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L0 Gradient Smoothing and Bimodal Histogram Analysis: A Robust Method for Sea-sky-line Detection

Published: 10 January 2020 Publication History

Abstract

Sea-sky-line detection is an important research topic in the field of object detection and tracking on the sea. We propose an L0 gradient smoothing and bimodal histogram analysis based method to improve the robustness and accuracy of sea-sky-line detection. The proposed method mainly depends on the brightness difference between the sea region and the sky region in the image. First, we use L0 gradient smoothing to eliminate discrete noise in the image and achieve the modularity of brightness. Differing from previous methods, diagonal dividing is applied to obtain the brightness thresholds for the sky and sea regions. Then the thresholds are used for bimodal histogram analysis which helps to obtain the brightness near the sea-sky-line and narrow the detection region. After narrowing the detection region, the sea-sky-line in the image is extracted by a linear fitting method. To evaluate the performance of the proposed method, we manually construct an dataset which includes 40, 000 images taken in five scenes. Moreover, we also mark the corresponding ground-truth positions of sea-sky-line in each of the images. Extensive experiments on the dataset demonstrate that our method outperforms the state-of-the-art methods tremendously.

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  1. L0 Gradient Smoothing and Bimodal Histogram Analysis: A Robust Method for Sea-sky-line Detection

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    cover image ACM Conferences
    MMAsia '19: Proceedings of the 1st ACM International Conference on Multimedia in Asia
    December 2019
    403 pages
    ISBN:9781450368414
    DOI:10.1145/3338533
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 10 January 2020

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    Author Tags

    1. Bimodal histogram analysis
    2. L0 gradient smoothing
    3. Sea-sky-line detection

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    MMAsia '19
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    MMAsia '19: ACM Multimedia Asia
    December 15 - 18, 2019
    Beijing, China

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    MMAsia '19 Paper Acceptance Rate 59 of 204 submissions, 29%;
    Overall Acceptance Rate 59 of 204 submissions, 29%

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    Cited By

    View all
    • (2025)Automated steel corrosion detection and damage assessment using YOLOv5 algorithmShips and Offshore Structures10.1080/17445302.2025.2472487(1-16)Online publication date: 3-Mar-2025
    • (2025)Effects of Vehicle Interactions on Drivers’ Lateral Decisions during Lane Changes Based on a Visual SearchJournal of Transportation Engineering, Part A: Systems10.1061/JTEPBS.TEENG-8827151:3Online publication date: Mar-2025
    • (2021)Marine Target Detection in Noisy Infrared Images using a Hybrid Recognition AlgorithmSignal and Data Processing10.52547/jsdp.18.3.14718:3(147-160)Online publication date: 1-Dec-2021
    • (2020)Multi-Visual Feature Saliency Detection for Sea-Surface Targets through Improved Sea-Sky-Line DetectionJournal of Marine Science and Engineering10.3390/jmse81007998:10(799)Online publication date: 15-Oct-2020

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