Matching Image Features (Chapter 3): Computer Vision Training for Student Competition Teams
You are now following this Submission
- You will see updates in your followed content feed
- You may receive emails, depending on your communication preferences
Learn to detect features using a variety of detectors. We will then show how to match features between images.
Features are points or areas of unique content such as corners or blobs. The Computer Vision System Toolbox™ provides the FAST, Harris, and Shi & Tomasi methods for detecting corner features, and the SURF and MSER methods for detecting blob features.
Features are used in two fundamental ways:
* To localize anchor points for use in image stitching, 3-D reconstruction, and stereovision rectification.
* To represent image contents compactly for object detection, recognition or tracking, without requiring image segmentation.
By learning feature detection and matching, you’ll have the fundamental concepts necessary to perform basic object detection and recognition with features.
Cite As
MathWorks Student Competitions Team (2026). Computer Vision for Student Competitions: Matching Image Features (https://github.com/mathworks/auvsi-cv-feature), GitHub. Retrieved .
General Information
- Version 15.1.1.0 (4.28 MB)
-
View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 15.1.1.0 | Description Updated
|
