Reading #6 Protractor: An enhanced $1 using angular distance

Reading #6 Protractor: An enhanced $1 using optimal angular distance

Comments on: 
Jianjie Zhang

Summary:

Li designed Protractor using a angular distance to calculate the similarity between candidates and templates. It is essentially similar with $1, both of them are template based recognizer. Compared with parametric recognizer(like Rubine’s method), template based recognizer  (1) does not need many training data; (2) do not have to choose features(which is not easy) to represent the parametric  model, therefore can be easily customized as long as the user provided related samples. However, template recognize is time and space consuming caused by many comparison needed.

In spite of the similarity, Protractor has many differences from $1:

(1)    Protractor can be orientation-sensitive, it rotate the stroke to one of the 8 base orientations which require minimum rotation.

(2)    Choose N=16 instead of N=64 to improve the computing speed and decreases the storage needed.

(3)    Using optimal angular distance rather than Euclidian distances to compute of similarity from certain template.

(4)    Because the size of stroke is irrelevant to compute angular distance, Protractor does not resize the stroke.

(5)    As for searching the optimal rotation, Protractor uses a closed-form solution to compute the optimal rotation rather than the time-consuming iterative approach used in $1 recognizer.

Discussion:

Protractor is a modification of $1 in many details, the biggest change is that it use angular distance rather than Euclidean distance. He also shows, when the critical distance computing method is changed, the supplementary processing of the strokes may also changes. I think, on the other side, the insight of how to preprocessing of the stroke can also lead to some new ways to compute the distances.  

 As indicated by the author, parametric recognizer is based on a parametric model and it is not easy to find a great model to include all the aimed gesture and exclude unrelated gestures.  Does it mean that this kind of method is not promising along with the improvement of computer speed and storage?