Work of Camera Calibration (13)

I. Run part of our datasets in order to confirm which setup is going to be taken for matching:

       - The final decision is:
              1. Use MSER descriptor to locate the possible location of useful feature:
                     - a region-based method
                     - set 'RegionAreaRange' to [5 RAR*0.3] in order to maintain global information
              2. Use SURF descriptor as a description for each feature generating from last step:
                     - a blob-based method
                     - using 128 as the length of a descriptor, which considers its orientation comparing to '64'
                     - Explanation of SURF in OpenCV
              3. Use 'SAD' and 'NearestNeighborSymmetric' metric to compute to confirm similarities among
                  Kinect frames and Hand-held camera frames.
              4. This is how the descriptor looks like:

       - Part of Results in doing matching under different scaling after improvement and modification the
          computation metrics as well as parameters (Right: Kinect frame; Left: Hand-held camera)

 
II. I would like to use some values such as 'SignOfLaplacian' and 'Orientation' in my modified 
     matching process. 

       - Current process in Matching is able to make the correspondence becoming much more precise and concentrate. Yet it is for describe how similar between a Kinect grames and a Hand-held camera frame. I need additional metrics to describe how different between these frames. (Right: Kinect frame; Left: Hand-held camera)

       - Eliminate 1-to-multiple relationship in the matching results... may need to sort out

       - Check whether the following information is useful or not:

III. Adaptive method to decide the threshold for filtering out inappropriate matches instead of 

       using a fix value