Difference between revisions of "Directionality Analysis"
From ISLAB/CAISR
| Line 12: | Line 12: | ||
* Part 1 dedicated to the extraction of edges, corners, lines and circles from images | * Part 1 dedicated to the extraction of edges, corners, lines and circles from images | ||
* Part 2 dedicated to the detection of symmetric patterns via the Generalized Structure Tensor | * Part 2 dedicated to the detection of symmetric patterns via the Generalized Structure Tensor | ||
| + | |||
| + | In part 1, we will describe the most used algorithms for edges, corners, lines and circles extraction from images. | ||
| + | |||
| + | The extraction of edges is the first step of many Computer Vision algorithms. Other segments such as corners, lines or circles are defined thanks to the presence of edges. These geometrical shapes allows to go higher in the analysis of objects and scenes. | ||
| + | |||
| + | [[Image:Vision-reasoning-levels.jpg|none|300px]] | ||
| + | |||
Revision as of 18:38, 7 January 2015
| |
| Directionality Analysis | |
|---|---|
| Contact: Fernando Alonso-Fernandez |
Welcome to this lecture on Directionality Analysis.
This lecture is divided into two parts:
- Part 1 dedicated to the extraction of edges, corners, lines and circles from images
- Part 2 dedicated to the detection of symmetric patterns via the Generalized Structure Tensor
In part 1, we will describe the most used algorithms for edges, corners, lines and circles extraction from images.
The extraction of edges is the first step of many Computer Vision algorithms. Other segments such as corners, lines or circles are defined thanks to the presence of edges. These geometrical shapes allows to go higher in the analysis of objects and scenes.
Contents
Part 1: Edges, corners, lines, circles
Teaching Material
Get slides from Google docs (ppt) here
References and sources
R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2010
|
Simon J.D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012
|
| R. Klette, “Concise Computer Vision”, Springer, 2014 |
Part 2: Structure tensor
Teaching Material
Get slides from Google docs (ppt) here
References and sources
“Hard” references (with full mathematical description):
J. Bigun, Vision with Direction, Springer, 2006
|
| J. Bigun, T. Bigun, K.Nilsson, Recognition by Symmetry Derivatives and the Generalized Structure Tensor, IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 26, n. 12, December 2004 |
A more “soft” source with a light, introductory description is:
D. Teferi, Recognition and Evaluation by Video Synthesis Methods and Symmetry Features, PhD Thesis, Chalmers University of Technology, 2009
|
