Computer Vision Lecture 1: Course Overview and Convolution - Prof. Marshall Tappen, Study Guides, Projects, Research of Computer Science

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CAP 5415 – Computer Vision Marshall Tappen Fall 2009 Lecture 1

Welcome!

 About Me − (Relatively) New Faculty Member (Three Years) − (^) Interested in Machine Vision and Machine Learning − (^) Happy to chat with you at almost any time  (^) May want to e-mail me first − Office Hours:  (^) Tuesday-Thursday after class

Doing the problems

 Finishing the problem sets will require access to an interpreted environment − (^) MATLAB − Octave − Numerical Python  NO COMPILED LANGUAGES!!!!! − (^) No C/C++ − (^) No Java − No x86 Assembler  My Compiled Languages Rant

Environments

 MATLAB − Pro:Well-established package. You can find many tutorials on the net. − Con: Not free. If your lab does not already have it, talk to me about getting access.  Octave − Free MATLAB look-alike − (^) Pro: Should be able to handle anything you will do in this class − Con: “Should be”. I'm not sure about support in Windows

Math

 We will use it  We will be talking about mathematical models of images and image formation  This class is not about proving theorems  My goal is to have you build intuitions about the models  Try and visualize the computation that each equation is expressing  Basic Calculus and Basic Linear Algebra should be sufficient

Course Text

 We will use Szeliski Book – Free this year!  Not required – more of a reference

Image Processing

 For now, we won't worry about the physical aspects of getting images  View image as an array of continuous values

Simple Modification

 What if we wanted to blur this image?  We could take a local average − Replace each pixel with the mean of an NxN pixel neighborhood surrounding that pixel.

5x5 Neighborhood

Original Averaged

7x7 Neighborhood

Original Averaged

Let's represent this more generally

Input Image Kernel

Let's represent this more generally

 Multiply corresponding numbers and add

Input Image

This is called convolution

 Mathematically expressed as Resulting Image Input Image Convolution Kernel

Take out a piece of paper

 Let’s say i = 10 and j=  Which location in K is multiplied by I (5,5)?  I (5,4) Resulting Image Input Image Convolution Kernel