Quantitative Performance Analysis of Hybrid Mesh Segmentation

Vaibhav J. Hase, Yogesh J. Bhalerao, Mahesh P. Nagarkar, Sandip N. Jadhav

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Abstract

This paper presents a comprehensive quantitative performance analysis of hybrid mesh segmentation algorithm. An important contribution of this proposed hybrid mesh segmentation algorithm is that it clusters facets using “facet area” as a novel mesh attribute. The method does not require to set any critical parameters for segmentation. The performance of the proposed algorithm is evaluated by comparing the proposed algorithm with the recently developed state-of-the-art algorithms in terms of coverage, time complexity, and accuracy. The experimentation results on various benchmark test cases demonstrate that Hybrid Mesh Segmentation approach does not depend on complex attributes, and outperforms the existing state-of-the-art algorithms. The simulation reveals that Hybrid Mesh Segmentation achieves a promising performance with coverage of more than 95%.

Original languageEnglish
Title of host publication2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2019
EditorsAnandakumar Haldorai, Arulmurugan Ramu, Sudha Mohanram, Mu-Yen Chen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages115-141
Number of pages27
ISBN (Print)9783030475598
DOIs
Publication statusPublished - 1 Jan 2021
Event2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2019 - Coimbatore, India
Duration: 12 Dec 201913 Dec 2019

Publication series

NameEAI/Springer Innovations in Communication and Computing
ISSN (Print)2522-8595
ISSN (Electronic)2522-8609

Conference

Conference2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2019
CountryIndia
CityCoimbatore
Period12/12/1913/12/19

Keywords

  • CAD mesh model
  • Coverage
  • Feature recognition
  • Hybrid mesh segmentation
  • Interacting features

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