Talk with Prof. Jana about SIFT features

SIFT features have two parts: location and scale. (Figure out the details about them later).

Now the idea is trying to replace the scale part, when combining with the depth data. How to decide the scale selection mechanism consistent to depth data.
Take a reference to the NYU paper presented by Reza, which takes three different scales.

See the SIFT tutorial in vlfeat.org. Think about how to customize the sift: location, scale and orientation.

About the surfel:
Know how to get the surfel. Not go into the render part.

GICP algorithm developed by Alex,
search how to run it on MATLAB platform.
run script shell from matlab.

write data to png file...NOT SOLVED

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/