Digital Image Processing DIP Lecture 5 Dr Abdul

Digital Image Processing (DIP) Lecture # 5 Dr. Abdul Basit Siddiqui Assistant Professor-FURC-BCSE 7 1

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 FURC-BCSE 7 2

Image Processing Steps FURC-BCSE 7 3

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 FURC-BCSE 7 4

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 FURC-BCSE 7 5

Image Representation • Image – Two-dimensional function f(x, y) – x, y : spatial coordinates • Value of f : Intensity or gray level FURC-BCSE 7 6

Digital Image • A set of pixels (picture elements, pels) • Pixel means – pixel coordinate – pixel value – or both • Both coordinates and value are discrete FURC-BCSE 7 7

Example • 640 x 480 8 -bit image FURC-BCSE 7 8

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Digital Image Processing (DIP) Digital Image Fundamentals and Image Acquisition FURC-BCSE 7 10

Image Acquisition FURC-BCSE 7 11

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 FURC-BCSE 7 12

Sampling and Quantization FURC-BCSE 7 13

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: 640 x 480, 1024 x 1024, up to 4064 x 2704 Digital video cameras: 640 x 480 at 30 frames/second 1920 x 1080 at 60 f/s (HDTV) FURC-BCSE 7 14

Sampling and Quantization Digital image is expressed as FURC-BCSE 7 15

Sampling FURC-BCSE 7 16

Effect of Sampling and Quantization FURC-BCSE 7 17

RGB (color) Images FURC-BCSE 7 18

Image Acquisition FURC-BCSE 7 19

Basic Relationships between Pixels FURC-BCSE 7 20

Basic Relationships between Pixels FURC-BCSE 7 21

Basic Relationships between Pixels FURC-BCSE 7 22

Basic Relationships between Pixels FURC-BCSE 7 23

Distance Measures Chessboard distance between p and q: FURC-BCSE 7 24

Distance Measures • D 4 distance (city-block distance): – D 4(p, q) = |x-s| + |y-t| – forms a diamond centered at (x, y) – e. g. pixels with D 4≤ 2 from p D 4 = 1 are the 4 -neighbors of p FURC-BCSE 7 25

Distance Measures • D 8 distance (chessboard distance): – D 8(p, q) = max(|x-s|, |y-t|) – Forms a square centered at p – e. g. pixels with D 8≤ 2 from p D 8 = 1 are the 8 -neighbors of p FURC-BCSE 7 26