Reading #9. PaleoSketch: Acurate Primitive Sketch Recognition and Beautification (Paulson)

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Kim

Summary

PaleoSketch is a primitive shape recognizer for free sketches. Unlike feature based sketches the user is allowed to draw freely. The intention of the recognizer is to match each single stroke to basic primitive shape (Line, polyLine, Circle, Ellipse, Arc, Curve, Spiral, Helix). The main contributions are:

• Extend the set of recognizable shapes particularly by differentiating ellipses from circles and arcs from curves, and also by recognizing more complex shapes as spirals and helixes.
• Support for overtraded sketches.
• The introduction of 2 new features (Normalized Distance between direction extremes NDDE and Direction Change Ratio DCR)
• Ranking algorithm to find the best fit for each shape.

The authors base the algorithm on a combination of previous methods, particularly in the pre-recognition stage, where some processing is made to the stroke and several features are calculated. Then several fit tests are made for each primitive and “interpretations” of the stroke are attached with their correspondent confidence. Finally the best-Fit is found with the ranking algorithm. The results show almost perfect accuracy and a real-time response time. Some examples are shown of higher level work built over paleo leaving space for promising future work.

Discussion

Unlike previous papers we now can discuss this work not only as article readers but as paleo users. After assignment No.1 we can talk with more confidence about paleo. The response time is in fact fast enough for most if not all practical purposes. And the accuracy of recognition works very, very well. Also the version of paleo that we are using that features an extended shape set (e.g. arrows) and the work we have done so far with the assignment along with other work such as mekanix show that definitively paleo is a very useful tool for higher level recognition. One thing I would like to know more about is the origin of the threshold values (a.k.a Magic Numbers). But most likely they are just as “magic” as the ones many of us are using in our homework. Another thing I found in this paper, and in general in most of the recognition papers, is the use of the term “recognition rate” as a quantitative form of qualifying the algorithm. And I am aware that this is common use, but even as paleo is the best free-sketch recognizer I have seen some far, either I am very unlucky or paleo does not really recognize 98.56% of the sketches in a general form. And I am guessing that the data was taken in a rather controlled environment.  But as I said before this is an issue all of the recognition papers usually have. So I wonder if the recognition rate is really a meaningful number without a universally standardized dataset.