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 Pattern Recognition: ECE471, ECE571 Computer Vision: ECE573 Computer Graphics: CS494, CS594 Advanced Topics: ECE692

Image Representation

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

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

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)

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

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) Explain like The current pixel of interest is (I,j), The origin is at the upper-left corner Then its neighbors would be … (row) 4-neighborhood 8-neighborhood

Closedness ambiguity (The adjacency paradox)

A variation: Hexagonal pixels

Image as surface - Gradient Isophote Ridge

Outline

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

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

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