i(x,y)

Report
Digital Image Processing (DIP)
Lecture # 5
Dr. Abdul Basit Siddiqui
Assistant Professor-FURC
FURC-BCSE7
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Classification of DIP and Computer Vision Processes

Low-Level Process: (DIP)
– Primitive operations where inputs and outputs are images; major
functions: image pre-processing like noise reduction, contrast
enhancement, image sharpening, etc.

Mid-Level Process (DIP and Computer Vision)
– Inputs are images, outputs are attributes (e.g., edges); major
functions: segmentation, description, classification / recognition of
objects

High-Level Process (Computer Vision)
– Make sense of an ensemble of recognized objects; perform the
cognitive functions normally associated with vision
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Image Processing Steps
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DIP Course


Digital Image Fundamentals and Image Acquisition
(briefly)
Image Enhancement in Spatial Domain
– Pixel operations
– Histogram processing
– Filtering

Image Enhancement in Frequency Domain
– Transformation and reverse transformation
– Frequency domain filters
– Homomorphic filtering

Image Restoration
– Noise reduction techniques
– Geometric transformations
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DIP Course

Wavelets and Multi-Resolution Processing
– Multi-resolution expansion
– Wavelet transforms, etc.

Image Segmentation
– Edge, point and boundary detection
– Thresholding
– Region based segmentation, etc
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Image Representation
• Image
– Two-dimensional function f(x,y)
– x, y : spatial coordinates
• Value of f : Intensity or gray level
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Digital Image
• A set of pixels (picture elements, pels)
• Pixel means
– pixel coordinate
– pixel value
– or both
• Both coordinates and value are discrete
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Example
• 640 x 480 8-bit image
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Digital Image Processing (DIP)
Digital Image Fundamentals and
Image Acquisition
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Image Acquisition
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Image Description
f (x,y): intensity/brightness of the image at spatial coordinates
(x,y)
0< f (x,y)<∞ and determined by 2 factors:
illumination component i(x,y): amount of source light
incident
reflectance component r(x,y): amount of light reflected by
objects
f (x,y) = i(x,y)r(x,y)
Where 0< i(x,y)<∞: determined by the light source
0< r(x,y)<1: determined by the characteristics of objects
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Sampling and Quantization
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Sampling and Quantization
Sampling:
Digitization of the spatial coordinates (x,y)
Quantization: Digitization in amplitude (also called gray-level
quantization)
8 bit quantization: 28 =256 gray levels (0: black, 255: white)
Binary (1 bit quantization):2 gray levels (0: black, 1: white)
Commonly used number of samples (resolution)
Digital still cameras: 640x480, 1024x1024, up to 4064 x 2704
Digital video cameras: 640x480 at 30 frames/second 1920x1080 at
60 f/s (HDTV)
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Sampling and Quantization
Digital image is expressed as
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Sampling
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Effect of Sampling and Quantization
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RGB (color) Images
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Image Acquisition
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Basic Relationships between Pixels
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Basic Relationships between Pixels
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Basic Relationships between Pixels
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Basic Relationships between Pixels
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Distance Measures
Chessboard distance between p and q:
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Distance Measures
• D4 distance (city-block distance):
– D4(p,q) = |x-s| + |y-t|
– forms a diamond centered at (x,y)
– e.g. pixels with D4≤2 from p
2
2 1 2
2 1 0 1 2
D4 = 1 are the 4-neighbors of p
2 1 2
2
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Distance Measures
• D8 distance (chessboard distance):
– D8(p,q) = max(|x-s|,|y-t|)
– Forms a square centered at p
– e.g. pixels with D8≤2 from p
2 2 2 2 2
2 1 1 1 2
D8 = 1 are the 8-neighbors of p
2 1 0 1 2
2 1 1 1 2
2 2 2 2 2
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