TECHNICAL UNIVERSITY OF CRETE DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING MACHINE VISION. Euripides G.M. Petrakis Michalis Zervakis

Size: px
Start display at page:

Download "TECHNICAL UNIVERSITY OF CRETE DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING MACHINE VISION. Euripides G.M. Petrakis Michalis Zervakis"

Transcription

1 TECHNICAL UNIVERSITY OF CRETE DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING MACHINE VISION Euripides G.M. Petrakis Michalis Zervakis Chania 2010 E.G.M. Petrakis Machine Vision (Introduction) 1

2 Machine Vision The goal of Machine Vision is to create a model of the real world from images A machine vision system recovers useful information about a scene from its two dimensional projections The world is three dimensional Two dimensional digitized images E.G.M. Petrakis Machine Vision (Introduction) 2

3 Machine Vision (2) Knowledge about the objects (regions) in a scene and projection geometry is required. The information which is recovered differs depending on the application Satellite, medical images etc. Processing takes place in stages: Enhancement, segmentation, image analysis and matching (pattern recognition). E.G.M. Petrakis Machine Vision (Introduction) 3

4 Illumination Image Acquisition Machine Vision System Scene 2D Digital Image Image Description Feedback The goal of a machine vision system is to compute a meaningful description of the scene (e.g., object)

5 Machine Vision Stages Image Acquisition (by cameras, scanners etc) Image Processing Image Enhancement Image Restoration Image Segmentation Image Analysis (Binary Image Processing) Model Matching Pattern Recognition Analog to digital conversion Remove noise/patterns, improve contrast Find regions (objects) in the image Take measurements of objects/relationships Match the above description with similar description of known objects (models) E.G.M. Petrakis Machine Vision (Introduction) 5

6 Image Processing Image Processing Input Image Output Image Image transformation image enhancement (filtering, edge detection, surface detection, computation of depth). Image restoration (remove point/pattern degradation: there exist a mathematical expression of the type of degradation like e.g. Added multiplicative noise, sin/cos pattern degradation etc). E.G.M. Petrakis Machine Vision (Introduction) 6

7 Image Segmentation Image Segmentation Input Image Regions/Objects Classify pixels into groups (regions/objects of interest) sharing common characteristics. Intensity/Color, texture, motion etc. Two types of techniques: Region segmentation: find the pixels of a region. Edge segmentation: find the pixels of its outline contour. E.G.M. Petrakis Machine Vision (Introduction) 7

8 Image Analysis Image Analysis Input Image Segmented Image (regions, objects) Measurements Take useful measurements from pixels, regions, spatial relationships, motion etc. Grey scale / color intensity values; Size, distance; Velocity; E.G.M. Petrakis Machine Vision (Introduction) 8

9 Pattern Recognition Model Matching Pattern Recognition Image/regions Measurements, or Structural description Class identifier Classify an image (region) into one of a number of known classes Statistical pattern recognition (the measurements form vectors which are classified into classes); Structural pattern recognition (decompose the image into primitive structures). E.G.M. Petrakis Machine Vision (Introduction) 9

10 Digital Image Representation Image: 2D array of gray level or color values Pixel: array element; Pixel value: arithmetic value of gray level or color intensity. Gray level image: f = f(x,y) - 3D image f=f(x,y,z) Color image (multi-spectral) f = {R red (x,y), G green (x,y), B blue (x,y)} E.G.M. Petrakis Machine Vision (Introduction) 10

11 What a computer sees is very different from what a human sees. A computer sees pixels (arithmetic values) while a human sees shapes, structures etc. E.G.M. Petrakis Machine Vision (Introduction) 11

12 Relationships to other fields Image Processing (IP) Pattern Recognition (PR) Computer Graphics (CG) Artificial Intelligence (AI) Neural Networks (NN) Psychophysics E.G.M. Petrakis Machine Vision (Introduction) 12

13 Image Processing (IP) IP transforms images to images Image filtering, compression, restoration IP is applied at the early stages of machine vision. IP is usually used to enhance particular information and to suppress noise. E.G.M. Petrakis Machine Vision (Introduction) 13

14 Pattern Recognition (PR) PR classifies numerical and symbolic data. Statistical: classify feature vectors. Structural: represent the composition of an object in terms of primitives and parse this description. PR is usually used to classify objects but object recognition in machine vision usually requires many other techniques. E.G.M. Petrakis Machine Vision (Introduction) 14

15 Statistical Pattern Recognition Pattern: the description of an an object Feature vector (size, roundness, color, texture) Pattern class: set of patterns with similar characteristics. Take measurements from a population of patterns. Classification: Map each pattern to a class. E.G.M. Petrakis Machine Vision (Introduction) 15

16 Structure of PR Systems Sensor input Processing Measurements Classification class E.G.M. Petrakis Machine Vision (Introduction) 16

17 Example of Statistical PR Two classes: I. W 1 Basketball players II. W 2 jockeys Description: X = (X 1, X 2 ) = (height, weight) X 1 W 1 W D(X) = AX 1 + BX 2 + C = 0 Decision function X 2 E.G.M. Petrakis Machine Vision (Introduction) 17

18 Syntactic Pattern Recognition The structure is important Identify primitives E.g., Shape primitives Break down an image (shape) into a sequence of such primitives. The way the primitives are related to each other to form a shape is unique. Use a grammar/algorithm Parse the shape E.G.M. Petrakis Machine Vision (Introduction) 18

19 Primitives G 1,L(G 1 ) : submedian Grammar G 2,L(G 2 ) : telocentric Grammar E.G.M. Petrakis Machine Vision (Introduction) 19

20 Each digit is represented by a waveform representing black/white, white/black transitions (scan the image from Left to right. E.G.M. Petrakis Machine Vision (Introduction) 20

21 Computer Graphics (CG) Machine vision is the analysis of images while CG is the decomposition of images: CG generates images from geometric primitives (lines, circles, surfaces). Machine vision is the inverse: estimate the geometric primitives from an image. Visualization and virtual reality bring these two fields closer. E.G.M. Petrakis Machine Vision (Introduction) 21

22 Artificial Intelligence (AI) Machine vision is considered to be sub-field of AI. AI studies the computational aspects of intelligence. CV is used to analyze scenes and compute symbolic representations from them. AI: perception, cognition, action Perception translates signals to symbols; Cognition manipulates symbols; Action translates symbols to signals that effect the world. E.G.M. Petrakis Machine Vision (Introduction) 22

23 Psychophysics Psychophysics and cognitive science have studied human vision for a long time. Many techniques in machine vision are related to what is known about human vision. E.G.M. Petrakis Machine Vision (Introduction) 23

24 Neural Networks (NN) NNs are being increasingly applied to solve many machine vision problems. NN techniques are usually applied to solve PR tasks. Image recognition/classification. They have also applied to segmentation and other machine vision tasks. E.G.M. Petrakis Machine Vision (Introduction) 24

25 Machine Vision Applications Robotics Medicine Remote Sensing Cartography Meteorology Quality inspection Reconnaissance E.G.M. Petrakis Machine Vision (Introduction) 25

26 Robot Vision Machine vision can make a robot manipulator much more versatile. Allow it to deal with variations in parts position and orientation. E.G.M. Petrakis Machine Vision (Introduction) 26

27 Remote Sensing Take images from high altitudes (from aircrafts, satellites). Find ships in the aerial image of the dock. Find if new ships have arrived. What kind of ships? E.G.M. Petrakis Machine Vision (Introduction) 27

28 Remote Sensing (2) Analyze the image Generate a description Match this descriptions with the descriptions of empty docs There are four ships Marked by + E.G.M. Petrakis Machine Vision (Introduction) 28

29 Medical Applications Assist a physician to reach a diagnosis. Construct 2D, 3D anatomy models of the human body. CG geometric models. Analyze the image to extract useful features. E.G.M. Petrakis Machine Vision (Introduction) 29

30 Machine Vision Systems There is no universal machine vision system One system for each application Assumptions: Good lighting; Low noise; 2D images Passive - Active environment Changes in the environment call for different actions (e.g., turn left, push the break etc). E.G.M. Petrakis Machine Vision (Introduction) 30

31 Vision by Man and Machine What is the mechanism of human vision? Can a machine do the same thing? There are many studies; Most are empirical. Humans and machines have different Software Hardware E.G.M. Petrakis Machine Vision (Introduction) 31

32 Human Hardware Photoreceptors take measurements of light signals. About 10 6 Photoreceptors. Retinal ganglion cells transmit electric and chemical signals to the brain Complex 3D interconnections; What the neurons do? In what sequence? Algorithms? Heavy Parallelism. E.G.M. Petrakis Machine Vision (Introduction) 32

33 Machine Vision Hardware PCs, workstations etc. Signals: 2D image arrays gray level/color values. Modules: low level processing, shape from texture, motion, contours etc. Simple interconnections. No parallelism. E.G.M. Petrakis Machine Vision (Introduction) 33

34 Course Outline Introduction to machine vision, applications, Image formation, color, reflectance, depth, stereopsis. Basic image processing techniques (filtering, digitization, restoration), Fourier transform. Binary image processing and analysis, Distance transform, morphological operators. E.G.M. Petrakis Machine Vision (Introduction) 34

35 Course Outline (2) Image segmentation (region segmentation, edge segmentation). Edge detection, edge enhancement and linking. Thresholding, region growing, region merging/splitting. Relaxation labeling, Hough transform. Image analysis, shape analysis. Polygonal approximation, splines, skeletons. Shape features, multi-resolution representations. E.G.M. Petrakis Machine Vision (Introduction) 35

36 Course Outline (3) Image representation, image - shape recognition and classification. Attributed relational graphs, semantic nets. Image - shape matching (Fourier descriptors, moments, matching in scale space). Texture representation and recognition, statistical and structural methods. Motion, motion detection, optical flow. Video E.G.M. Petrakis Machine Vision (Introduction) 36

37 Bibliography Machine Vision, Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, Mc Graw-Hill, 1995 (highly recommended!). "Image Processing, Analysis and Machine Vision", Milan Sonka, Vaclav Hlavac, Roger Boyle, PWS Publishing, Second Edition. "Machine Vision, Theory, Algorithms, Practicalities'', E. R. Davies, Academic Press, E.G.M. Petrakis Machine Vision (Introduction) 37

38 "Practical Computer Vision Using C'', J. R. Parker, John Wiley & Sons Inc., Selected articles from the literature. Lecture notes ( Webcourses ( E.G.M. Petrakis Machine Vision (Introduction) 38

39 Grading Scheme Final Exam (F): 40%, min 5 Assignments (Α): 40% Two assignments Obligatory E.G.M. Petrakis Machine Vision (Introduction) 39

Colorado School of Mines Computer Vision Professor William Hoff

Colorado School of Mines Computer Vision Professor William Hoff Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ 1 Introduction to 2 What is? A process that produces from images of the external world a description

More information

Analecta Vol. 8, No. 2 ISSN 2064-7964

Analecta Vol. 8, No. 2 ISSN 2064-7964 EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,

More information

Introduction to Computer Graphics

Introduction to Computer Graphics Introduction to Computer Graphics Torsten Möller TASC 8021 778-782-2215 torsten@sfu.ca www.cs.sfu.ca/~torsten Today What is computer graphics? Contents of this course Syllabus Overview of course topics

More information

Limitations of Human Vision. What is computer vision? What is computer vision (cont d)?

Limitations of Human Vision. What is computer vision? What is computer vision (cont d)? What is computer vision? Limitations of Human Vision Slide 1 Computer vision (image understanding) is a discipline that studies how to reconstruct, interpret and understand a 3D scene from its 2D images

More information

Digital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction

Digital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction Digital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction Content Remote sensing data Spatial, spectral, radiometric and

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

Introduction to Pattern Recognition

Introduction to Pattern Recognition Introduction to Pattern Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Spring 2009 CS 551, Spring 2009 c 2009, Selim Aksoy (Bilkent University)

More information

Digital Image Processing: Introduction

Digital Image Processing: Introduction Digital : Introduction Slides by Brian Mac Namee Brian.MacNamee@comp.dit.ie Materials found at: Slides: http://www.comp.dit.ie/bmacnamee/materials/dip/lectures/1-introduction.ppt Lectures: http://homepages.inf.ed.ac.uk/rbf/books/vernon/

More information

COMPARISON OF OBJECT BASED AND PIXEL BASED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS

COMPARISON OF OBJECT BASED AND PIXEL BASED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS COMPARISON OF OBJECT BASED AND PIXEL BASED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS B.K. Mohan and S. N. Ladha Centre for Studies in Resources Engineering IIT

More information

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic

More information

Graduate Co-op Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina

Graduate Co-op Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina Graduate Co-op Students Information Manual Department of Computer Science Faculty of Science University of Regina 2014 1 Table of Contents 1. Department Description..3 2. Program Requirements and Procedures

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

A Short Introduction to Computer Graphics

A Short Introduction to Computer Graphics A Short Introduction to Computer Graphics Frédo Durand MIT Laboratory for Computer Science 1 Introduction Chapter I: Basics Although computer graphics is a vast field that encompasses almost any graphical

More information

A Proposal for OpenEXR Color Management

A Proposal for OpenEXR Color Management A Proposal for OpenEXR Color Management Florian Kainz, Industrial Light & Magic Revision 5, 08/05/2004 Abstract We propose a practical color management scheme for the OpenEXR image file format as used

More information

Building an Advanced Invariant Real-Time Human Tracking System

Building an Advanced Invariant Real-Time Human Tracking System UDC 004.41 Building an Advanced Invariant Real-Time Human Tracking System Fayez Idris 1, Mazen Abu_Zaher 2, Rashad J. Rasras 3, and Ibrahiem M. M. El Emary 4 1 School of Informatics and Computing, German-Jordanian

More information

A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA

A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA N. Zarrinpanjeh a, F. Dadrassjavan b, H. Fattahi c * a Islamic Azad University of Qazvin - nzarrin@qiau.ac.ir

More information

A bachelor of science degree in electrical engineering with a cumulative undergraduate GPA of at least 3.0 on a 4.0 scale

A bachelor of science degree in electrical engineering with a cumulative undergraduate GPA of at least 3.0 on a 4.0 scale What is the University of Florida EDGE Program? EDGE enables engineering professional, military members, and students worldwide to participate in courses, certificates, and degree programs from the UF

More information

Digital Image Processing EE368/CS232

Digital Image Processing EE368/CS232 Digital Image Processing EE368/CS232 Bernd Girod, Gordon Wetzstein Department of Electrical Engineering Stanford University Digital Image Processing: Bernd Girod, 2013-2014 Stanford University -- Introduction

More information

Computer Science. Master of Science

Computer Science. Master of Science Computer Science Master of Science The Master of Science in Computer Science program at UALR reflects current trends in the computer science discipline and provides students with a solid theoretical and

More information

OBJECT TRACKING USING LOG-POLAR TRANSFORMATION

OBJECT TRACKING USING LOG-POLAR TRANSFORMATION OBJECT TRACKING USING LOG-POLAR TRANSFORMATION A Thesis Submitted to the Gradual Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements

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

Professor, D.Sc. (Tech.) Eugene Kovshov MSTU «STANKIN», Moscow, Russia

Professor, D.Sc. (Tech.) Eugene Kovshov MSTU «STANKIN», Moscow, Russia Professor, D.Sc. (Tech.) Eugene Kovshov MSTU «STANKIN», Moscow, Russia As of today, the issue of Big Data processing is still of high importance. Data flow is increasingly growing. Processing methods

More information

Data Storage. Chapter 3. Objectives. 3-1 Data Types. Data Inside the Computer. After studying this chapter, students should be able to:

Data Storage. Chapter 3. Objectives. 3-1 Data Types. Data Inside the Computer. After studying this chapter, students should be able to: Chapter 3 Data Storage Objectives After studying this chapter, students should be able to: List five different data types used in a computer. Describe how integers are stored in a computer. Describe how

More information

Color image processing: pseudocolor processing

Color image processing: pseudocolor processing Color image processing: pseudocolor processing by Gleb V. Tcheslavski: gleb@ee.lamar.edu http://ee.lamar.edu/gleb/dip/index.htm Spring 2008 ELEN 4304/5365 DIP 1 Preliminaries Pseudocolor (false color)

More information

School of Computer Science

School of Computer Science School of Computer Science Computer Science - Honours Level - 2014/15 October 2014 General degree students wishing to enter 3000- level modules and non- graduating students wishing to enter 3000- level

More information

Document Image Processing - A Review

Document Image Processing - A Review Document Image Processing - A Review Shazia Akram Research Scholar University of Kashmir (India) Dr. Mehraj-Ud-Din Dar Director, IT & SS University of Kashmir (India) Aasia Quyoum Research Scholar University

More information

Space Perception and Binocular Vision

Space Perception and Binocular Vision Space Perception and Binocular Vision Space Perception Monocular Cues to Three-Dimensional Space Binocular Vision and Stereopsis Combining Depth Cues 9/30/2008 1 Introduction to Space Perception Realism:

More information

MANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL

MANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL MANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL G. Maria Priscilla 1 and C. P. Sumathi 2 1 S.N.R. Sons College (Autonomous), Coimbatore, India 2 SDNB Vaishnav College

More information

ICS : 435. Computer Graphics Applications. Instructor : Da'ad Albalawneh

ICS : 435. Computer Graphics Applications. Instructor : Da'ad Albalawneh ICS : 435 Computer Graphics Applications Instructor : Da'ad Albalawneh Course Outline Applications CAD/CAM, Art, Entertainment, Education, Training, Visualization, GUI, Image Processing. Overview of Computer

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

Introduction. C 2009 John Wiley & Sons, Ltd

Introduction. C 2009 John Wiley & Sons, Ltd 1 Introduction The purpose of this text on stereo-based imaging is twofold: it is to give students of computer vision a thorough grounding in the image analysis and projective geometry techniques relevant

More information

Intelligent Flexible Automation

Intelligent Flexible Automation Intelligent Flexible Automation David Peters Chief Executive Officer Universal Robotics February 20-22, 2013 Orlando World Marriott Center Orlando, Florida USA Trends in AI and Computing Power Convergence

More information

An Approach for Utility Pole Recognition in Real Conditions

An Approach for Utility Pole Recognition in Real Conditions 6th Pacific-Rim Symposium on Image and Video Technology 1st PSIVT Workshop on Quality Assessment and Control by Image and Video Analysis An Approach for Utility Pole Recognition in Real Conditions Barranco

More information

Part-Based Recognition

Part-Based Recognition Part-Based Recognition Benedict Brown CS597D, Fall 2003 Princeton University CS 597D, Part-Based Recognition p. 1/32 Introduction Many objects are made up of parts It s presumably easier to identify simple

More information

VECTORAL IMAGING THE NEW DIRECTION IN AUTOMATED OPTICAL INSPECTION

VECTORAL IMAGING THE NEW DIRECTION IN AUTOMATED OPTICAL INSPECTION VECTORAL IMAGING THE NEW DIRECTION IN AUTOMATED OPTICAL INSPECTION Mark J. Norris Vision Inspection Technology, LLC Haverhill, MA mnorris@vitechnology.com ABSTRACT Traditional methods of identifying and

More information

Synthetic Aperture Radar: Principles and Applications of AI in Automatic Target Recognition

Synthetic Aperture Radar: Principles and Applications of AI in Automatic Target Recognition Synthetic Aperture Radar: Principles and Applications of AI in Automatic Target Recognition Paulo Marques 1 Instituto Superior de Engenharia de Lisboa / Instituto de Telecomunicações R. Conselheiro Emídio

More information

Robot Perception Continued

Robot Perception Continued Robot Perception Continued 1 Visual Perception Visual Odometry Reconstruction Recognition CS 685 11 Range Sensing strategies Active range sensors Ultrasound Laser range sensor Slides adopted from Siegwart

More information

Image Processing and Computer Graphics. Rendering Pipeline. Matthias Teschner. Computer Science Department University of Freiburg

Image Processing and Computer Graphics. Rendering Pipeline. Matthias Teschner. Computer Science Department University of Freiburg Image Processing and Computer Graphics Rendering Pipeline Matthias Teschner Computer Science Department University of Freiburg Outline introduction rendering pipeline vertex processing primitive processing

More information

Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with Panchromatic Textural Features

Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with Panchromatic Textural Features Remote Sensing and Geoinformation Lena Halounová, Editor not only for Scientific Cooperation EARSeL, 2011 Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with

More information

Data Storage 3.1. Foundations of Computer Science Cengage Learning

Data Storage 3.1. Foundations of Computer Science Cengage Learning 3 Data Storage 3.1 Foundations of Computer Science Cengage Learning Objectives After studying this chapter, the student should be able to: List five different data types used in a computer. Describe how

More information

Big Data: Image & Video Analytics

Big Data: Image & Video Analytics Big Data: Image & Video Analytics How it could support Archiving & Indexing & Searching Dieter Haas, IBM Deutschland GmbH The Big Data Wave 60% of internet traffic is multimedia content (images and videos)

More information

FSI Machine Vision Training Programs

FSI 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 information

3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension

3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension 3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension R.Queen Suraajini, Department of Civil Engineering, College of Engineering Guindy, Anna University, India, suraa12@gmail.com

More information

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS Nguyen Dinh Duong Department of Environmental Information Study and Analysis, Institute of Geography, 18 Hoang Quoc Viet Rd.,

More information

Template-based Eye and Mouth Detection for 3D Video Conferencing

Template-based Eye and Mouth Detection for 3D Video Conferencing Template-based Eye and Mouth Detection for 3D Video Conferencing Jürgen Rurainsky and Peter Eisert Fraunhofer Institute for Telecommunications - Heinrich-Hertz-Institute, Image Processing Department, Einsteinufer

More information

International Journal of Advanced Information in Arts, Science & Management Vol.2, No.2, December 2014

International Journal of Advanced Information in Arts, Science & Management Vol.2, No.2, December 2014 Efficient Attendance Management System Using Face Detection and Recognition Arun.A.V, Bhatath.S, Chethan.N, Manmohan.C.M, Hamsaveni M Department of Computer Science and Engineering, Vidya Vardhaka College

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

3D Scanner using Line Laser. 1. Introduction. 2. Theory

3D Scanner using Line Laser. 1. Introduction. 2. Theory . Introduction 3D Scanner using Line Laser Di Lu Electrical, Computer, and Systems Engineering Rensselaer Polytechnic Institute The goal of 3D reconstruction is to recover the 3D properties of a geometric

More information

Readings in Image Processing

Readings in Image Processing OVERVIEW OF IMAGE PROCESSING K.M.M. Rao*,Deputy Director,NRSA,Hyderabad-500 037 Introduction Image Processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space

More information

DAMAGED ROAD TUNNEL LASER SCANNER SURVEY

DAMAGED ROAD TUNNEL LASER SCANNER SURVEY University of Brescia - ITALY DAMAGED ROAD TUNNEL LASER SCANNER SURVEY Prof. Giorgio Vassena giorgio.vassena@unibs.it WORKFLOW - Demand analysis - Instruments choice - On field operations planning - Laser

More information

DATA VISUALIZATION GABRIEL PARODI STUDY MATERIAL: PRINCIPLES OF GEOGRAPHIC INFORMATION SYSTEMS AN INTRODUCTORY TEXTBOOK CHAPTER 7

DATA VISUALIZATION GABRIEL PARODI STUDY MATERIAL: PRINCIPLES OF GEOGRAPHIC INFORMATION SYSTEMS AN INTRODUCTORY TEXTBOOK CHAPTER 7 DATA VISUALIZATION GABRIEL PARODI STUDY MATERIAL: PRINCIPLES OF GEOGRAPHIC INFORMATION SYSTEMS AN INTRODUCTORY TEXTBOOK CHAPTER 7 Contents GIS and maps The visualization process Visualization and strategies

More information

Introduction. Chapter 1

Introduction. Chapter 1 1 Chapter 1 Introduction Robotics and automation have undergone an outstanding development in the manufacturing industry over the last decades owing to the increasing demand for higher levels of productivity

More information

STRAND: Number and Operations Algebra Geometry Measurement Data Analysis and Probability STANDARD:

STRAND: Number and Operations Algebra Geometry Measurement Data Analysis and Probability STANDARD: how August/September Demonstrate an understanding of the place-value structure of the base-ten number system by; (a) counting with understanding and recognizing how many in sets of objects up to 50, (b)

More information

INTRODUCTION IMAGE PROCESSING >INTRODUCTION & HUMAN VISION UTRECHT UNIVERSITY RONALD POPPE

INTRODUCTION IMAGE PROCESSING >INTRODUCTION & HUMAN VISION UTRECHT UNIVERSITY RONALD POPPE INTRODUCTION IMAGE PROCESSING >INTRODUCTION & HUMAN VISION UTRECHT UNIVERSITY RONALD POPPE OUTLINE Course info Image processing Definition Applications Digital images Human visual system Human eye Reflectivity

More information

Perception of Light and Color

Perception of Light and Color Perception of Light and Color Theory and Practice Trichromacy Three cones types in retina a b G+B +R Cone sensitivity functions 100 80 60 40 20 400 500 600 700 Wavelength (nm) Short wavelength sensitive

More information

Understanding Megapixel Camera Technology for Network Video Surveillance Systems. Glenn Adair

Understanding Megapixel Camera Technology for Network Video Surveillance Systems. Glenn Adair Understanding Megapixel Camera Technology for Network Video Surveillance Systems Glenn Adair Introduction (1) 3 MP Camera Covers an Area 9X as Large as (1) VGA Camera Megapixel = Reduce Cameras 3 Mega

More information

Computer Applications in Textile Engineering. Computer Applications in Textile Engineering

Computer Applications in Textile Engineering. Computer Applications in Textile Engineering 3. Computer Graphics Sungmin Kim http://latam.jnu.ac.kr Computer Graphics Definition Introduction Research field related to the activities that includes graphics as input and output Importance Interactive

More information

DEGREE PLAN INSTRUCTIONS FOR COMPUTER ENGINEERING

DEGREE PLAN INSTRUCTIONS FOR COMPUTER ENGINEERING DEGREE PLAN INSTRUCTIONS FOR COMPUTER ENGINEERING Fall 2000 The instructions contained in this packet are to be used as a guide in preparing the Departmental Computer Science Degree Plan Form for the Bachelor's

More information

Automatic Detection of PCB Defects

Automatic Detection of PCB Defects IJIRST International Journal for Innovative Research in Science & Technology Volume 1 Issue 6 November 2014 ISSN (online): 2349-6010 Automatic Detection of PCB Defects Ashish Singh PG Student Vimal H.

More information

APPLICATIONS AND USAGE

APPLICATIONS AND USAGE APPLICATIONS AND USAGE http://www.tutorialspoint.com/dip/applications_and_usage.htm Copyright tutorialspoint.com Since digital image processing has very wide applications and almost all of the technical

More information

LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE. indhubatchvsa@gmail.com

LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE. indhubatchvsa@gmail.com LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE 1 S.Manikandan, 2 S.Abirami, 2 R.Indumathi, 2 R.Nandhini, 2 T.Nanthini 1 Assistant Professor, VSA group of institution, Salem. 2 BE(ECE), VSA

More information

LONG BEACH CITY COLLEGE MEMORANDUM

LONG BEACH CITY COLLEGE MEMORANDUM LONG BEACH CITY COLLEGE MEMORANDUM DATE: May 5, 2000 TO: Academic Senate Equivalency Committee FROM: John Hugunin Department Head for CBIS SUBJECT: Equivalency statement for Computer Science Instructor

More information

Morphological segmentation of histology cell images

Morphological segmentation of histology cell images Morphological segmentation of histology cell images A.Nedzved, S.Ablameyko, I.Pitas Institute of Engineering Cybernetics of the National Academy of Sciences Surganova, 6, 00 Minsk, Belarus E-mail abl@newman.bas-net.by

More information

Next Generation Artificial Vision Systems

Next Generation Artificial Vision Systems Next Generation Artificial Vision Systems Reverse Engineering the Human Visual System Anil Bharath Maria Petrou Imperial College London ARTECH H O U S E BOSTON LONDON artechhouse.com Contents Preface xiii

More information

SLAM maps builder system for domestic mobile robots navigation

SLAM maps builder system for domestic mobile robots navigation 871 ECORFAN Journal SLAM maps builder system for domestic mobile robots navigation FAJARDO-M*, HERRERA-F, OSORIO-C, BENÍTEZ-M Centro de Investigacion USC Universidad de San Carlos State of Mexico Department

More information

Methods for Real-Time Full Web, Pulp, Paper and Paperboard Analysis ABB s Web Imaging System (WIS)

Methods for Real-Time Full Web, Pulp, Paper and Paperboard Analysis ABB s Web Imaging System (WIS) White paper/ Tommi Huotilainen (ABB), Myron Laster (ABB), Seppo Riikonen (ABB) Methods for Real-Time Full Web, Pulp, Paper and Paperboard Analysis ABB s Web Imaging System (WIS) High speed, high quality

More information

REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc])

REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc]) 244 REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc]) (See also General Regulations) Any publication based on work approved for a higher degree should contain a reference

More information

Cognitive Robotics: High-Level Robot Programming Inspired by Cognitive Science

Cognitive Robotics: High-Level Robot Programming Inspired by Cognitive Science Cognitive Robotics: High-Level Robot Programming Inspired by Cognitive Science David S. Touretzky Ethan Tira-Thompson Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213-3891 July

More information

What is Visualization? Information Visualization An Overview. Information Visualization. Definitions

What is Visualization? Information Visualization An Overview. Information Visualization. Definitions What is Visualization? Information Visualization An Overview Jonathan I. Maletic, Ph.D. Computer Science Kent State University Visualize/Visualization: To form a mental image or vision of [some

More information

Appendices master s degree programme Artificial Intelligence 2014-2015

Appendices master s degree programme Artificial Intelligence 2014-2015 Appendices master s degree programme Artificial Intelligence 2014-2015 Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability

More information

. Learn the number of classes and the structure of each class using similarity between unlabeled training patterns

. Learn the number of classes and the structure of each class using similarity between unlabeled training patterns Outline Part 1: of data clustering Non-Supervised Learning and Clustering : Problem formulation cluster analysis : Taxonomies of Clustering Techniques : Data types and Proximity Measures : Difficulties

More information

APPLYING COMPUTER VISION TECHNIQUES TO TOPOGRAPHIC OBJECTS

APPLYING COMPUTER VISION TECHNIQUES TO TOPOGRAPHIC OBJECTS APPLYING COMPUTER VISION TECHNIQUES TO TOPOGRAPHIC OBJECTS Laura Keyes, Adam Winstanley Department of Computer Science National University of Ireland Maynooth Co. Kildare, Ireland lkeyes@cs.may.ie, Adam.Winstanley@may.ie

More information

Levels of Analysis and ACT-R

Levels of Analysis and ACT-R 1 Levels of Analysis and ACT-R LaLoCo, Fall 2013 Adrian Brasoveanu, Karl DeVries [based on slides by Sharon Goldwater & Frank Keller] 2 David Marr: levels of analysis Background Levels of Analysis John

More information

Face Recognition For Remote Database Backup System

Face Recognition For Remote Database Backup System Face Recognition For Remote Database Backup System Aniza Mohamed Din, Faudziah Ahmad, Mohamad Farhan Mohamad Mohsin, Ku Ruhana Ku-Mahamud, Mustafa Mufawak Theab 2 Graduate Department of Computer Science,UUM

More information

Tracking Moving Objects In Video Sequences Yiwei Wang, Robert E. Van Dyck, and John F. Doherty Department of Electrical Engineering The Pennsylvania State University University Park, PA16802 Abstract{Object

More information

Introduction. Selim Aksoy. Bilkent University saksoy@cs.bilkent.edu.tr

Introduction. Selim Aksoy. Bilkent University saksoy@cs.bilkent.edu.tr Introduction Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr What is computer vision? What does it mean, to see? The plain man's answer (and Aristotle's, too)

More information

EHR CURATION FOR MEDICAL MINING

EHR CURATION FOR MEDICAL MINING EHR CURATION FOR MEDICAL MINING Ernestina Menasalvas Medical Mining Tutorial@KDD 2015 Sydney, AUSTRALIA 2 Ernestina Menasalvas "EHR Curation for Medical Mining" 08/2015 Agenda Motivation the potential

More information

Representing Geography

Representing Geography 3 Representing Geography OVERVIEW This chapter introduces the concept of representation, or the construction of a digital model of some aspect of the Earth s surface. The geographic world is extremely

More information

Computer Animation and Visualisation. Lecture 1. Introduction

Computer Animation and Visualisation. Lecture 1. Introduction Computer Animation and Visualisation Lecture 1 Introduction 1 Today s topics Overview of the lecture Introduction to Computer Animation Introduction to Visualisation 2 Introduction (PhD in Tokyo, 2000,

More information

How To Get A Computer Engineering Degree

How To Get A Computer Engineering Degree COMPUTER ENGINEERING GRADUTE PROGRAM FOR MASTER S DEGREE (With Thesis) PREPARATORY PROGRAM* COME 27 Advanced Object Oriented Programming 5 COME 21 Data Structures and Algorithms COME 22 COME 1 COME 1 COME

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

Depth and Excluded Courses

Depth and Excluded Courses Depth and Excluded Courses Depth Courses for Communication, Control, and Signal Processing EECE 5576 Wireless Communication Systems 4 SH EECE 5580 Classical Control Systems 4 SH EECE 5610 Digital Control

More information

Classifying Manipulation Primitives from Visual Data

Classifying Manipulation Primitives from Visual Data Classifying Manipulation Primitives from Visual Data Sandy Huang and Dylan Hadfield-Menell Abstract One approach to learning from demonstrations in robotics is to make use of a classifier to predict if

More information

Computer Graphics and Image Processing Introduction

Computer Graphics and Image Processing Introduction Computer Graphics and Image Processing Introduction Part 1 Lecture 1 1 COMPSCI 373 Lecturers: A. Prof. Patrice Delmas (303.391) Week 1-4 Contact details: p.delmas@auckland.ac.nz Office: 303-391 (3 rd level

More information

RUN-LENGTH ENCODING FOR VOLUMETRIC TEXTURE

RUN-LENGTH ENCODING FOR VOLUMETRIC TEXTURE RUN-LENGTH ENCODING FOR VOLUMETRIC TEXTURE Dong-Hui Xu, Arati S. Kurani, Jacob D. Furst, Daniela S. Raicu Intelligent Multimedia Processing Laboratory, School of Computer Science, Telecommunications, and

More information

BUILDING TELEPRESENCE SYSTEMS: Translating Science Fiction Ideas into Reality

BUILDING TELEPRESENCE SYSTEMS: Translating Science Fiction Ideas into Reality BUILDING TELEPRESENCE SYSTEMS: Translating Science Fiction Ideas into Reality Henry Fuchs University of North Carolina at Chapel Hill (USA) and NSF Science and Technology Center for Computer Graphics and

More information

The Scientific Data Mining Process

The Scientific Data Mining Process Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

More information

Automatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 269 Class Project Report

Automatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 269 Class Project Report Automatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 69 Class Project Report Junhua Mao and Lunbo Xu University of California, Los Angeles mjhustc@ucla.edu and lunbo

More information

BACnet for Video Surveillance

BACnet for Video Surveillance The following article was published in ASHRAE Journal, October 2004. Copyright 2004 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. It is presented for educational purposes

More information

Big Data: Rethinking Text Visualization

Big Data: Rethinking Text Visualization Big Data: Rethinking Text Visualization Dr. Anton Heijs anton.heijs@treparel.com Treparel April 8, 2013 Abstract In this white paper we discuss text visualization approaches and how these are important

More information

Course Overview. CSCI 480 Computer Graphics Lecture 1. Administrative Issues Modeling Animation Rendering OpenGL Programming [Angel Ch.

Course Overview. CSCI 480 Computer Graphics Lecture 1. Administrative Issues Modeling Animation Rendering OpenGL Programming [Angel Ch. CSCI 480 Computer Graphics Lecture 1 Course Overview January 14, 2013 Jernej Barbic University of Southern California http://www-bcf.usc.edu/~jbarbic/cs480-s13/ Administrative Issues Modeling Animation

More information

Study Regulations for the Master Course Visual Computing

Study Regulations for the Master Course Visual Computing Study Regulations for the Master Course Visual Computing As of January 26 th, 2006 Pursuant to 54 of Act No. 1556 on Saarland University (University Act UG) from June 23 rd, 2004 (Official Gazette p. 1782)

More information

Big Ideas in Mathematics

Big Ideas in Mathematics Big Ideas in Mathematics which are important to all mathematics learning. (Adapted from the NCTM Curriculum Focal Points, 2006) The Mathematics Big Ideas are organized using the PA Mathematics Standards

More information

Classification of Fingerprints. Sarat C. Dass Department of Statistics & Probability

Classification of Fingerprints. Sarat C. Dass Department of Statistics & Probability Classification of Fingerprints Sarat C. Dass Department of Statistics & Probability Fingerprint Classification Fingerprint classification is a coarse level partitioning of a fingerprint database into smaller

More information

EXECUTIVE SUPPORT SYSTEMS (ESS) STRATEGIC INFORMATION SYSTEM DESIGNED FOR UNSTRUCTURED DECISION MAKING THROUGH ADVANCED GRAPHICS AND COMMUNICATIONS *

EXECUTIVE SUPPORT SYSTEMS (ESS) STRATEGIC INFORMATION SYSTEM DESIGNED FOR UNSTRUCTURED DECISION MAKING THROUGH ADVANCED GRAPHICS AND COMMUNICATIONS * EXECUTIVE SUPPORT SYSTEMS (ESS) STRATEGIC INFORMATION SYSTEM DESIGNED FOR UNSTRUCTURED DECISION MAKING THROUGH ADVANCED GRAPHICS AND COMMUNICATIONS * EXECUTIVE SUPPORT SYSTEMS DRILL DOWN: ability to move

More information

Virtual Environments - Basics -

Virtual Environments - Basics - Virtual Environments - Basics - What Is Virtual Reality? A Web-Based Introduction Version 4 Draft 1, September, 1998 Jerry Isdale http://www.isdale.com/jerry/vr/whatisvr.html Virtual Environments allow

More information

Computer Vision for Quality Control in Latin American Food Industry, A Case Study

Computer Vision for Quality Control in Latin American Food Industry, A Case Study Computer Vision for Quality Control in Latin American Food Industry, A Case Study J.M. Aguilera A1, A. Cipriano A1, M. Eraña A2, I. Lillo A1, D. Mery A1, and A. Soto A1 e-mail: [jmaguile,aciprian,dmery,asoto,]@ing.puc.cl

More information

Digital Image Increase

Digital Image Increase Exploiting redundancy for reliable aerial computer vision 1 Digital Image Increase 2 Images Worldwide 3 Terrestrial Image Acquisition 4 Aerial Photogrammetry 5 New Sensor Platforms Towards Fully Automatic

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