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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.

www.ImageProcessingPlace.com

© 1992–2008 R. C. Gonzalez & R. E. Woods

Gonzalez & Woods

Chapter 1Introduction


Lecture 1-2




Gonzalez & Woods



• An image is a 2-D function f(x,y):– x, y: spatial coordinates

– f: intensity / grey level

– f(x,y): Pixel

• If x, y and f are discrete: Digital Image– Digitization of x, y: Spatial Sampling

– Digitization of x, y: Quantization

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• If f(x, y) is:– 0 / 1: Binary Image

– [0, 255]: Gray Scale B/W Image

– <[0, 255], [0, 255], [0, 255]>: Color or Multi-spectral Image




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• What is image processing?– Input is image; output is image

– Input is image; output is features

• DIP is processing of digital images by digital computers

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Bartlane System, 1920

Trans-Atlantic Transmission

1921: Five gray levels1929: Fifteen gray levels




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Bartlane System, 1920

Fifteen gray levels here

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Gonzalez & Woods



• Digital Computers– 1948: Transistor, Bell Labs

– 1950’s, 1960’s: High-Level Languages

– 1958: IC, TI

– Early 1960’s: OS

– Early 1970’s: Microprocessors, Intel

– 1980’s /1990’s: VLSI / ULSI

– Advances in Mass Storage / Display System

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• Digital Image Processing– 1964: Space Probe, Jet Propulsion Laboratory

– 1960’s / 1970’s: • Medical Imaging

• Remote Sensing

• Astronomy

– Early 1970’s: CAT (Computerized Axial Tomography) or CT




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• DIP relates deeply to other areas– Pattern Recognition

– Computer Vision

– Artificial Intelligence

– Computer Graphics

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• Vision: Most important human perception– Limited to Visual Band of EM Spectrum

• DIP applies beyond visual:– Gamma Rays to Radio Waves

– Ultra-sound, Electron Microscopy, …

– Synthetic Images – Visualized Information




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• Examples of Fields that use Digital Image Processing– Gamma-Ray Imaging

– X-Ray Imaging

– Imaging in UV Band

– Imaging in Visible & IR Bands

– Imaging in Microwave Band

– Imaging in Radio Band

– Examples where other Imaging Methods are used

Source: Chapter 01 of DIP, 3E:Introduction




Gonzalez & Woods



• Three types of Computer Processes:– Low-level

• Noise reduction, Contrast Enhancement, Image Sharpening

• I/P & O/P: Both images

– Mid-level• Segmentation / Object Description / Recognition

• I/P: Images, O/P: Attributed Entities

– High-level• Interpretation, ‘Making Sense’, …

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• Example: Automated Analysis of Document – Text Processing:

• Pages, Columns, Paragraphs, etc

– Graphical Processing• Non-textual elements: Tables, lines, images, symbols,

delimiters, logo, pictures, etc

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• Example: Automated Analysis of Document – Acquisition: Scanning




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Binarization

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Skew / Tilt Correction




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• Example: Automated Analysis of Document – Pre-Processing:

• Noise Cleaning,

• Binarization,

• Tilt Correction

• Page Alignment

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Region Segmentation




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Region Segmentation

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• Example: Automated Analysis of Document – Segmentation:

• Region / Zone Column Paragraph Line Word Character / Stroke

• Text & Non-Text (Image)




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• Example: Automated Analysis of Document – Feature Extraction:

• Bounding Box,

• Curvature,

• Length,

• Orientation,

• Moment, …

– Classification:• Statistical

• Syntactic

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• Example: Automated Analysis of Document – Recognition:

• Character

• Word Formation

• Dictionary Validation

– Perceptual Map• Words to regions

• Captions to Photos

• Zone Marking

• …

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Lecture 2




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Lecture 1 in brief

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• An image is a 2-D function f(x,y):

– x, y: spatial coordinates

– f: intensity / grey level

– f(x,y): Pixel

• If x, y and f are discrete: Digital Image

– Digitization of x, y: Spatial Sampling

– Digitization of x, y: Quantization

• If f(x, y) is:

– 0 / 1: Binary Image

– [0, 255]: Gray Scale B/W Image

– <[0, 255], [0, 255], [0, 255]>: Color or Multi-spectral Image




Gonzalez & Woods



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• Three types of Computer Processes:

– Low-level

• Noise reduction, Contrast Enhancement, Image Sharpening

• I/P & O/P: Both images

– Mid-level

• Segmentation / Object Description / Recognition

• I/P: Images, O/P: Attributed Entities

– High-level

• Interpretation, ‘Making Sense’, …




Gonzalez & Woods



Skew / Tilt Correction

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Optical Mark Recognition (OMR): Bubble Sheet Processing




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Example: OMR: Bubble Sheet Processing

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Example: OMR: Bubble Sheet Processing




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• Example: OMR– Acquisition: Scanning

– Pre-Processing: Cleaning, Binarization, Alignment

– Segmentation: Row / Column Identification

– Feature Extraction: Moments

– Object Recognition: Blob Detection

– Interpretation: Mapping of Marks, Validation

– Making Sense: Answer Construction & Correction

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Lecture 5




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• Energy Sources for Images– EM Energy Spectrum

– Acoustic

– Ultrasound

– Electronic

– Synthetic

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Gonzalez & Woods




– X-Ray Imaging







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• Gamma Ray Imaging– Nuclear Medicine (Bone Scan, Positron Emission

Tomography (PET))

– Astronomical Observations




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• X-Ray Imaging– Medical Diagnosis

• Bone X-Ray

• Angiography

• Computerized Axial Tomography (CAT)

– Industrial Scanning & Testing

– Astronomy




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• Imaging in Ultra-Violet Band– Lithography

– Industrial Inspection

– Microscopy (Fluorescence)

– Lasers

– Biological Imaging

– Astronomical Observations




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• Imaging in the Visible and Infrared Bands– Light Microscopy

– Remote Sensing

– Weather Observation / Prediction

– Automated Visual Inspection

– Finger Printing




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1993 2003




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• Imaging in the Microwave Bands– Radar

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Gonzalez & Woods



• Imaging in the Radio Bands– Medicine: MRI

– Astronomy

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– X-Ray Imaging







Gamma

X-ray

Ultra Violet

Infra ray

Micro wave Radio




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• EM– Gamma Ray Imaging

– X-Ray Imaging

– Imaging in Ultra-Violet Band

– Imaging in the Visible and Infrared Bands

– Imaging in the Microwave Bands

– Imaging in the Radio Bands

• Non EM– Acoustic

– Ultrasound

– Electronic

– Synthetic

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l01 02 introduction

Documents

ctdigital image processing

image output

image input

binary image

woodsgonzalez woodschapter

digital images

gray scale bw image

introduction digital