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ECE 692 – Advanced Topics in Computer Vision

ECE 692 – Advanced Topics in Computer Vision. Lecture 1 - Introduction 01/14/16. Some clarification. Image & Graphics Image processing & Computer vision Image processing & Image understanding Image processing & Pattern recognition Image Processing: ECE472, ECE572

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ECE 692 – Advanced Topics in Computer Vision

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  1. ECE 692 – Advanced Topics in Computer Vision Lecture 1 - Introduction 01/14/16

  2. Some clarification • Image & Graphics • Image processing & Computer vision • Image processing & Image understanding • Image processing & Pattern recognition • Image Processing: ECE472, ECE572 • Pattern Recognition: ECE471, ECE571 • Computer Vision: ECE573 • Computer Graphics: CS494, CS594 • Advanced Topics: ECE692

  3. Image Representation

  4. y Gray level x pixel Digital image Original picture I[i, j] or I[x, y] f(x, y) What is an image? - The bitmap (iconic) representation • Also called “raster or pixel maps” representation • An image is broken up into a grid

  5. Image acquisition • Video camera • Infrared camera • Range camera • Line-scan camera • Hyperspectral camera • Omni-directional camera • and more …

  6. What is an image? - The vector representation • Object-oriented representation • Does not show information of individual pixel, but information of an object (circle, line, square, etc.) Circle(100, 20, 20) Line(xa1, ya1, xa2, ya2) Line(xb1, yb1, xb2, yb2) Line(xc1, yc1, xc2, yc2) Line(xd1, yd1, xd2, yd2)

  7. What is an image? (cont’d) • The functional representation • z = ax2+by2+cxy+dx+ey+f • The linear (vector representation) • [5 10; 6 4]  [5 10 6 4]T • The probabilistic representation (random field) • The graphical representation

  8. Types of neighborhoods • Neighbors of a pixel j (column) (i-1, j-1) (i-1, j) (i-1, j+1) (i, j-1) (i, j) (i, j+1) i (i+1, j-1) (i+1, j) (i+1, j+1) (row) 4-neighborhood 8-neighborhood

  9. Closedness ambiguity (The adjacency paradox)

  10. A variation: Hexagonal pixels

  11. Image as surface - Gradient • Gradient • Isophote • Ridge

  12. Outline

  13. Image Acquisition Image Enhancement Image Segmentation Image Restoration Representation & Description Image Compression Recognition & Interpretation Image Coding Morphological Image Processing Wavelet Analysis What has been learned? (472/572) Preprocessing – low level Image Improvement High-level IP Image Understanding Knowledge Base

  14. What to learn? (this course) • Preliminaries • Image representation and creation • Preprocessing • Kernel operators • Noise removal • Mathematical morphology • Image understanding • Segmentation • Parametric transforms • Shape • Descriptors • From 2D to 3D

  15. Objectives • In-depth study of computer vision algorithms • Study the trend and predict the future • Optimization and consistency • CVPR/WACV/ECCV submissions

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