Sebastian Thrun & Jana Kosecka CS223B Computer Vision, Winter 2007 Stanford CS223B Computer Vision, Winter 2007 Lecture 2b Software for Computer Vision.

Sebastian Thrun & Jana Kosecka CS223B Computer Vision, Winter 2007

Stanford CS223B Computer Vision, Winter 2007

Lecture 2b Software for Computer Vision

Professors Sebastian Thrun and Jana Kosecka

CAs: Vaibhav Vaish and David Stavens


Matlab versus OpenCV

• Extremely easy to use

• Interpreter + compiler

• Advanced graphics

• Difficult to install

• Highly Efficient

• More advanced functions

• Pre-installed in Linux FC6

• Inconsistent


Matlab


F1 - Matlab Help


Basic Matrix Operations (Demo)


Live Demo Here


A Simple Example


A Simple Example, Revisited

• Check out Image Library, many common routines available


Try “Image Toolbox Demos”


Blurring Example Demo (1)


Blurring Example Demo (2)

• More steps in Matlab Demo


Demo: Image Transformations


Live Demo Here


Matlab Code


Matlab Code


Output of edge_script.m


Figure 4: quiver


OpenCV by Gary Bradski, Intel


OpenCV: Install as rpm


OpenCV Code


Compiling+Running OpenCV


Movies in OpenCV:


Live Demo Here

Sebastian Thrun & Jana Kosecka CS223B Computer Vision, Winter 2007 Stanford CS223B Computer Vision, Winter 2007 Lecture 2b Software for Computer Vision.

Documents

jana kosecka cas

matlab slide

opencv slide

matlab demo slide

intel slide

quiver slide

rpm slide

matlab code slide