Work of Camera Calibration (14)

I. Adding 2 based way to improve the matching result:
 
   1. Deal with the 'n-to-1' cases: keep the most important one
   2. Make an attention area in both Kinect frame and hand-held camera frame:
       - keep the information which is 60% of the original frame (...just use the central 70% information)
       - Reason:
             a. A person's visual attention should be at the most central part of a view (...in hand-held frame)
             b. Information closing to the boundary is not important (... in Kinect frame)  
   3. Re-Confirming the metric for similarity computation:
       - Use SAD + NearestNeighborRatio
       - SURF features in 128-length extracting from each MSER feature location

   4. Part of results in scaling cases (...basic cases): number of matches are around 20 to 90

Scaling 1: before improve

Scaling 1: after improve

Scaling 2: before improve

Scaling 2: after improve

Scaling 5: before improve

Scaling 5: after improve

    5. Part of results in negative cases: number of matches are lower than 10

 II. Part of results in changing viewpoint: 
      -  number of matches has the similar trend as shown on the top 2 cases

III. Next... Considering to introduce Color and Depth information from the RGB-D camera, i.e. Kinect camera, seeing whether make something different in distinguishing position and negative cases