Progress for week 45 (2014)
From Robin
(Difference between revisions)
Line 39: | Line 39: | ||
* Compare Baldwinian and Lamarckian learning | * Compare Baldwinian and Lamarckian learning | ||
* Compare different local searches | * Compare different local searches | ||
+ | |||
+ | === Accounting === | ||
+ | |||
+ | == André == | ||
+ | === Budget === | ||
+ | * Try to find more information of where to by the omnidirectional camera | ||
+ | * Try to implement a Feature detection algorithm with openCV | ||
+ | * Borrow a Kinect an set the kinect up ready for testing | ||
+ | ** Use the kinect first as distance measure | ||
+ | ** Look into more usages of the kinect | ||
=== Accounting === | === Accounting === |
Revision as of 10:27, 5 November 2014
Contents |
Student template (copy this for your entry)
Budget
- Todo 1
- Todo 2
Accounting
- Done 1
- Done 2
Snorre
Budget
- Start to implement the NSGA-II algorithm
Accounting
Rune
Budget
- Finish PRM
- Begin looking at learning
Accounting
Stian
Budget
- Move the previous OpenCV stereo implementation into the framework.
- Extend it for some parameter experimentation.
- Gather a new training set (current one can't be run on algorithms requiring image width divisible by 16)
Accounting
Else-Line
Budget
- Look into good statistical methods for data comparison
- Continue testing parameters
- Compare Baldwinian and Lamarckian learning
- Compare different local searches
Accounting
André
Budget
- Try to find more information of where to by the omnidirectional camera
- Try to implement a Feature detection algorithm with openCV
- Borrow a Kinect an set the kinect up ready for testing
- Use the kinect first as distance measure
- Look into more usages of the kinect