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A Multi-scale Line Feature Detection Using Second Order Semi-Gaussian Filters

Abstract : Among the common image structures, line feature is the extensively used geometric structure for various image processing applications, including the analysis of biomedical image with blood vessels highlighting, graph-shape structures, cracks detection, satellite images or remote sensing data. Multi-scale processing of line feature is essentially required for the extraction of more relevant information or line structures of heterogeneous widths. In this paper, a multi-scale filtering-based line detection approach using second-order semi-Gaussian anisotropic kernel is proposed. Meanwhile, a strategy is introduced to calculate the strength of the observed line feature across the different scales. The proposed technique is evaluated on real images by using their tied hand-labeled images. Finally, the experimental results and comparison of images containing different line feature widths with state-of-the-art techniques have sufficiently supported the effectiveness of our technique.
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https://hal.mines-ales.fr/hal-03413881
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Submitted on : Wednesday, November 24, 2021 - 4:35:34 PM
Last modification on : Monday, December 6, 2021 - 10:30:24 AM
Long-term archiving on: : Friday, February 25, 2022 - 7:40:11 PM

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Baptiste Magnier, Ghulam-Sakhi Shokouh, Binbin Xu, Philippe Montesinos. A Multi-scale Line Feature Detection Using Second Order Semi-Gaussian Filters. CAIP 2021 - The 19th International Conference on Computer Analysis of Images and Patterns, Sep 2021, Virtual Conference, France. pp.98-108, ⟨10.1007/978-3-030-89131-2_9⟩. ⟨hal-03413881⟩

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