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TRANSCRIPT
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4th Computer Vision Workshop
February 2011
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Introduction to Image Processing
What is a digital image?
What is digital image processing?
Usage examples of digital image processing
Key stages in digital image processing
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Introduction to Image Processing
What is a digital image?
What is digital image processing?
Usage examples of digital image processing
Key stages in digital image processing
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What is a 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
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What is a Digital Image? (cont…)
Pixel values typically represent gray levels, colours, heights, opacities etc
Remember digitization implies that a digital image is an approximation of a real scene
1 pixel
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What is a Digital Image? (cont…)
Common image formats include:
1 sample per point (B&W or Grayscale)
3 samples per point (Red, Green, and Blue)
4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a. Opacity)
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Introduction to Image Processing
What is a digital image?
What is digital image processing?
Usage examples of digital image processing
Key stages in digital image processing
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What is Digital Image Processing? Digital image processing focuses on two major tasks
Improvement of pictorial information for human interpretation
Processing of image data for storage, transmission and representation for autonomous machine perception
Some argument about where image processing ends and fields such as image analysis and computer vision start
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What is Digital Image Processing? (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
Examples: Object recognition, segmentation
High Level Process
Input: Attributes Output: Understanding
Examples: Scene understanding, autonomous navigation
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Introduction to Image Processing
What is a digital image?
What is digital image processing?
Usage examples of digital image processing
Key stages in digital image processing
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Examples: Image Enhancement One of the most common uses of DIP techniques:
improve quality, remove noise etc
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denoise
Examples: Image Enhancement
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deblur
Examples: Image Enhancement
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Examples: Medicine 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
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Examples: GIS Geographic Information Systems
Digital image processing techniques are used extensively to manipulate satellite imagery
Terrain classification
…
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Examples: Law Enforcement Image processing techniques are used extensively by law enforcers
Number plate recognition for speed cameras/automated toll systems
Fingerprint recognition
Enhancement of CCTV images
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Examples: HCI Try to make human computer interfaces more natural
Face recognition
Gesture recognition
These tasks can be extremely difficult
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Examples: Object Segmentation
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Introduction to Image Processing
What is a digital image?
What is digital image processing?
Usage examples of digital image processing
Key stages in digital image processing
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Key Stages in Digital Image Processing
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Image Acquisition
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Image Enhancement
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Image Restoration
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Morphological Processing
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Segmentation
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Object Recognition
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Representation & Description
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Image Compression
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Key Stages in Digital Image Processing: Colour Image Processing
Image Acquisition
Image Restoration
Morphological Processing
Segmentation
Representation & Description
Image Enhancement
Object Recognition
Problem Domain
Colour Image Processing
Image Compression
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Thresholding Use a threshold to transforms an input image
into a binary image
Compare each input pixel value f(i, j) against the threshold, θ, and setting the output pixel value g(i, j) appropriately
0 if ( , )( , )
1 otherwise
f i jg i j
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Thresholding
Dr. Philip Tse Multimedia Coding and Processing 31
Sample black and white images
θ=141 θ=128 θ=115
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Thresholding Different images can be
generated from using different thresholds.
Higher threshold allows only fewer brighter pixels to become white.
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Thresholding Many applications in image processing
Difficult to decide the value of threshold, θ.
The threshold value may depend on the application required.
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Summary We have looked at:
What is a digital image?
What is digital image processing?
Usage examples of digital image processing
Key stages in digital image processing
Next section we will talk about OpenCV
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Introduction
Getting Started
Architecture & Modules
Sample Code
OpenCV
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Introduction
Getting Started
Architecture & Modules
Sample Code
OpenCV
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Open Source Computer Vision Library
Originally developed by Intel, currently maintained by Willow Garage
Written in C/C++
Implements Image Processing and Computer Vision algorithms
OpenCV - Introduction
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Cross-platform and extremely portable
Free!
Targeted for real-time applications
OpenCV - Introduction
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Introduction
Getting Started
Architecture & Modules
Sample Code
OpenCV
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http://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.1/
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Introduction
Getting Started
Architecture & Modules
Sample Code
OpenCV
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CvAux
Defunc areas and experimental algorithms
IPP
High Performance low-level routines if IPP is present
OpenCV – Architecture & Modules
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HighGUI
“Smart” windows
Image I/O, rendering
Processing keyboard and other events, timeouts
Trackbars
Mouse callbacks
Video I/O
OpenCV – Architecture & Modules
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Introduction
Getting Started
Architecture & Modules
Sample Code
OpenCV
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Example code: load an image from disk and display it on the screen
#include “highgui.h”
int main( int argc, char **argv )
{
IplImage *img = cvLoadImage( argv[1] );
CvNamedWindow(“Example1”, CV_WINDOW_AUTOSIZE);
cvShowImage(“Example1”, img);
cvWaitKey(0);
cvReleaseImage( &img );
cvDestroyWindow(“Example1”);
}
OpenCV – Sample Code