Citation
T. M. Sezgin, T. Stahovich, and R. Davis, “Sketch Based Interfaces : Early Processing for Sketch Understanding.”
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.8291
There are lot of inferences that can be adapted from real life to solve the technical problems. e.g. While driving, we normally get slow at the corners. The same inference can actually be observed while we tend to sketch things. Sezgin et al. (and Stahowich) used this inference and came up with this paper where he used the sketching Speed and Curvature to figure out the corners in a sketch.
For any sketch, he derived the Curvature Graph and the Speed Graph. The idea behind this was that while we are at corners, the curvature graph would have a spike (local Maxima) and on the other hand, the speed graph would have a local Minima. So, all the speed values below a certain threshold and all the curvature values beyond an empirically chosen threshold would correspond to the corners. For the first step, the authors proved that these two graphs, when used in isolation, don't give the correct corners as we may end up considering the non-corners as corners due to wrong threshold values and noise. So, they used the results from both the graphs and merged the corners.
To correctly determine the correct corners among the merged set, they introduced a new concept called ODSQ (Orthogonal Distance SQuared). This method involved starting with two initial end-points and computing ODSQ by adding a corner each from the set. If adding a corner gives a ODSQ value much below a threshold, the point is rejected from the list of potential corner. For curved regions, they used the concept of Bezier curves.
Thoughts:
The concept of using Speed and Curvature while drawing sketches is pretty novel and clever. This also underlines the fact that solutions to many problems can be adapted from real-life instances. These two features can be used by any Corner finder as they tend to give good results.
Future Ideas and Research:
We can think of deriving more such ideas from real life examples. As discussed in the class, sound can be one such feature. We can also look at the relative density of points collected. Since we tend to slow down at corner, the relative density of points at corners will be high as compared to the normal points. Although this idea is not fool-proof as user may intentionally/unintentionally slow down at non-corners. However, this feature can be tried in conjunction with the set of other features to overcome this problem.
