Authors:
Jaspinder Kaur
;
Tyler Laforet
and
Pascal Matsakis
Affiliation:
Department of Computer Science, University of Guelph, Guelph, Canada
Keyword(s):
Relative Position Descriptor, Computer Vision, Image Processing, Force Histogram.
Abstract:
The force histogram is a quantitative representation of the relative position between two objects. Two practical algorithms have been previously introduced to compute the force histogram between objects: the line-based algorithm (which works well with 2D data, but is computationally unstable in the case of 3D data), and the Fast Fourier Transform (FFT)-based algorithm (which is inefficient in the case of 2D data, but has not been implemented for 3D data). In this paper, an efficient FFT-based algorithm for force histogram computation in the case of 3D raster data is introduced. Its computation time is compared against that of the 3D line-based algorithm; except in a few cases, the computation time for new FFT-based algorithm is less than that of 3D line-based algorithm. The experiments validate that the FFT-based algorithm is computationally efficient regardless of the number of directions, type of forces, and shape of the objects (convex, concave, disjoint or overlapping).