Slope detection criterion robust to sparse 2D data

Dmitry Bocharov; Alexey Kroshnin; Dmitry Nikolaev

doi:10.1117/12.2586861

4 January 2021 Slope detection criterion robust to sparse 2D data

Dmitry Bocharov, Alexey Kroshnin, Dmitry Nikolaev

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116050I (2021) https://doi.org/10.1117/12.2586861
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

The study is referred to a task of 2D data slope estimation. We consider the integral projections analysis technique and a common criterion of sum of squared values (SSV) for optimal angle detection. This criterion is dependent on the density of input data and for very sparse data its efficiency significantly decreases. We propose the alternative criteria – the sum of the inversed lengths (SIL) that preserves SSV characteristics for dense data but that is much more robust for sparse input. The experiments conducted on simulated and real datasets demonstrate better quality of slope detection using the proposed criterion.

Citation Download Citation

Dmitry Bocharov, Alexey Kroshnin, and Dmitry Nikolaev "Slope detection criterion robust to sparse 2D data", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116050I (4 January 2021); https://doi.org/10.1117/12.2586861

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