59 Version: 3 For customers in the U.S.A. This equipment has been tested and found to comply with
More informationAn Efficient Color Image Segmentation using Edge Detection and Thresholding Methods
19 An Efficient Color Image Segmentation using Edge Detection and Thresholding Methods T.Arunachalam* Post Graduate Student, P.G. Dept. of Computer Science, Govt Arts College, Melur - 625 106 Email-Arunac682@gmail.com
More informationCHAPTER 9 POSITION SENSITIVE PHOTOMULTIPLIER TUBES
CHAPTER 9 POSITION SENSITIVE PHOTOMULTIPLIER TUBES The current multiplication mechanism offered by dynodes makes photomultiplier tubes ideal for low-light-level measurement. As explained earlier, there
More informationOptical basics for machine vision systems. Lars Fermum Chief instructor STEMMER IMAGING GmbH
Optical basics for machine vision systems Lars Fermum Chief instructor STEMMER IMAGING GmbH www.stemmer-imaging.de AN INTERNATIONAL CONCEPT STEMMER IMAGING customers in UK Germany France Switzerland Sweden
More informationCS534 Introduction to Computer Vision. Linear Filters. Ahmed Elgammal Dept. of Computer Science Rutgers University
CS534 Introduction to Computer Vision Linear Filters Ahmed Elgammal Dept. of Computer Science Rutgers University Outlines What are Filters Linear Filters Convolution operation Properties of Linear Filters
More information2013 LMIC Imaging Workshop. Sidney L. Shaw Technical Director. - Light and the Image - Detectors - Signal and Noise
2013 LMIC Imaging Workshop Sidney L. Shaw Technical Director - Light and the Image - Detectors - Signal and Noise The Anatomy of a Digital Image Representative Intensities Specimen: (molecular distribution)
More informationNON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT:
IJCE January-June 2012, Volume 4, Number 1 pp. 59 67 NON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT: A COMPARATIVE STUDY Prabhdeep Singh1 & A. K. Garg2
More informationIMAGE SENSOR SOLUTIONS. KAC-96-1/5" Lens Kit. KODAK KAC-96-1/5" Lens Kit. for use with the KODAK CMOS Image Sensors. November 2004 Revision 2
KODAK for use with the KODAK CMOS Image Sensors November 2004 Revision 2 1.1 Introduction Choosing the right lens is a critical aspect of designing an imaging system. Typically the trade off between image
More informationAdvanced Camera and Image Sensor Technology. Steve Kinney Imaging Professional Camera Link Chairman
Advanced Camera and Image Sensor Technology Steve Kinney Imaging Professional Camera Link Chairman Content Physical model of a camera Definition of various parameters for EMVA1288 EMVA1288 and image quality
More informationImage Processing Lecture 4
Image Enhancement Image enhancement aims to process an image so that the output image is more suitable than the original. It is used to solve some computer imaging problems, or to improve image quality.
More informationPhysics 2310 Lab #5: Thin Lenses and Concave Mirrors Dr. Michael Pierce (Univ. of Wyoming)
Physics 2310 Lab #5: Thin Lenses and Concave Mirrors Dr. Michael Pierce (Univ. of Wyoming) Purpose: The purpose of this lab is to introduce students to some of the properties of thin lenses and mirrors.
More informationA 3MPixel Multi-Aperture Image Sensor with 0.7µm Pixels in 0.11µm CMOS
A 3MPixel Multi-Aperture Image Sensor with 0.7µm Pixels in 0.11µm CMOS Keith Fife, Abbas El Gamal, H.-S. Philip Wong Stanford University, Stanford, CA Outline Introduction Chip Architecture Detailed Operation
More informationOptical Components for Laser Applications. Günter Toesko - Laserseminar BLZ im Dezember
Günter Toesko - Laserseminar BLZ im Dezember 2009 1 Aberrations An optical aberration is a distortion in the image formed by an optical system compared to the original. It can arise for a number of reasons
More informationExamination, TEN1, in courses SK2500/SK2501, Physics of Biomedical Microscopy,
KTH Applied Physics Examination, TEN1, in courses SK2500/SK2501, Physics of Biomedical Microscopy, 2009-06-05, 8-13, FB51 Allowed aids: Compendium Imaging Physics (handed out) Compendium Light Microscopy
More informationA Novel Morphological Method for Detection and Recognition of Vehicle License Plates
American Journal of Applied Sciences 6 (12): 2066-2070, 2009 ISSN 1546-9239 2009 Science Publications A Novel Morphological Method for Detection and Recognition of Vehicle License Plates 1 S.H. Mohades
More informationEC-433 Digital Image Processing
EC-433 Digital Image Processing Lecture 2 Digital Image Fundamentals Dr. Arslan Shaukat 1 Fundamental Steps in DIP Image Acquisition An image is captured by a sensor (such as a monochrome or color TV camera)
More informationFRAUNHOFER AND FRESNEL DIFFRACTION IN ONE DIMENSION
FRAUNHOFER AND FRESNEL DIFFRACTION IN ONE DIMENSION Revised November 15, 2017 INTRODUCTION The simplest and most commonly described examples of diffraction and interference from two-dimensional apertures
More informationHello, welcome to the video lecture series on Digital Image Processing.
Digital Image Processing. Professor P. K. Biswas. Department of Electronics and Electrical Communication Engineering. Indian Institute of Technology, Kharagpur. Lecture-33. Contrast Stretching Operation.
More informationMech 296: Vision for Robotic Applications. Vision for Robotic Applications
Mech 296: Vision for Robotic Applications Lecture 1: Monochrome Images 1.1 Vision for Robotic Applications Instructors, jrife@engr.scu.edu Jeff Ota, jota@scu.edu Class Goal Design and implement a vision-based,
More informationRadial Polarization Converter With LC Driver USER MANUAL
ARCoptix Radial Polarization Converter With LC Driver USER MANUAL Arcoptix S.A Ch. Trois-portes 18 2000 Neuchâtel Switzerland Mail: info@arcoptix.com Tel: ++41 32 731 04 66 Principle of the radial polarization
More informationBasler ral km. Camera Specification. Measurement protocol using the EMVA Standard 1288 Document Number: BD Version: 01
Basler ral8-8km Camera Specification Measurement protocol using the EMVA Standard 188 Document Number: BD79 Version: 1 For customers in the U.S.A. This equipment has been tested and found to comply with
More informationImage Capture and Problems
Image Capture and Problems A reasonable capture IVR Vision: Flat Part Recognition Fisher lecture 4 slide 1 Image Capture: Focus problems Focus set to one distance. Nearby distances in focus (depth of focus).
More informationQUALITY CHECKING AND INSPECTION BASED ON MACHINE VISION TECHNIQUE TO DETERMINE TOLERANCEVALUE USING SINGLE CERAMIC CUP
QUALITY CHECKING AND INSPECTION BASED ON MACHINE VISION TECHNIQUE TO DETERMINE TOLERANCEVALUE USING SINGLE CERAMIC CUP Nursabillilah Mohd Alie 1, Mohd Safirin Karis 1, Gao-Jie Wong 1, Mohd Bazli Bahar
More informationOn spatial resolution
On spatial resolution Introduction How is spatial resolution defined? There are two main approaches in defining local spatial resolution. One method follows distinction criteria of pointlike objects (i.e.
More informationROBOT VISION. Dr.M.Madhavi, MED, MVSREC
ROBOT VISION Dr.M.Madhavi, MED, MVSREC Robotic vision may be defined as the process of acquiring and extracting information from images of 3-D world. Robotic vision is primarily targeted at manipulation
More informationIntroduction. Prof. Lina Karam School of Electrical, Computer, & Energy Engineering Arizona State University
EEE 508 - Digital Image & Video Processing and Compression http://lina.faculty.asu.edu/eee508/ Introduction Prof. Lina Karam School of Electrical, Computer, & Energy Engineering Arizona State University
More informationNumber Plate Recognition System using OCR for Automatic Toll Collection
IJSTE - International Journal of Science Technology & Engineering Volume 2 Issue 10 April 2016 ISSN (online): 2349-784X Number Plate Recognition System using OCR for Automatic Toll Collection Mohini S.Karande
More informationHomework Set 3.5 Sensitive optoelectronic detectors: seeing single photons
Homework Set 3.5 Sensitive optoelectronic detectors: seeing single photons Due by 12:00 noon (in class) on Tuesday, Nov. 7, 2006. This is another hybrid lab/homework; please see Section 3.4 for what you
More informationDECISION NUMBER FOURTEEN TO THE TREATY ON OPEN SKIES
DECISION NUMBER FOURTEEN TO THE TREATY ON OPEN SKIES OSCC.DEC 14 12 October 1994 METHODOLOGY FOR CALCULATING THE MINIMUM HEIGHT ABOVE GROUND LEVEL AT WHICH EACH VIDEO CAMERA WITH REAL TIME DISPLAY INSTALLED
More informationScrappiX. Visual inspection equipment for dimensional. and surface defects control
ScrappiX Visual inspection equipment for dimensional and surface defects control ScrappiX Visual inspection equipment for dimensional and surface defects control Scrappix is an innovative visual inspection
More informationECEN 4606, UNDERGRADUATE OPTICS LAB
ECEN 4606, UNDERGRADUATE OPTICS LAB Lab 2: Imaging 1 the Telescope Original Version: Prof. McLeod SUMMARY: In this lab you will become familiar with the use of one or more lenses to create images of distant
More informationUniversity Of Lübeck ISNM Presented by: Omar A. Hanoun
University Of Lübeck ISNM 12.11.2003 Presented by: Omar A. Hanoun What Is CCD? Image Sensor: solid-state device used in digital cameras to capture and store an image. Photosites: photosensitive diodes
More informationDevelopment of a new multi-wavelength confocal surface profilometer for in-situ automatic optical inspection (AOI)
Development of a new multi-wavelength confocal surface profilometer for in-situ automatic optical inspection (AOI) Liang-Chia Chen 1#, Chao-Nan Chen 1 and Yi-Wei Chang 1 1. Institute of Automation Technology,
More informationImage Acquisition. Jos J.M. Groote Schaarsberg Center for Image Processing
Image Acquisition Jos J.M. Groote Schaarsberg schaarsberg@tpd.tno.nl Specification and system definition Acquisition systems (camera s) Illumination Theoretical case : noise Additional discussion and questions
More informationEnhanced Shape Recovery with Shuttered Pulses of Light
Enhanced Shape Recovery with Shuttered Pulses of Light James Davis Hector Gonzalez-Banos Honda Research Institute Mountain View, CA 944 USA Abstract Computer vision researchers have long sought video rate
More informationEE 392B: Course Introduction
EE 392B Course Introduction About EE392B Goals Topics Schedule Prerequisites Course Overview Digital Imaging System Image Sensor Architectures Nonidealities and Performance Measures Color Imaging Recent
More informationCamera Calibration Certificate No: DMC III 27542
Calibration DMC III Camera Calibration Certificate No: DMC III 27542 For Peregrine Aerial Surveys, Inc. #201 1255 Townline Road Abbotsford, B.C. V2T 6E1 Canada Calib_DMCIII_27542.docx Document Version
More informationImage Processing. COMP 3072 / GV12 Gabriel Brostow. TA: Josias P. Elisee (with help from Dr Wole Oyekoya) Image Processing.
Image Processing COMP 3072 / GV12 Gabriel Brostow TA: Josias P. Elisee (with help from Dr Wole Oyekoya) 1 2 Motivation and Goals Grounding in image processing techniques Concentrate on algorithms used
More informationPhotons and solid state detection
Photons and solid state detection Photons represent discrete packets ( quanta ) of optical energy Energy is hc/! (h: Planck s constant, c: speed of light,! : wavelength) For solid state detection, photons
More informationGeneral Imaging System
General Imaging System Lecture Slides ME 4060 Machine Vision and Vision-based Control Chapter 5 Image Sensing and Acquisition By Dr. Debao Zhou 1 2 Light, Color, and Electromagnetic Spectrum Penetrate
More informationIMAGE ACQUISITION, CAMERAS
IMAGE ACQUISITION, CAMERAS 1/39 Václav Hlaváč hlavac@cmp.felk.cvut.cz http://cmp.felk.cvut.cz/ hlavac IMAGING SYSTEMS 2/39 Pohled na celek: from the observed property of interest through radiance L and
More informationImage and Multidimensional Signal Processing
Image and Multidimensional Signal Processing Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ Digital Image Fundamentals 2 Digital Image Fundamentals
More informationFully depleted, thick, monolithic CMOS pixels with high quantum efficiency
Fully depleted, thick, monolithic CMOS pixels with high quantum efficiency Andrew Clarke a*, Konstantin Stefanov a, Nicholas Johnston a and Andrew Holland a a Centre for Electronic Imaging, The Open University,
More informationBe aware that there is no universal notation for the various quantities.
Fourier Optics v2.4 Ray tracing is limited in its ability to describe optics because it ignores the wave properties of light. Diffraction is needed to explain image spatial resolution and contrast and
More informationINDIAN VEHICLE LICENSE PLATE EXTRACTION AND SEGMENTATION
International Journal of Computer Science and Communication Vol. 2, No. 2, July-December 2011, pp. 593-599 INDIAN VEHICLE LICENSE PLATE EXTRACTION AND SEGMENTATION Chetan Sharma 1 and Amandeep Kaur 2 1
More informationMML-High Resolution 5M Series
Fixed Magnification Series -High Resolution 5M Series High-resolution models that possess the best contrast and NA of all Series. Image acquisition with even higher image quality is realized by combining
More informationIntroduction. Lighting
&855(17 )8785(75(1'6,10$&+,1(9,6,21 5HVHDUFK6FLHQWLVW0DWV&DUOLQ 2SWLFDO0HDVXUHPHQW6\VWHPVDQG'DWD$QDO\VLV 6,17()(OHFWURQLFV &\EHUQHWLFV %R[%OLQGHUQ2VOR125:$< (PDLO0DWV&DUOLQ#HF\VLQWHIQR http://www.sintef.no/ecy/7210/
More informationCSE 473/573 Computer Vision and Image Processing (CVIP)
CSE 473/573 Computer Vision and Image Processing (CVIP) Ifeoma Nwogu inwogu@buffalo.edu Lecture 4 Image formation(part I) Schedule Last class linear algebra overview Today Image formation and camera properties
More informationMAV-ID card processing using camera images
EE 5359 MULTIMEDIA PROCESSING SPRING 2013 PROJECT PROPOSAL MAV-ID card processing using camera images Under guidance of DR K R RAO DEPARTMENT OF ELECTRICAL ENGINEERING UNIVERSITY OF TEXAS AT ARLINGTON
More informationDigital Image Processing
Digital Image Processing Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, 2008. Digital Image Processing
More informationHistogram and Its Processing
Histogram and Its Processing 3rd Lecture on Image Processing Martina Mudrová 24 Definition What a histogram is? = vector of absolute numbers occurrence of every colour in the picture [H(1),H(2), H(c)]
More informationVEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL
VEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL Instructor : Dr. K. R. Rao Presented by: Prasanna Venkatesh Palani (1000660520) prasannaven.palani@mavs.uta.edu
More informationSupplementary Figure 1
Supplementary Figure 1 Technical overview drawing of the Roadrunner goniometer. The goniometer consists of three main components: an inline sample-viewing microscope, a high-precision scanning unit for
More informationThe Henryk Niewodniczański INSTITUTE OF NUCLEAR PHYSICS Polish Academy of Sciences ul. Radzikowskiego 152, Kraków, Poland.
The Henryk Niewodniczański INSTITUTE OF NUCLEAR PHYSICS Polish Academy of Sciences ul. Radzikowskiego 152, 31-342 Kraków, Poland. www.ifj.edu.pl/reports/2003.html Kraków, grudzień 2003 Report No 1931/PH
More informationGE 113 REMOTE SENSING. Topic 7. Image Enhancement
GE 113 REMOTE SENSING Topic 7. Image Enhancement Lecturer: Engr. Jojene R. Santillan jrsantillan@carsu.edu.ph Division of Geodetic Engineering College of Engineering and Information Technology Caraga State
More informationDigital Image Fundamentals. Digital Image Processing. Human Visual System. Contents. Structure Of The Human Eye (cont.) Structure Of The Human Eye
Digital Image Processing 2 Digital Image Fundamentals Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Those who wish to succeed must ask the right preliminary questions Aristotle Images
More informationDigital Image Fundamentals. Digital Image Processing. Human Visual System. Contents. Structure Of The Human Eye (cont.) Structure Of The Human Eye
Digital Image Processing 2 Digital Image Fundamentals Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall,
More informationHistogram and Its Processing
... 3.. 5.. 7.. 9 and Its Processing 3rd Lecture on Image Processing Martina Mudrová Definition What a histogram is? = vector of absolute numbers occurrence of every colour in the picture [H(),H(), H(c)]
More informationLecture 20: Optical Tools for MEMS Imaging
MECH 466 Microelectromechanical Systems University of Victoria Dept. of Mechanical Engineering Lecture 20: Optical Tools for MEMS Imaging 1 Overview Optical Microscopes Video Microscopes Scanning Electron
More informationTHE CCD RIDDLE REVISTED: SIGNAL VERSUS TIME LINEAR SIGNAL VERSUS VARIANCE NON-LINEAR
THE CCD RIDDLE REVISTED: SIGNAL VERSUS TIME LINEAR SIGNAL VERSUS VARIANCE NON-LINEAR Mark Downing 1, Peter Sinclaire 1. 1 ESO, Karl Schwartzschild Strasse-2, 85748 Munich, Germany. ABSTRACT The photon
More informationDigital Image Processing
Digital Image Processing Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, 2008. Digital Image Processing
More informationEBU - Tech 3335 : Methods of measuring the imaging performance of television cameras for the purposes of characterisation and setting
EBU - Tech 3335 : Methods of measuring the imaging performance of television cameras for the purposes of characterisation and setting Alan Roberts, March 2016 SUPPLEMENT 19: Assessment of a Sony a6300
More informationDigital Photogrammetry. Presented by: Dr. Hamid Ebadi
Digital Photogrammetry Presented by: Dr. Hamid Ebadi Background First Generation Analog Photogrammetry Analytical Photogrammetry Digital Photogrammetry Photogrammetric Generations 2000 digital photogrammetry
More informationFSI Machine Vision Training Programs
FSI Machine Vision Training Programs Table of Contents Introduction to Machine Vision (Course # MVC-101) Machine Vision and NeuroCheck overview (Seminar # MVC-102) Machine Vision, EyeVision and EyeSpector
More informationLecture 19: Depth Cameras. Kayvon Fatahalian CMU : Graphics and Imaging Architectures (Fall 2011)
Lecture 19: Depth Cameras Kayvon Fatahalian CMU 15-869: Graphics and Imaging Architectures (Fall 2011) Continuing theme: computational photography Cheap cameras capture light, extensive processing produces
More informationCCD Requirements for Digital Photography
IS&T's 2 PICS Conference IS&T's 2 PICS Conference Copyright 2, IS&T CCD Requirements for Digital Photography Richard L. Baer Hewlett-Packard Laboratories Palo Alto, California Abstract The performance
More informationExperiment 1: Fraunhofer Diffraction of Light by a Single Slit
Experiment 1: Fraunhofer Diffraction of Light by a Single Slit Purpose 1. To understand the theory of Fraunhofer diffraction of light at a single slit and at a circular aperture; 2. To learn how to measure
More informationSpeckle disturbance limit in laserbased cinema projection systems
Speckle disturbance limit in laserbased cinema projection systems Guy Verschaffelt 1,*, Stijn Roelandt 2, Youri Meuret 2,3, Wendy Van den Broeck 4, Katriina Kilpi 4, Bram Lievens 4, An Jacobs 4, Peter
More informationORIFICE MEASUREMENT VERISENS APPLICATION DESCRIPTION: REQUIREMENTS APPLICATION CONSIDERATIONS RESOLUTION/ MEASUREMENT ACCURACY. Vision Technologies
VERISENS APPLICATION DESCRIPTION: ORIFICE MEASUREMENT REQUIREMENTS A major manufacturer of plastic orifices needs to verify that the orifice is within the correct measurement band. Parts are presented
More informationAn Improved Bernsen Algorithm Approaches For License Plate Recognition
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) ISSN: 78-834, ISBN: 78-8735. Volume 3, Issue 4 (Sep-Oct. 01), PP 01-05 An Improved Bernsen Algorithm Approaches For License Plate Recognition
More informationOpto Engineering S.r.l.
TUTORIAL #1 Telecentric Lenses: basic information and working principles On line dimensional control is one of the most challenging and difficult applications of vision systems. On the other hand, besides
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY Mechanical Engineering Department. 2.71/2.710 Final Exam. May 21, Duration: 3 hours (9 am-12 noon)
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Mechanical Engineering Department 2.71/2.710 Final Exam May 21, 2013 Duration: 3 hours (9 am-12 noon) CLOSED BOOK Total pages: 5 Name: PLEASE RETURN THIS BOOKLET WITH
More informationHigh-speed Micro-crack Detection of Solar Wafers with Variable Thickness
High-speed Micro-crack Detection of Solar Wafers with Variable Thickness T. W. Teo, Z. Mahdavipour, M. Z. Abdullah School of Electrical and Electronic Engineering Engineering Campus Universiti Sains Malaysia
More informationDetermining MTF with a Slant Edge Target ABSTRACT AND INTRODUCTION
Determining MTF with a Slant Edge Target Douglas A. Kerr Issue 2 October 13, 2010 ABSTRACT AND INTRODUCTION The modulation transfer function (MTF) of a photographic lens tells us how effectively the lens
More informationScrabble Board Automatic Detector for Third Party Applications
Scrabble Board Automatic Detector for Third Party Applications David Hirschberg Computer Science Department University of California, Irvine hirschbd@uci.edu Abstract Abstract Scrabble is a well-known
More informationLENSES. INEL 6088 Computer Vision
LENSES INEL 6088 Computer Vision Digital camera A digital camera replaces film with a sensor array Each cell in the array is a Charge Coupled Device light-sensitive diode that converts photons to electrons
More informationThe introduction and background in the previous chapters provided context in
Chapter 3 3. Eye Tracking Instrumentation 3.1 Overview The introduction and background in the previous chapters provided context in which eye tracking systems have been used to study how people look at
More informationA 3D Multi-Aperture Image Sensor Architecture
A 3D Multi-Aperture Image Sensor Architecture Keith Fife, Abbas El Gamal and H.-S. Philip Wong Department of Electrical Engineering Stanford University Outline Multi-Aperture system overview Sensor architecture
More informationA Vehicle Speed Measurement System for Nighttime with Camera
Proceedings of the 2nd International Conference on Industrial Application Engineering 2014 A Vehicle Speed Measurement System for Nighttime with Camera Yuji Goda a,*, Lifeng Zhang a,#, Seiichi Serikawa
More informationScanArray Overview. Principle of Operation. Instrument Components
ScanArray Overview The GSI Lumonics ScanArrayÒ Microarray Analysis System is a scanning laser confocal fluorescence microscope that is used to determine the fluorescence intensity of a two-dimensional
More informationarxiv:physics/ v1 [physics.optics] 12 May 2006
Quantitative and Qualitative Study of Gaussian Beam Visualization Techniques J. Magnes, D. Odera, J. Hartke, M. Fountain, L. Florence, and V. Davis Department of Physics, U.S. Military Academy, West Point,
More informationNon Linear Image Enhancement
Non Linear Image Enhancement SAIYAM TAKKAR Jaypee University of information technology, 2013 SIMANDEEP SINGH Jaypee University of information technology, 2013 Abstract An image enhancement algorithm based
More informationImage processing for gesture recognition: from theory to practice. Michela Goffredo University Roma TRE
Image processing for gesture recognition: from theory to practice 2 Michela Goffredo University Roma TRE goffredo@uniroma3.it Image processing At this point we have all of the basics at our disposal. We
More informationImplementation of License Plate Recognition System in ARM Cortex A8 Board
www..org 9 Implementation of License Plate Recognition System in ARM Cortex A8 Board S. Uma 1, M.Sharmila 2 1 Assistant Professor, 2 Research Scholar, Department of Electrical and Electronics Engg, College
More informationX-ray generation by femtosecond laser pulses and its application to soft X-ray imaging microscope
X-ray generation by femtosecond laser pulses and its application to soft X-ray imaging microscope Kenichi Ikeda 1, Hideyuki Kotaki 1 ' 2 and Kazuhisa Nakajima 1 ' 2 ' 3 1 Graduate University for Advanced
More informationMaster thesis: Author: Examiner: Tutor: Duration: 1. Introduction 2. Ghost Categories Figure 1 Ghost categories
Master thesis: Development of an Algorithm for Ghost Detection in the Context of Stray Light Test Author: Tong Wang Examiner: Prof. Dr. Ing. Norbert Haala Tutor: Dr. Uwe Apel (Robert Bosch GmbH) Duration:
More informationChapters 1-3. Chapter 1: Introduction and applications of photogrammetry Chapter 2: Electro-magnetic radiation. Chapter 3: Basic optics
Chapters 1-3 Chapter 1: Introduction and applications of photogrammetry Chapter 2: Electro-magnetic radiation Radiation sources Classification of remote sensing systems (passive & active) Electromagnetic
More informationDigital Image Processing
Part 1: Course Introduction Achim J. Lilienthal AASS Learning Systems Lab, Dep. Teknik Room T1209 (Fr, 11-12 o'clock) achim.lilienthal@oru.se Course Book Chapters 1 & 2 2011-04-05 Contents 1. Introduction
More informationStructured-Light Based Acquisition (Part 1)
Structured-Light Based Acquisition (Part 1) CS635 Spring 2017 Daniel G. Aliaga Department of Computer Science Purdue University Passive vs. Active Acquisition Passive + Just take pictures + Does not intrude
More information