Fast Stroke Matching by Angle Quantization

Determining similarity of two point sequences (strokes) is a fundamental task in gestural interfaces. Because the length of each stroke is arbitrary, mapping to a fixed-dimension feature space is often done to allow for direct comparison. In this paper, we propose a new feature space based on angle quantization. For each adjacent pair of points in a stroke, the vector between them defines an angle relative to a fixed axis. The sequence of these angles can be mapped to a kdimensional feature space by quantizing the unit circle into k ranges, and taking a normalized count of the number of stroke angles in each range. The Euclidean distance between strokes in this feature space gives a measure of stroke similarity. The measure is scale invariant, and some degree of rotational invariance can be achieved with slight modification. Our method is shown to offer efficient and accurate gestural matching performance compared to traditional signal-processing and image-based methods.