Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods

Antonio Gambale; Emmett Kerr; Dermot Kerr; Sonya Coleman

doi:10.3390/engproc2024065008

Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods

Antonio Gambale, Emmett Kerr, Dermot Kerr, Sonya Coleman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Downloads (Pure)

Abstract

This paper introduces a methodology for precise object orientation determination using Principal Component Analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with Axis-Aligned Bounding Boxes in smart manufacturing environments. The proposed approach paves the way for improved alignment in robotic grasping tasks, positioning it as a computationally efficient alternative to ML methods employing Oriented Bounding Boxes. The methodology demonstrated a maximum angle deviation of 3.5 degrees under severe noise conditions through testing with bolts in orientations of 0 to 180 degrees.

Original language	English
Title of host publication	The 39th International Manufacturing Conference
Subtitle of host publication	Smart Manufacturing: The Next Generation
Publisher	MDPI
Pages	1-2
Number of pages	2
Volume	65
Edition	1
DOIs	https://doi.org/10.3390/engproc2024065008
Publication status	Published online - 1 Mar 2024
Event	The 39th International Manufacturing Conference: Smart Manufacturing - The Next Generation - Ulster University, Magee Campus, Derry/Londonderry, Northern Ireland Duration: 24 Aug 2023 → 25 Aug 2023 https://www.manufacturingcouncil.ie/imc39-2023

Publication series

Name	Engineering Proceedings
Publisher	MDPI

Conference

Conference	The 39th International Manufacturing Conference
Abbreviated title	IMC39 2023
Country/Territory	Northern Ireland
City	Derry/Londonderry
Period	24/08/23 → 25/08/23
Internet address	https://www.manufacturingcouncil.ie/imc39-2023

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

Manufacturing
Vision
PCA
Machine Learning
vision
manufacturing
machine learning

Access to Document

10.3390/engproc2024065008

IMC-extended-abstract-Antonio-Gambale_EK-D3Final published version, 182 KBLicence: Unspecified

Cite this

Gambale, A., Kerr, E., Kerr, D., & Coleman, S. (2024). Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods. In The 39th International Manufacturing Conference: Smart Manufacturing: The Next Generation (1 ed., Vol. 65, pp. 1-2). Article 8 (Engineering Proceedings). MDPI. Advance online publication. https://doi.org/10.3390/engproc2024065008

@inproceedings{1be3d89972db48c08e0d70e7523372c3,

title = "Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods",

abstract = "This paper introduces a methodology for precise object orientation determination using Principal Component Analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with Axis-Aligned Bounding Boxes in smart manufacturing environments. The proposed approach paves the way for improved alignment in robotic grasping tasks, positioning it as a computationally efficient alternative to ML methods employing Oriented Bounding Boxes. The methodology demonstrated a maximum angle deviation of 3.5 degrees under severe noise conditions through testing with bolts in orientations of 0 to 180 degrees.",

keywords = "Manufacturing , Vision, PCA, Machine Learning, vision, manufacturing, machine learning",

author = "Antonio Gambale and Emmett Kerr and Dermot Kerr and Sonya Coleman",

note = "Publisher Copyright: {\textcopyright} 2024 by the authors.; The 39th International Manufacturing Conference : Smart Manufacturing - The Next Generation, IMC39 2023 ; Conference date: 24-08-2023 Through 25-08-2023",

year = "2024",

month = mar,

day = "1",

doi = "10.3390/engproc2024065008",

language = "English",

volume = "65",

series = "Engineering Proceedings",

publisher = "MDPI",

pages = "1--2",

booktitle = "The 39th International Manufacturing Conference",

address = "Switzerland",

edition = "1",

url = "https://www.manufacturingcouncil.ie/imc39-2023",

}

Gambale, A , Kerr, E , Kerr, D & Coleman, S 2024, Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods. in The 39th International Manufacturing Conference: Smart Manufacturing: The Next Generation . 1 edn, vol. 65, 8, Engineering Proceedings, MDPI, pp. 1-2, The 39th International Manufacturing Conference, Derry/Londonderry, Northern Ireland, 24/08/23. https://doi.org/10.3390/engproc2024065008

Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods. / Gambale, Antonio ; Kerr, Emmett ; Kerr, Dermot et al.
The 39th International Manufacturing Conference: Smart Manufacturing: The Next Generation . Vol. 65 1. ed. MDPI, 2024. p. 1-2 8 (Engineering Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods

AU - Gambale, Antonio

AU - Kerr, Emmett

AU - Kerr, Dermot

AU - Coleman, Sonya

PY - 2024/3/1

Y1 - 2024/3/1

N2 - This paper introduces a methodology for precise object orientation determination using Principal Component Analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with Axis-Aligned Bounding Boxes in smart manufacturing environments. The proposed approach paves the way for improved alignment in robotic grasping tasks, positioning it as a computationally efficient alternative to ML methods employing Oriented Bounding Boxes. The methodology demonstrated a maximum angle deviation of 3.5 degrees under severe noise conditions through testing with bolts in orientations of 0 to 180 degrees.

AB - This paper introduces a methodology for precise object orientation determination using Principal Component Analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with Axis-Aligned Bounding Boxes in smart manufacturing environments. The proposed approach paves the way for improved alignment in robotic grasping tasks, positioning it as a computationally efficient alternative to ML methods employing Oriented Bounding Boxes. The methodology demonstrated a maximum angle deviation of 3.5 degrees under severe noise conditions through testing with bolts in orientations of 0 to 180 degrees.

KW - Manufacturing

KW - Vision

KW - PCA

KW - Machine Learning

KW - vision

KW - manufacturing

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85196543628&partnerID=8YFLogxK

U2 - 10.3390/engproc2024065008

DO - 10.3390/engproc2024065008

M3 - Conference contribution

VL - 65

T3 - Engineering Proceedings

SP - 1

EP - 2

BT - The 39th International Manufacturing Conference

PB - MDPI

T2 - The 39th International Manufacturing Conference

Y2 - 24 August 2023 through 25 August 2023

ER -

Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this