Resource Efficient Mountainous Skyline Extraction using Shallow Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU140739" target="_blank" >RIV/00216305:26230/21:PU140739 - isvavai.cz</a>

Result on the web

<a href="http://cadik.posvete.cz/papers/IJCNN21_Skyline_Final.pdf" target="_blank" >http://cadik.posvete.cz/papers/IJCNN21_Skyline_Final.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN52387.2021.9533859" target="_blank" >10.1109/IJCNN52387.2021.9533859</a>

Result language

angličtina

Original language name

Resource Efficient Mountainous Skyline Extraction using Shallow Learning

Original language description

Skyline plays a  pivotal role in mountainous visual geo-localization and localization/navigation of planetary rovers/UAVs and virtual/augmented reality applications. We present a  novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions. Unlike earlier approaches, which either rely on extraction of explicit feature descriptors and their classification, or fine-tuning general scene parsing deep networks for sky segmentation, our approach learns linear filters based on local structure analysis. At test time, for every candidate edge pixel, a single filter is chosen from the set of learned filters based on pixels structure tensor, and then applied to the patch around it. We then employ dynamic programming to solve the shortest path problem for the resultant multistage graph to get the sky-mountain boundary. The proposed approach is computationally faster than earlier methods while providing comparable performance and is more suitable for resource constrained platforms e.g., mobile devices, planetary rovers and UAVs. We compare our proposed approach against earlier skyline detection methods using four different data sets. Our code is available at https://github.com/TouqeerAhmad/skylinedetection

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LTAIZ19004" target="_blank" >LTAIZ19004: Deep-Learning Approach to Topographical Image Analysis</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the International Joint Conference on Neural Networks (IJCNN)

ISBN

978-1-6654-3900-8

ISSN

—

e-ISSN

—

Number of pages

9

Pages from-to

1-9

Publisher name

Institute of Electrical and Electronics Engineers

Place of publication

Hoffman Estates

Event location

USA

Event date

Jul 18, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000722581704065