Abstract
A fast mean shift algorithm for processing the image data is presented. Although it is based on the known basic principles of the original mean shift method, it improves the computational speed substantially. It is being assumed that the spatial image coordinates and range coordinates can be discretised by introducing a regular grid. Firstly, the algorithm precomputes the values of shifts at the grid points. The mean shift iterations are then carried out by making use of the grid values and trilinear interpolation. In the paper, it is shown that this can be done effectively. Measured by the order of complexity, the values at all grid points can be precomputed in the time that is equal to the time required, in the original method, for computing only one mean shift iteration for all image points. The interpolation step is computationally inexpensive. The experimental results confirming the theoretical expectations are presented. The use of the step kernel for computing the shifts (corresponding to the Epanechnikov kernel for estimating the densities), and the images with only a single value at each pixel are required.
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Sojka, E., Gaura, J., Fabián, T., Krumnikl, M. (2010). Fast Mean Shift Algorithm Based on Discretisation and Interpolation. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17688-3_38
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DOI: https://doi.org/10.1007/978-3-642-17688-3_38
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