OpenCV Installation

1. Install OpenCV with Code:Blocks
    1)Followed instruction here: http://opencv.willowgarage.com/wiki/InstallGuide
     if you download opencv 2.3.1 extract files to a folder. And in this folder there have already compiled     files   for Mingw, vs9, vs10. So you donot need to use cmake to compile again.(But these files cannot be used successfully, so you need to use CMake to compile again)
    2)configue settings for codeblocks
     http://opencv.willowgarage.com/wiki/MinGW

 after you finish compiling using cmake, please do not delete the cache, else you cannot run mingw32-make in the terminal successfully.

when I finished all above steps, I encount some problems when compiling.(All these problems are gone away when I compile by myself.)
1) libgcc_s_dw2-1.dll and libstdc++-6.dll are missing.
So, I download these two files online, and put them in the directory of compiler in codeblocks. That is, D:\Program Files\CodeBlocks\MinGW\bin (which is the path in my computer).
2) the problem always crashed when calling cvNamedWindow function


2. Install OpenCV with Devcpp

3. Install OpenCV with Microsoft Visual Studio 2008
the steps are similar to what mentioned above.
The bug frustrated me for a long time is you should put the test image correctly.
If you have problem, take a reference to the linnk below:
http://blog.csdn.net/moc062066/article/details/6676117

Meet with Prof. Jana

1. The problem I will focus on: CHANGE DETECTION.
2. Assignments:
    1) Read the paper: Toward object discovery and modeling via 3-D scene comparison
          http://www.cs.washington.edu/homes/xren/publication/herbst_icra11_scene_differencing.pdf
    2) Data set for the paper above
          http://www.cs.washington.edu/ai/Mobile_Robotics//projects/object-discovery/
    3) Machine learning class
        Search You tub lectures and look at the assignments in the syllubus, try do it by yourself.
         http://cs229.stanford.edu/schedule.html
        Link to Video Lecture: http://academicearth.org/courses/machine-learning
        

• Introduction (1 class) Basic concepts.
  
• Supervised learning. (7 classes) Supervised learning setup. LMS.
  Logistic regression. Perceptron. Exponential family.
  Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes.
  Support vector machines. (Understand it as a whole, understand the inputs and outputs, not every details at this stage)
  Model selection and feature selection.
  Ensemble methods: Bagging, boosting.
  Evaluating and debugging learning algorithms.
  
• Learning theory. (3 classes) Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.
  VC dimension. Worst case (online) learning.
  Practical advice on how to use learning algorithms.
  
• Unsupervised learning. (5 classes) Clustering. K-means.
  EM. Mixture of Gaussians.
  Factor analysis.
  PCA (Principal components analysis).
  ICA (Independent components analysis).
  
• Reinforcement learning and control. (4 classes) MDPs. Bellman equations.
  Value iteration and policy iteration.
  Linear quadratic regulation (LQR). LQG.
  Q-learning. Value function approximation.
  Policy search. Reinforce. POMDPs.

       4) Geometric Context from Single Image - software - look at the code for generating labels of regions.

          http://www.cs.illinois.edu/homes/dhoiem/