digital image processing (dip) lecture # 5 dr. abdul basit siddiqui assistant professor-furc

Digital Image Processing (DIP)Lecture # 5

Dr. Abdul Basit SiddiquiAssistant Professor-FURC

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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

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 incidentreflectance 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 source0< 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 2704Digital 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:

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

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D4 = 1 are the 4-neighbors of p

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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

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D8 = 1 are the 8-neighbors of p

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