Sketch Recognition Algorithms for Comparing Complex and Unpredictable Shapes

Sketch Recognition Algorithms for

Comparing Complex and Unpredictable Shapes

Howdy!

             I would like to talk a little bit about the above paper and state my opinion on it.

             The paper talks about a sketch- recognition system Mechanix which can be used to teach statics problems. The system recognized the gesture drawn as a truss or a free body diagram and then recognizes the mechanics behind it. It allows students to draw diagrams as if they are drawing it with pen and paper and then gives them feedback by comparing the student's drawing with the correct one. This helps professors and  TAs teaching classes with a lot of students.

The system uses 3 metrics to compare the user's response with the instructor's stored correct answer: Hausdorff distance, modified Hausdorff distance and Tanimoto co- efficient. Hausdorff distance between two shapes A and B is:

where Pa is the set of points of A and Pb is the set of points of B. Similarly, Db is calculated and the Hausdorff Distance is:

The modified Hausdorff distance is:

From the Hausdorff distance, the probability of the two shaped being similar is calculated as follows:

The constant 20 is chosen as half of the side- length of the bounding box. If P is less than zero, it means that the shapes are really dissimilar and hence it can be treated as a zero probability.

The Tanimoto coefficient is calculated as follows:

nAB is the number of points common to A and B and nA and nB are the number of points in A and B respectively. 

The paper discusses about the methods that they use for truss identification and free body identification. Identifying those two shapes is very important as they form the main component in any such diagram.

Their experiments and results suggest that students like the Mechanix system and they find it helpful. I personally think that this is a very cool design as the instructors can leverage it to compensate for the lack of individual attention to students in huge classes. 

I used the following sources for this blog post:

[1] Proceeding IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three. Pages 2436-2441. AAAI Press ©2011

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