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lecture01_new - ELEC317 Digital Image Processing...

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ELEC317 Digital Image Processing Lecture 1 Introduction Digital Image Processing Area Applications Restoration and Enhancement Space Pictures Computerized Tomography Coding Transmission – Teleconferencing Store – Land Sat Images Pattern Recognition Blood Cell Analysis Remote Sensing Robotic Vision Computer Graphics Flight Simulation Auto Body Design Special Effect in Movie Computer Graphics -- Computer generation and display of images. -- A Major Problem: Display of perspective views o f 3-D objects and scenes. Stick Figures: Hidden line elimination. Solids: Hidden surface elimination, shading. Needs: Fast algorithm, hardware. Applications: Fight simulation, auto body design, architecture and special effect in movie making CAT. 1
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Efficient Coding 1. Broadcast TV – NTSC color video signal (sampled at 10.7MHz, quantized to 256 levels Æ 85.6Mbit/sec). (a) Teleconferencing (b) RPV (Remotely Piloted Vehicles) 2. Land Sat Multispectral Images – Land sat has 4 bands (2300x3300 pels/band, 6 bits/pel Æ 2x10 8 bit/scene, 365x200 scene/year Æ 1.5x10 13 bit/yr). Image Restoration and Enhancement Restored fr ( x,y ) Degraded g ( x,y ) Ideal f(x,y ) ? Imaging System Restoration: Find ? to obtain fr as close to f as possible. Enhancement: Find ? to obtain something more suitable than f . Applications: Space Pictures – JPL Astronomy and artificial satellites Atmospheric turbulences Biomedical pictures Human Viewing Land Sat Images Machine Classification Methods: 1. Active methods: -- Coded Aperture Imaging -- Synthetic Aperture Radar 2
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2. Passive methods: -- Find ?. Degraded g ( x,y ) Ideal f ( x,y ) No Linear Memoryless Linear Noise Restore fr ( x,y ) g ( x,y ) ? Pattern Recognition Narrow Sense – Classifying a given pattern. Board Sense – Image or scene analysis. Applications: Remote Sensing – Crop classification Land use map Biomedical – Blood cell analysis Chromosome Karyotyping Industrial – Recognizing parts for automatic assembly Quality control Detecting Defects Law Enforcement – Fingerprint Classification Military – Reconnaissance information data base Approaches: Statistical decision theory Syntactic and semantic approach i). Raw image Æ Symbolic description ii). Updating 3
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Current Trend in Image Processing Research and Development I. Drawing on techniques from several disciplines, especially -- Artificial Intelligence -- Pattern Recognition -- Multidimensional Signal Processing II. Multiframe Data -- Temporal -- Multisensor III. Efficient Implementation A. General image processing computers B. Large image data base system C. Real-time hardware Two Dimensional Systems and Mathematical Preliminaries 1. Notation and Definitions 1-D continuous signals: f(x) , u(x) , s(t) 1-D discrete-time / sampled signals: u n , u [ n ] y x Continuous Image: u(x, y) , v(x, y) -- 2-D Sampled Image: u m,n , v [ m , n ] Separable Functions: A two dimensional function is separable if f ( x , y ) = f 1 ( x ) f 2 ( y ) 1 , n = 0 0, otherwise [ n ] = 1 2-Dimensional Delta Functions: ( x , y ) = ( x ) ( y ) [ m , n ] = [ m ] [ n ] 4
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From ELEC 211, +∞ = ) ( ) ( ) ( τ δ x dt t t x τ x(t) 1 ) ( lim 0 + = ε dt t Hence in 2-D case 1).
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lecture01_new - ELEC317 Digital Image Processing...

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