Reading #9 PaleoSketch: Accurate Primitive sketch Recognition and Beautification

Comment:
Chris
Summary:
Paleosketch is the primitive sketch recognition library, which is widely used in many projects. In fact, for sketch recognition, the first very begining task is to recognize primities as line, polyline, arc, and etc. For most of sketching system, the first step is to recognize each stroke as primitives, then hierarchyly builds more complex system by using botton-up approach. Thus, the primitive recognition the most important steps for sketch recognition, and greatly affect the later processing and recognition and overall accuracy of sketching system. Paleosketch is an accurate, configurable primitive recognition library, that can recognize each stroke as eight different primitives, line, polyline, circle, ellipse,arc curve,spiral and helix. PaleoSketch starts by preprocessing step that remove noise of strokes. In the implementation part, the author shows most disdingushiable features for each primitve recognition. Author shows that two most importatn contributuion of this paper is to use two new important features, NDDE and DCR respectively. These two features are very useful for differenciate polyline and curve. Anther important contribution of this paper is new ranking algorithm as mentioned in this paper. In the remaining part of paper, the author compares different versions of PaleoSketch as well as with SSD, the result shows that Paleo has very good recognition accuracy.


Discussion:
PaleoSketch is accurate and an important contribution to the Sketch Recognition community. It uses very good features to recognize differrent primitives. We can easily configure PaleoSketch based on our own needs and requriement of specific system. However, in my opition, it stills needs improvement. It acctually uses fixed threshold for all the features. This cause one problem, for recognizing Polyline, if part of these lines are not passed polyline test, it will failed and instead it is likely to recognize it as circle. Thus, my idea is instead of using fixed threshold for each part, we can make it flexible by using interval threshold. when we recognize certain stroke, we can calculate the sum of each part of values and then use the some distribution of this sum value. In this case, we can solve this problem : Certain part of stroke failed to pass the test, but from overall perspective, the whole part can still pass the test.