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• W3155146201 abstract "• I propose the first morphological implementation of the Hausdorff Distance (HD). Although Serra has proved that it can be computed using mathematics, he did not provide nor propose any tangible implementation. I am the first to propose a morphological implementation (there could be many) and I compare it using different paradigms (CPU and GPU) to the state-of-the-art. • The proposed algorithm is much faster than the state-of-the-art (work of Taha and Hanbury published in TPAMI) when we consider both the CPU and the GPU for the worst case and for tasks such as image registration. • I provide the most extensive evaluation of performance of the Hausdorff distance in the literature (considering different paradigms and programming languages). • When we consider the CPU, my proposal is up to 8 times faster than the Taha-Hanbury implementation for image registration and 22337 times faster for the worst case. In the case of the GPU, it is even faster than that. Hausdorff distance (HD) is a popular similarity metric used in the comparison of images or 3D volumes. Although popular, its main weakness is computing power consumption, being one of the slowest set distances. In this work, a novel, parallel and locality-oriented Hausdorff distance implementation is proposed. Novel as it is the first time in the literature that an actual algorithmic implementation using morphological dilations is proposed and thoroughly evaluated. Parallel, as it is more robust in terms of parallelization than the state-of-the-art algorithm and local as it has an intrinsic sensitivity to voxels that are closer in space. This proposal can be faster than the state-of-the-art in several practical cases such as in medical imaging registrations (up to 8 times faster on average in one of the CPU experiments) and is faster in the worst-case (up to 22337 times faster in one of the CPU experiments). Worst-case scenarios and high resolution volumes also favor the proposed approach. Throughout the work, several sequential and parallel CPU and GPU implementations are evaluated and compared." @default.
• W3155146201 created "2021-04-26" @default.
• W3155146201 creator A5041749289 @default.
• W3155146201 date "2021-09-01" @default.
• W3155146201 modified "2023-10-18" @default.
• W3155146201 title "An efficient and locality-oriented Hausdorff distance algorithm: Proposal and analysis of paradigms and implementations" @default.
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• W3155146201 doi "https://doi.org/10.1016/j.patcog.2021.107989" @default.
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