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DIGITAL IMAGE PROCESSING - SCUT · 2019-03-04 · Digital image processing –perform digital signal processing operations on digital images (by means of digital computers.) MAPPING

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  • Xiangyu Yu

    School of Electronic and Information Engineering,

    South China University of Technology, P. R. China

    yuxy@scut.edu.cn

    DIGITAL IMAGE PROCESSING

  • ABOUT THIS COURSE(1)

    3/4/2019 DIGITAL IMAGE PROCESSING 2

    32 Class hours

    Date and Time:Week 2-9 Monday 14:30-16:10 330102

    Week 2-9 Tuesday 16:20-18:00 330202

    No Final Examination!

    Course Grading20% Presentation(about 15mins)

    30% Programming Assignments(most in python, some in Matlab and C)

    50% Project in group(3-5 person a group)

    Slides in English lecture in Mandarin

    Slide and resource are available on www2.scut.edu.cn/misip/

  • ABOUT THIS COURSE(2)

    3/4/2019 DIGITAL IMAGE PROCESSING 3

    Goals of this courseIntroductory course: basic concepts, classical methods, fundamental theorems

    Getting acquainted with basic properties of images

    Getting acquainted with various representations of image data

    Acquire fundamental knowledge in processing and analysis digital images

    Prerequisites

    Signals and systems

  • ABOUT THE LECTURER

    3/4/2019 DIGITAL IMAGE PROCESSING 4

    学习经历1996-2000 武汉大学 通信工程专业

    2000-2006 武汉大学 通信与信息系统专业

    工作经历2006-今 华南理工大学电子与信息学院 (2013年9月晋升副教授)

    2016.12-2017.11 英国华威大学 访问学者

    讲授课程包括2008-2013 2015级《移动通信》

    2004级《信号与系统》

    2004级 交通学院《通信技术基础》

    2006-2010级及2007-2011年辅修班《数字通信原理》

    2006级 计算机学院/2011-2013 2016级电信学院《通信原理》

    2010级电信学院/机械学院《数字电子技术》

    2006-2007级硕士研究生《接入网技术》

    2007年 南校区通选课《通信概论》

    2013-2015、2017级在职工程硕士《移动通信新进展》

    2016-2018级留学生硕士班《Principle of Modern Communications 》

  • TEXTBOOK

    3/4/2019 DIGITAL IMAGE PROCESSING 7

    Gonzalez, R. C. and Woods, R. E., "Digital Image Processing", Prentice Hall, 3rd Ed. , 2008

  • REFERENCE BOOK(1)

    3/4/2019 DIGITAL IMAGE PROCESSING 8

    Kenneth R. Castelman "Digital Image Processing", Prentice Hall

  • REFERENCE BOOK(2)

    3/4/2019 DIGITAL IMAGE PROCESSING 9

    Mark S. Nixon and Alberto S. Aguado, “Feature Extraction and Image Processing”, 2012.

  • REFERENCE BOOK(3)

    3/4/2019 DIGITAL IMAGE PROCESSING 10

    Alan C. Bovik, “Handbook of Image and Video Processing”, 2005.

  • REFERENCE BOOK(4)

    3/4/2019 DIGITAL IMAGE PROCESSING 11

    John C. Russ, “The Image Processing Handbook”, 7th Edition, CRC Press., 2017.

  • REFERENCE BOOK(5)

    3/4/2019 DIGITAL IMAGE PROCESSING 12

    Anil K. Jain, “Fundamentals of Digital Image Processing”, Prentice-Hall, Inc., 1989.

    http://images.google.co.il/imgres?imgurl=http://vig-fp.prenhall.com/bigcovers/0133361659.jpg&imgrefurl=http://www.pearsonhighered.com/educator/product/Fundamentals-of-Digital-Image-Processing/9780133361650.page&usg=__l2EoNhXUpxAntUp8ajlVc20qK5g=&h=648&w=464&sz=76&hl=en&start=1&sig2=QmTocY_ZeeBnLwAUC2Ujdw&um=1&itbs=1&tbnid=kBTJEhhwDDZMQM:&tbnh=137&tbnw=98&prev=/images?q=Fundamentals+of+Digital+Image+Processing++Jain&um=1&hl=en&sa=N&rlz=1T4GGHP_enIL366&tbs=isch:1&ei=SD-FS9eVMcye4QbRmZjPAQhttp://images.google.co.il/imgres?imgurl=http://vig-fp.prenhall.com/bigcovers/0133361659.jpg&imgrefurl=http://www.pearsonhighered.com/educator/product/Fundamentals-of-Digital-Image-Processing/9780133361650.page&usg=__l2EoNhXUpxAntUp8ajlVc20qK5g=&h=648&w=464&sz=76&hl=en&start=1&sig2=QmTocY_ZeeBnLwAUC2Ujdw&um=1&itbs=1&tbnid=kBTJEhhwDDZMQM:&tbnh=137&tbnw=98&prev=/images?q=Fundamentals+of+Digital+Image+Processing++Jain&um=1&hl=en&sa=N&rlz=1T4GGHP_enIL366&tbs=isch:1&ei=SD-FS9eVMcye4QbRmZjPAQ

  • REFERENCE BOOK(6)

    3/4/2019 DIGITAL IMAGE PROCESSING 13

    William K. Pratt, “Digital Image Processing”, 2nd Edition, Wiley & Sons, Inc., 1991.

  • REFERENCE BOOK(7)

    3/4/2019 DIGITAL IMAGE PROCESSING 14

    Milan Sonka · Roger Boyle · Vaclav Hlavac, “Image Processing: Analysis and Machine Vision”, 3nd Edition, Chapman & Hall., 1999.

  • REFERENCE BOOK(8)

    3/4/2019 DIGITAL IMAGE PROCESSING 15

    章毓晋 图像工程(上册) 图像处理(第三版) 清华大学出版社 2012.

  • REFERENCE BOOK(9)

    3/4/2019 DIGITAL IMAGE PROCESSING 16

    Gonzalez, Woods, and Eddins, “Digital Image Processing Using MATLAB”, 2nd Edition, 2009.

  • REFERENCE BOOK(10)

    3/4/2019 DIGITAL IMAGE PROCESSING 17

    Alasdair Mcandrew, “An Introduction to Digital Image Processing with Matlab”, 2004.

  • REFERENCE BOOK(11)

    3/4/2019 DIGITAL IMAGE PROCESSING 18

    左飞. 数字图像处理:原理与实践(MATLAB版)电子工业出版社 2014.

  • CONTENTS OF THIS COURSE

    3/4/2019 DIGITAL IMAGE PROCESSING 19

    L1 Introduction(C1)

    L2 Digital Image Fundamentals(C2)

    L3 Intensity Transformations(3.1-3.2)

    L4 Histogram(3.3)

    L5 Spatial Filtering and Edge Detection(3.4-3.8+10.2)

    L6 Filtering in Frequency domain(C4)

    L7 Image Restoration and Reconstruction(C5)

    L8 Image Segmentation(10.3 10.4 10.6)

    L9 Morphological Image Processing(C9 10.5)

    L10 Representation and Description(C11)

    L11 Wavelets and Multiresoluton Processing(C7)

    L12 Color Image Processing(C6)

    L13 Image Compression(C8)

  • ABOUT THE PROGRAMMING HOMEWORK

    3/4/2019 DIGITAL IMAGE PROCESSING 20

    Most In Jupyter Notebook

    A few in Matlab or C

    Send your homework to scutmobile@qq.com

    https://cviptools.siue.edu/

    mailto:scutmobile@qq.com

  • ABOUT THE PROJECT

    3/4/2019 DIGITAL IMAGE PROCESSING 21

    3-5 person a group

    Evaluation by you!

    Presentation time:Later June or Early July

  • PROJECT #1

    这学期的数字图像处理课,老师不提供课件给学生(不是真的!)。为了课后复习,班上的同学都用手机拍摄每张幻灯片,但有的同学有遗漏,有的同学拍得不清晰(手抖动,对焦错误),有的同学拍摄的图像被前面同学的头遮挡,此外还有光照等问题。班上的A同学决定现学现用,把同学手机拍的幻灯片联合起来形成一组较为完整且版本较清晰的幻灯片供复习。

    要求

    将一批图像放入文件夹中,软件自动生成课件PDF(由于手机文件通常以拍摄时间命名,所以不同手机图片的先后顺序容易确定)

    图片库由学生自建

    涉及知识

    图像配准、图像融合、图像拼接、图像去模糊等

    更高级处理(可选)

    对于图片中不够清晰的文字,可以用打印体替换。其他可以自己发挥。

    3/4/2019 DIGITAL IMAGE PROCESSING 22

  • PROJECT #2

    毕业季到了,B同学拍了很多毕业照,但发现照片里太多无关的人。Ta想利用图像处理课上学到的知识对这批照片进行一些处理尽可能消除图片中的无关人且让图像看上去较真实。

    要求

    图像库学生自建

    涉及知识

    图像背景提取,图像平均等

    更高级处理(可选)

    3/4/2019 DIGITAL IMAGE PROCESSING 23

  • PROJECT #3

    小C很喜欢旅游,并且有一群志同道合的驴友。每次旅行回来,Ta都会将所有驴友拍摄的照片收集并整理,然后在马蜂窝撰写游记。把每个人拍的照片看一遍很费时,Ta想利用图像处理课程学到的知识进行自动图像筛选。

    要求

    1. 对于同一场景不同人拍摄的图像,系统自动删除拍得不好的(主要是模糊,也包括比如有些建筑或人没拍到顶部)

    2.对于少量场景(如一幢建筑,D拍到顶部但没拍到底部,E反之),对于多幅这样的图像进行自动拼接。

    3.对于某些场景只有唯一一幅图片但质量不够好(如模糊),尝试提升图像质量。

    图片库由学生自建

    涉及知识

    图像配准、图像拼接、图像去模糊等

    更高级处理(可选)

    3/4/2019 DIGITAL IMAGE PROCESSING 24

  • PROJECT #4

    无人机中目标计数

    要求

    1. 统计无人机拍摄的图片中,统一类型目标的数量

    涉及知识

    图像边缘检测、角点检测等

    更高级处理(可选)

    3/4/2019 DIGITAL IMAGE PROCESSING 25

  • PROJECT #5 AND MORE

    Proposed by you

    3/4/2019 DIGITAL IMAGE PROCESSING 26

  • LECTURE 1INTRODUCTION

  • “One picture is worth more than ten thousand words”

    Anonymous

    3/4/2019 DIGITAL IMAGE PROCESSING 28

  • CONTENTS

    3/4/2019 DIGITAL IMAGE PROCESSING 29

    What is a digital image?

    What is digital image processing?

    History of digital image processing

    State of the art examples of digital image processing

    Key stages in digital image processing

    Image processing problems

    Applications of image processing

  • IMAGE AND IMAGE PROCESSING

    3/4/2019 DIGITAL IMAGE PROCESSING 30

    Image – A two-dimensional signal that can be observed by human visual system

    Digital image – Representation of images by sampling in time and space.

    Digital image processing – perform digital signal processing operations on digital images (by means of digital computers.)

  • MAPPING 3D REAL WORLD -> 2D IMAGE

    2D intensity image = perspective projection of the 3D scene

    information lost - transformation is not one-to-one

    geometric problem - information recovery

    understanding brightness info

    3/4/2019 DIGITAL IMAGE PROCESSING 31

  • THE VISUAL SCIENCES

    3/4/2019 DIGITAL IMAGE PROCESSING 32

    Computer

    Vision

    Rendering

    Image

    Image

    Processing

    Model

    3D Object

    Geometric

    Modeling

  • DIGITAL IMAGE

    A digital image is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixels

    Imag

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    3/4/2019 DIGITAL IMAGE PROCESSING 33

  • 3/4/2019 DIGITAL IMAGE PROCESSING 34

    Digital image = a multidimensional

    array of numbers (such as intensity image)

    or vectors (such as color image)

    Each component in the image

    called pixel associates with

    the pixel value (a single number in

    the case of intensity images or a

    vector in the case of color images).

    39871532

    22132515

    372669

    28161010

    39656554

    42475421

    67965432

    43567065

    99876532

    92438585

    67969060

    78567099

  • WHY DIGITAL?

    Computers store data and understand data in numerical form. We can say that a digital image is a numerical representation of a “picture” –a set of numbers interpreted by the computer which creates a visual representation that is understood by humans.

    3/4/2019 DIGITAL IMAGE PROCESSING 35

    255 255 199143 97 18732 12 3423 22 11

    244 198 179123 94 19532 43 5213 32 11

    253 217 23468 185 9713 12 2711 14 26

  • DIGITIZATION IMPLIES APPROXIMATION

    Pixel values typically represent gray levels, colours, heights, opacities etc

    Remember digitization implies that a digital image is an approximationof a real scene

    1 pixel

    Ima

    ges ta

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    Dig

    ital Im

    age P

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    2002)

    3/4/2019 DIGITAL IMAGE PROCESSING 36

  • ANALOG VS DIGITAL IMAGE PROCESSING

    3/4/2019 DIGITAL IMAGE PROCESSING 37

  • WHY DIP?

    One picture worth 1000 words!

    Support visual communication

    Facilitate inspection, diagnosis of complex systems

    Human body

    Manufacturing

    Entertainment

    Keep record, history

    Managing multimedia information

    Security,

    monitoring,

    watermarking, etc

    3/4/2019 DIGITAL IMAGE PROCESSING 38

  • IMAGE PROCESSING FIELDS

    Computer Graphics: The creation of images

    Image Processing: Enhancement or other manipulation of the image

    Computer Vision: Analysis of the image content

    3/4/2019 DIGITAL IMAGE PROCESSING 39

  • IMAGE PROCESSING V.S. COMPUTER VISION

    Image Processing

    Computer Vision

    Low Level

    High Level

    Acquisition, representation,

    compression, transmission

    image enhancement

    edge/feature extraction

    Pattern matching

    image "understanding“

    (Recognition, 3D)

    Image analysis - Image processing - Computer vision3/4/2019 DIGITAL IMAGE PROCESSING

  • WHAT IS DIP? (CONT…)

    The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes

    Low Level Process

    Input: Image

    Output: Image

    Examples: Noise

    removal, image

    sharpening

    Mid Level Process

    Input: Image

    Output:

    Attributes(Features)

    Examples: Object

    recognition,

    segmentation

    High Level Process

    Input:

    Attributes(Features)

    Output:

    Understanding(Analysi

    s)

    Examples: Scene

    understanding,

    autonomous navigation

    In this course we will

    stop here3/4/2019 DIGITAL IMAGE PROCESSING 41

  • IMAGE PROCESSING FIELDS

    Input / Output Image Description

    Image Image Processing Computer Vision

    Description Computer Graphics AI

    Sometimes, Image Processing is defined as “a

    discipline in which both the input and output of a

    process are images

    But, according to this classification, trivial tasks of

    computing the average intensity of an image would not

    be considered an image processing operation

    3/4/2019 DIGITAL IMAGE PROCESSING 42

  • COMPUTERIZED PROCESSES TYPES

    Low-Level Processes:

    Input and output are images

    Tasks: Primitive operations, such as, image processing to reduce noise, contrast enhancement and image sharpening

    3/4/2019 DIGITAL IMAGE PROCESSING 43

  • LOW LEVEL DIGITAL IMAGE PROCESSING

    Low level computer vision ~ digital image processing

    Image Acquisition

    image captured by a sensor (TV camera) and digitized

    Preprocessing

    suppresses noise (image pre-processing)

    enhances some object features - relevant to understanding the image

    edge extraction, smoothing, thresholding etc.

    Image segmentation

    separate objects from the image background

    colour segmentation, region growing, edge linking etc

    Object description and classification

    after segmentation

    3/4/2019 DIGITAL IMAGE PROCESSING 44

  • LOW-LEVEL

    3/4/2019 DIGITAL IMAGE PROCESSING 45

    blurring

    sharpening

  • COMPUTERIZED PROCESSES TYPES

    Mid-Level Processes:

    Inputs, generally, are images. Outputs are attributes extracted from those images (edges, contours, identity of individual objects)

    Tasks:

    Segmentation (partitioning an image into regions or objects)

    Description of those objects to reduce them to a form suitable for computer processing

    Classifications (recognition) of objects

    3/4/2019 DIGITAL IMAGE PROCESSING 46

  • MID-LEVEL

    3/4/2019 DIGITAL IMAGE PROCESSING 47

    K-means

    clustering

    original color image regions of homogeneous color

    (followed by

    connected

    component

    analysis)

    data

    structure

  • COMPUTERIZED PROCESSES TYPES

    High-Level Processes: Image analysis and computer vision

    3/4/2019 DIGITAL IMAGE PROCESSING 48

  • KEY STAGES IN DIGITAL IMAGE PROCESSING

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 49

  • IMAGE ACQUISITION

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 50

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 1: Image Acquisition

    The image is captured by a sensor (eg. Camera), and digitized if the output of the camera or sensor is not already in digital form, using analogue-to-digital convertor

    3/4/2019 DIGITAL IMAGE PROCESSING 51

  • IMAGE MODELLING-IMAGE TRANSFORMS (PART 1)

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    Original Image Fourier Transform

    Amplitude Phase

    3/4/2019 DIGITAL IMAGE PROCESSING 52

    http://www.dai.ed.ac.uk/HIPR2/images/cln1.gifhttp://www.dai.ed.ac.uk/HIPR2/images/cln1.gifhttp://www.dai.ed.ac.uk/HIPR2/images/cln1fur2.gifhttp://www.dai.ed.ac.uk/HIPR2/images/cln1fur2.gifhttp://www.dai.ed.ac.uk/HIPR2/images/cln1fur3.gifhttp://www.dai.ed.ac.uk/HIPR2/images/cln1fur3.gif

  • IMAGE ENHANCEMENT (PART 2)

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 53

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 2: Image Enhancement

    The process of manipulating an image so that the result is more suitable than the original for specific applications.

    The idea behind enhancement techniques is to bring out details that are hidden, or simple to highlight certain features of interest in an image.

    3/4/2019 DIGITAL IMAGE PROCESSING 54

  • EXAMPLES: IMAGE ENHANCEMENT

    One of the most common uses of DIP techniques: improve quality, remove noise etc

    Imag

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    3/4/2019 DIGITAL IMAGE PROCESSING 55

  • IMAGE RESTORATION (PART 3)

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 56

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 3: Image Restoration

    - Improving the appearance of an image

    - Tend to be mathematical or probabilistic models. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” enhancement result.

    3/4/2019 DIGITAL IMAGE PROCESSING 57

  • IMAGE RESTORATION

    3/4/2019 DIGITAL IMAGE PROCESSING 58

    Distorted Image Restored Image

  • DISTORTION DUE TO CAMERA MISFOCUS

    3/4/2019 DIGITAL IMAGE PROCESSING 59

    Original image Distorted image

  • DISTORTION DUE TO CAMERA MISFOCUS

    Camera lens

    3/4/2019 DIGITAL IMAGE PROCESSING 60

  • DISTORTION DUE TO MOTION

    Camera lens

    3/4/2019 DIGITAL IMAGE PROCESSING 61

  • DISTORTION DUE TO RANDOM NOISE

    Distorted imageOriginal image

    3/4/2019 DIGITAL IMAGE PROCESSING 62

  • IMAGE COMPRESSION (PART 4)

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 63

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 6: Compression

    Techniques for reducing the storage required to save an image or the bandwidth required to transmit it.

    3/4/2019 DIGITAL IMAGE PROCESSING 64

  • MORPHOLOGICAL PROCESSING

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 65

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 7: Morphological Processing

    Tools for extracting image components that are useful in the representation and description of shape.

    In this step, there would be a transition from processes that output images, to processes that output image attributes.

    3/4/2019 DIGITAL IMAGE PROCESSING 66

  • SEGMENTATION

    3/4/2019 DIGITAL IMAGE PROCESSING 67

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 8: Image Segmentation

    Segmentation procedures partition an image into its constituent parts or objects.

    Important Tip: The more accurate the segmentation, the more likely recognition is to succeed.

    3/4/2019 DIGITAL IMAGE PROCESSING 68

  • OBJECT RECOGNITION

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 69

  • REPRESENTATION AND DESCRIPTION

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 70

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 9: Representation and Description

    -Representation: Make a decision whether the data should be represented as a boundary or as a complete region. It is almost always follows the output of a segmentation stage.

    -Boundary Representation: Focus on external shape characteristics, such as corners and inflections (انحناءات)

    -Region Representation: Focus on internal properties, such as texture or skeleton (هيكلية) shape

    3/4/2019 DIGITAL IMAGE PROCESSING 71

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 9: Representation and Description

    -Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing (mainly recognition)

    - Description: also called, feature selection, deals with extracting attributes that result in some information of interest.

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  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 9: Recognition and Interpretation

    Recognition: the process that assigns label to an object based on the information provided by its description.

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  • COLOUR IMAGE PROCESSING

    Image

    Acquisition

    Image

    Restoration

    Image

    Compression

    Morphological

    Processing

    Object

    Recognition

    Image

    Enhancement

    Segmentation

    Real life sceneColour Image

    Processing

    Representation

    & Description

    Image

    Modelling

    (Transforms)

    3/4/2019 DIGITAL IMAGE PROCESSING 74

  • FUNDAMENTAL STEPS IN DIP: (DESCRIPTION)

    Step 4: Colour Image Processing

    Use the colour of the image to extract features of interest in an image

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  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM Network

    Image displays Computer Mass storage

    HardcopySpecialized image

    processing hardware

    Image processing

    software

    Image sensorsProblem Domain

    Typical general-

    purpose DIP

    system

    G3E-P.513/4/2019 DIGITAL IMAGE PROCESSING G3C-P.16

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    1. Image Sensors

    Two elements are required to acquire digital images. The first isthe physical device that is sensitive to the energy radiated by theobject we wish to image (Sensor). The second, called a digitizer, isa device for converting the output of the physical sensing deviceinto digital form.

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    G3E-P.50-51G3C-P.16

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    2. Specialized Image Processing Hardware

    Usually consists of the digitizer, mentioned before, plus

    hardware that performs other primitive operations, suchas an arithmetic logic unit (ALU), which performsarithmetic and logical operations in parallel on entireimages.

    This type of hardware sometimes is called a front-endsubsystem, and its most distinguishing characteristic isspeed. In other words, this unit performs functions thatrequire fast data throughputs that the typical maincomputer cannot handle.

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    G3E-P.51G3C-P.16

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    3. Computer

    The computer in an image processing system is ageneral-purpose computer and can range from a PC to asupercomputer. In dedicated applications, sometimesspecially designed computers are used to achieve arequired level of performance.

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    G3E-P.51G3C-P.16

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    4. Image Processing Software

    Software for image processing consists of specializedmodules that perform specific tasks. A well-designedpackage also includes the capability for the user to writecode that, as a minimum, utilizes the specialized modules.

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    G3E-P.51-52G3C-P.16

  • IMAGE PROCESSING SOFTWARE

    3/4/2019 DIGITAL IMAGE PROCESSING 81

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    5. Mass Storage Capability

    Mass storage capability is a must in a image processingapplications. And image of sized 1024 × 1024 pixelsrequires one megabyte of storage space if the image isnot compressed.

    Digital storage for image processing applications fallsinto three principal categories:

    1. Short-term storage for use during processing.

    2. on line storage for relatively fast recall

    3. Archival storage, characterized by infrequent access

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    G3E-P.52G3C-P.16

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    5. Mass Storage Capability

    One method of providing short-term storage is computer memory.Another is by specialized boards, called frame buffers, that storeone or more images and can be accessed rapidly.

    The on-line storage method, allows virtually instantaneous imagezoom, as well as scroll (vertical shifts) and pan (horizontal shifts).On-line storage generally takes the form of magnetic disks andoptical-media storage. The key factor characterizing on-linestorage is frequent access to the stored data.

    Finally, archival storage is characterized by massive storagerequirements but infrequent need for access.

    3/4/2019 DIGITAL IMAGE PROCESSING 83

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    6. Image Displays

    The displays in use today are mainly color (preferably flat screen) TV monitors. Monitors are driven by the outputs of the image and graphics display cards that are an integral part of a computer system.

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    G3E-P.52G3C-P.16

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    7. Hardcopy devices

    Used for recording images, include laser printers, film cameras, heat-sensitive devices, inkjet units and digital units, such as optical and CD-Rom disks.

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    G3E-P.52G3C-P.16

  • COMPONENTS OF AN IMAGE PROCESSING SYSTEM

    8. Networking

    Is almost a default function in any computersystem, in use today. Because of the largeamount of data inherent in image processingapplications the key consideration in imagetransmission is bandwidth.

    In dedicated networks, this typically is not aproblem, but communications with remote sitesvia the internet are not always as efficient.

    3/4/2019 DIGITAL IMAGE PROCESSING 86

    G3E-P.52G3C-P.17

  • THE FIRST PHOTOGRAPH

    The First Photograph, or more specifically, the earliest known surviving photograph made in a camera, was taken by Joseph NicéphoreNiépce in 1826 or 1827. The image depicts the view from an upstairs window at Niépce's estate, Le Gras, in the Burgundy region of France. Learn more about the First Photograph through the links below.

    First successful commercial photograph due to Eastman in late 19th

    3/4/2019 DIGITAL IMAGE PROCESSING 87the original plate manually enhanced version

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  • PRESENTATION ASSIGMENT#1

    Talking about the history of photography

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  • Early 1920s: One of the first applications of digital imaging was in the newspaper industry

    The Bartlane cable picture transmission service

    Images were transferred by submarine cable between London and New York

    Pictures were coded for cable transfer and reconstructed at the receiving end on a telegraph printer

    Important to reduce transfer time.

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    HISTORY OF DIGITAL IMAGE PROCESSING

    G3E-P.253/4/2019 DIGITAL IMAGE PROCESSING

  • HISTORY OF DIGITAL IMAGE PROCESSING

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    FIGURE A digital picture produced in 1921

    from a coded tape by a telegraph printer

    with special type faces. (McFarlane.†)

    FIGURE A digital picture made in 1922 from a

    tape punched after the signals had crossed the

    Atlantic twice. Some errors are visible.

    (McFarlane.)

    Better quality

    G3E-P.25-26

  • HISTORY OF DIP (CONT…)

    Mid to late 1920s: Improvements to the Bartlane system resulted in higher quality images

    New reproduction processes based on photographic techniques

    Increased number of tones in reproduced images

    Early 15 tone digital

    image

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    Digital computers: 1940

    1st computer able to do digital image

    manipulations: early 1960

    Unretouched cable picture of Generals Pershing

    and Foch, transmitted in 1929 from London to

    New York by 15-tone equipment. (McFarlane.) G3E-P.26

    3/4/2019 DIGITAL IMAGE PROCESSING

  • HISTORY OF DIP (CONT…)

    1960s: Improvements in computing technology and the onset of the space race led to a surge of work in digital image processing

    1964: Computers used to improve the quality of images of the moon taken by the Ranger 7 probe

    Such techniques were used in other space missions including the Apollo landings

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    From computers, meaningful image processing tasks appeared.

    3/4/2019 DIGITAL IMAGE PROCESSING 92

  • 3/4/2019 DIGITAL IMAGE PROCESSING 93

    Ranger 7

    A picture of the moon taken by

    the Ranger 7 on 31 July 1964 at

    13:09 UT about 17 minutes

    before impact

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    Ranger 7 cameras system

  • HISTORY OF DIP (CONT…)

    1970s: Digital image processing begins to be used in medical applications

    1979: Sir Godfrey N. Hounsfield & Prof. Allan M. Cormack share the Nobel Prize in medicine for the invention of tomography, the technology behind Computerised Axial Tomography (CAT) scans

    Typical head slice CAT

    image

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  • MRI

    3/4/2019 DIGITAL IMAGE PROCESSING 95

  • HISTORY OF DIP (CONT…)

    1980s - Today: The use of digital image processing techniques has exploded and they are now used for all kinds of tasks in all kinds of areas

    Image enhancement/restoration

    Artistic effects

    Medical visualisation

    Industrial inspection

    Law enforcement

    Human computer interfaces

    3/4/2019 DIGITAL IMAGE PROCESSING 96

  • PRESENTATION ASSIGMENT#2

    Deep learning in image processing. Maybe more than one topic.

    3/4/2019 DIGITAL IMAGE PROCESSING 97

  • APPLICATIONS OF DIGITAL IMAGE PROCESSING?

    … virtually, everywhere!

    • Industry: inspection/sorting; manufacturing (robot vision)

    • Environment: strategic surveillance (hydro-dams, forests, forestfires, mine galleries) by surveillance cameras, autonomousrobots

    • Medicine: medical imaging (ultrasound, MRI, CT, visible)

    • Culture: digital libraries; cultural heritage preservation (storage,restoration, analysis – indexing)

    • Television: broadcasting, video editing, efficient storage

    • Education & tourism: multi-modal, intelligent human-computerinterfaces, with emotion recognition components

    • Security/authentication (iris recognition, signature verification)

    … etc…

    3/4/2019 DIGITAL IMAGE PROCESSING 98

  • APPLICATIONS – IMAGING MODALITIES

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    Principal energy source for images today: electromagnetic

    energy spectrum.

    Electromagnetic energy spectrum

    G3E-P.293/4/2019 DIGITAL IMAGE PROCESSING G3C-P.5

  • GAMMA-RAY IMAGING

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    Examples of gamma-ray imaging. (a) Bone scan.

    (b) PET image. (c) Cygnus Loop. (d) Gamma

    radiation (bright spot) from a reactor valve. G3E-P.30

    Gamma rays:

    Nuclear medicine

    (injection of

    radioactive tracer)

    Astronomical

    observations

    (object generate

    gamma rays)

    3/4/2019 DIGITAL IMAGE PROCESSING G3C-P.5

  • PET

    PET=Positron Emission Tomography

    imaging at molecular level

    3/4/2019 DIGITAL IMAGE PROCESSING 101

  • X-RAY IMAGING

    G3E-P.32

    Figure X-Ray images. (a) Chest X-ray. (b)

    Aortic angiogram. (c) Head CT. (d) Circuit

    boards. (e) Cygnus Loop.

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    X-rays (the oldest radiation-type imaging)

    -Discovered in 1895 by German

    physicist William Roentgen

    (Nobel prize in physics, 1901)

    -used in medicine/industry/astronomy

    X-ray tube (catode/anode, controlled by

    voltage), emitting ray, absorbption by

    object, rest captured onto a film,

    digitised.

    C.A.T. (Computerized Axial Tomography)

    uses X-rays.

    3/4/2019 DIGITAL IMAGE PROCESSING G3C-P.6

  • X-RAY IMAGING

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    X-ray of kneeCopyright: Radiology Centennial, Inc.

    An x-ray picture

    (radiograph) taken by

    Röntgen of Albert von

    Kölliker's hand at a

    public lecture on 23

    January 1896

    3/4/2019 DIGITAL IMAGE PROCESSING 103

    http://en.wikipedia.org/wiki/Albert_von_K%C3%B6lliker

  • ULTRAVIOLET BAND

    microscopy (fluorescence)

    the excited electron jumps to another energy level emitting light as a low-energy photon in the red region

    lasers

    biological imaging

    astronomical imaging

    industrial inspections

    3/4/2019 DIGITAL IMAGE PROCESSING 104

    A fluorescent tracer

    is bind to a

    molecular target

  • IMAGING IN THE ULTRAVIOLET BAND

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    Examples of ultraviolet imaging. (a) Normal

    corn. (b) Corn infected by smut. (c) Cygnus Loop.3/4/2019 DIGITAL IMAGE PROCESSING G3C-P.7

  • VISIBLE AND INFRARED BAND

    the most familiar to us….

    light microscopy

    infrared: remote sensing, weather prediction,

    satellite sensing/ night vision

    3/4/2019 DIGITAL IMAGE PROCESSING 106

  • IMAGING IN THE VISIBLE AND INFRARED BANDS

    G3E-P.35

    Figure Light microscopy images.

    (a) Taxol (anticancer agent),

    magnified 250X, (b)

    Cholesterol, 40X (c)

    microprocessor 60X. (d) Nickel

    oxide thin film, 600 X. (e)

    Surface of audio CD 1750 X. (f)

    Organic superconductor 450 X. Imag

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  • REMOTE SENSING

    3/4/2019 DIGITAL IMAGE PROCESSING 108

    G3E-P.36G3C-P.8

  • APPLICATIONS: GIS

    Geographic Information Systems

    Satellite imagery

    Terrain classification (LANDSAT)

    Meteorology (NOAA)

    LANDSAT satellite images of the Washington,

    D.C. area. The numbers refer to the thematic

    bands in Table 1.1. G3E-P.36Im

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  • 3/4/2019 DIGITAL IMAGE PROCESSING 110

    Mount Everest

    Mount Everest is the highest mountain on Earth, rising 29,029 feet above sea level. It is located on the border of Nepal and Tibet in the Himalayan mountain range. In Tibet the mountain is known as Chomolunga and in Nepal it is called Sagarmatha.

    This image of Mount Everest was taken from the

    International Space Station on November 26, 2003.

    In this image you can see Mount Everest covered in white snow with Lhotse, the fourth highest mountain on Earth connected via the South Col — the saddle point between the two peaks. Vegetation appears green and rock and soil appear brown in the image.This natural color Landsat 5 image was collected on June 11, 2005. It was created using bands 3, 2 and 1. Mount Everest is found on Landsat WRS-2 Path 140 Row 41.

    Nasa/Landsat

  • 3/4/2019 DIGITAL IMAGE PROCESSING 111

    Mono Lake, CaliforniaNasa/Landsat

    This Landsat 7 image of Mono Lake was acquired on July 27,

    2000. This image is a false-color composite made from the

    mid-infrared, near-infrared, and green spectral channels of the

    Landsat 7 ETM+ sensor – it also includes the panchromatic

    15-meter band for spatial sharpening purposes. In this image,

    the waters of Mono Lake appear a bluish-black and

    vegetation appears bright green. You will notice the

    vegetation to the west of the lake and following the tributaries

    that enter the lake.

  • Weather Observation, visible and infrared bands

    G3E-P.37

    Satellite image of Hurricane Katrina taken on

    August 29, 2005.

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  • TYPHOON MANGKHUT

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  • Interpretation of aerial photography is a problem domain in both

    computer vision and registration.

    INTERPRETATION OF AERIAL PHOTOGRAPHY

    3/4/2019 DIGITAL IMAGE PROCESSING 114

  • SATELLITE IMAGERY

    3/4/2019 DIGITAL IMAGE PROCESSING 115

    Volcanos in Russia and Alaska

  • REMOTE SENSING OF OUR COUNTRY

    Tiangong 1

    Tiangong 2

    GF1

    3/4/2019 DIGITAL IMAGE PROCESSING 116

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  • APPLICATIONS: GIS (CONT…)

    Night-Time Lights of the World data set

    (infra red)

    Global inventory of human settlement

    Not hard to imagine the kind of analysis that might be done using this data

    Infrared satellite images of the Americas. The

    small shaded map is provided for reference.

    Infrared imaging

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  • 3/4/2019 DIGITAL IMAGE PROCESSING 118

    Infrared satellite images of the remaining populated parts of the world. The small shaded map is provided for reference.

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    G3E-P.39G3C-P.9

  • ASTRONOMICAL IMAGES

    3/4/2019 DIGITAL IMAGE PROCESSING 119

  • APPLICATIONS: THE HUBBLE TELESCOPE

    Launched in 1990 the Hubble telescope can take images of very distant objects

    However, an incorrect mirror made many of Hubble’s images useless

    Image processing techniques were used to fix this

    3/4/2019 DIGITAL IMAGE PROCESSING 120

    http://en.wikipedia.org/wiki/Image:Hst_sts82.jpghttp://en.wikipedia.org/wiki/Image:Hst_sts82.jpg

  • INDUSTRIAL INSPECTION(INDUSTRIAL VISION SYSTEMS)

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  • APPLICATIONS: INDUSTRIAL INSPECTIONHuman operators are expensive, slow andunreliable

    Make machines do thejob instead

    Industrial vision systems are used in all kinds of industries

    Can we trust them?

    Some examples of manufactured goods checked using digital image processing. (a) Circuit board controller. (b) Packaged pills. (c) Bottles. (d) Air bubbles in a clear plastic product. (e) Cereal. (f) Image of intraocular implant. G3E-P.40

    Visible range:

    automated inspection tasks

    Automated visual inspection

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  • APPLICATIONS: PCB INSPECTION

    Printed Circuit Board (PCB) inspection

    Machine inspection is used to determine that all components are present and that all solder joints are acceptable

    Both conventional imaging and x-ray imaging are used

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  • 3/4/2019 DIGITAL IMAGE PROCESSING 124

    Some additional examples of imaging in the visible spectrum. (a) Thumb print. (b) Paper currency. (c) and (d) Automated license plate reading.

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    G3C-P.10

  • IMAGING IN THE MICROWAVE BAND

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    G3E-P.42

    Spaceborne radar image of mountainous region in southeast

    Tibet.

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    G3C-P.10

  • RADIO BAND

    MRI - imaging

    (Nobel prizes: Bloch 1952,

    … , 2003)

    A strong magnet passes radio waves

    though short pulses which causes

    a response pulse (echo)

    3/4/2019 DIGITAL IMAGE PROCESSING 126

  • IMAGING IN THE RADIO BANDMRI

    G3E-P.43

    MRI images of a human (a) knee, and (b) spine.

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  • MRI

    3/4/2019 DIGITAL IMAGE PROCESSING 128MRI of normal brain

    http://www.med.harvard.edu/AANLIB/cases/caseM/mr1_t/024.gifhttp://www.med.harvard.edu/AANLIB/cases/caseM/mr1_t/024.gif

  • MRI

    Take slice from MRI scan of canine heart, and find boundaries between types of tissue

    Image with gray levels representing tissue density

    Use a suitable filter to highlight edges

    Original MRI Image of a Dog Heart Edge Detection Image

    3/4/2019 DIGITAL IMAGE PROCESSING 129

  • Figure Images of the Crab Pulsar (in the center of each image)

    covering the electromagnetic spectrum.

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  • EXAMPLES IN WHICH OTHER IMAGING MODALITIES ARE USED

    Cross-sectional image of a seismic model. The arrow points to a hydrocarbon (oil and/or gas) trap.

    Sound

    G3E-P.44

    Other sources of energy

    beside electromagnetic waves:

    - acoustic waves

    (seismic, marine/atmospheric,

    sonar/radar, ultrasound)

    - electron microscopy

    - synthetic images

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  • APPLICATIONS: MEDICINE (CONT...)

    Figure Examples of ultrasound imaging. (a) A fetus. (b) Another view of the fetus. (c) Thyroids. (d) Muscle layers showing lesion.

    Ultrasound

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  • DIGITAL IMAGE PROCESSING

    ULTRASOUND IMAGEProfiles of a fetus at 4 months, the face is about 4cm long

    Ultra sound image is another imaging modality

    The fetal arm with the major arteries (radial and ulnar) clearly delineated.

    http://www.parenthood.com/us.html

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  • MEDICAL IMAGES

    3/4/2019 DIGITAL IMAGE PROCESSING 134

    Fetal ultrasound

  • Figure (a)250X SEM image of a tungsten filament following thermal failure(note the shattered pieces on the lower left).(b)2500XSEM image of a damaged integrated circuit. The white fibers are oxides resulting from thermal destruction.

    Electron Microscope

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    DIGITAL IMAGE PROCESSING

    G3C-P.13

  • Figure (a) and (b) Fractal images. (c) and (d) Images generated from 3-D computer models of the objects shown.

    Images generated by computers

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  • APPLICATIONS: ARTISTIC EFFECTS

    Artistic effects are used to make images more visually appealing, to add special effects and to make composite images

    3/4/2019 DIGITAL IMAGE PROCESSING 137

  • MORPHING

    3/4/2019 DIGITAL IMAGE PROCESSING 138

  • 3/4/2019 DIGITAL IMAGE PROCESSING 139

  • THE END OF LECTURE 1

    3/4/2019 DIGITAL IMAGE PROCESSING 140

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