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

Antonio Gambale; Sonya Coleman; Dermot Kerr; Emmett Kerr

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

Antonio Gambale, Sonya Coleman, Dermot Kerr, Emmett Kerr

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

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
Pages	1-2
Number of pages	2
Publication status	Accepted/In press - 25 Aug 2023
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

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

D.L. Pham and T.W. Chang, "A YOLO-based real-time packaging
defect detection system," in Procedia Computer Science, vol. 217,
pp. 886-894, 2023.
C.C. Wong, M.Y. Chien, R.J. Chen, H. Aoyama, and K.Y. Wong,
"Moving object prediction and grasping system of robot
manipulator," in IEEE Access, vol. 10, pp. 20159-20172, 2022.
H. Giberti, T. Abbattista, M. Carnevale, L. Giagu, and F. Cristini, "A
methodology for flexible implementation of collaborative robots in
smart manufacturing systems," in Robotics, vol. 11, no. 1, p. 9, 2022.
K. Nguyen, N. T. Huynh, P. C. Nguyen, K. D. Nguyen, N. D. Vo,
and T. V. Nguyen, "Detecting objects from space: An evaluation of
deep-learning modern approaches," Electronics, vol. 9, no. 4, pp.
583, 2020.
M. Zand, A. Etemad, and M. Greenspan, "Oriented Bounding Boxes
for Small and Freely Rotated Objects," in IEEE Transactions on
Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art. no.
4701715, doi: 10.1109/TGRS.2021.3076050.
H. Abdi and L.J. Williams, "Principal component analysis," Wiley
interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp.
433-459, 2010.
"Image
Thresholding,"
OpenCV.
Available:
https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html.
Accessed on: July 14, 2023.
G.B.P. da Costa, W.A. Contato, T.S. Nazare, J.E. Neto, and M.
Ponti, "An empirical study on the effects of different types of noise
in image classification tasks," arXiv preprint arXiv:1609.02781,
2016.

Keywords

Manufacturing
Vision
PCA
Machine Learning

Access to Document

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

Cite this

@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",

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

note = "D.L. Pham and T.W. Chang, {"}A YOLO-based real-time packaging defect detection system,{"} in Procedia Computer Science, vol. 217, pp. 886-894, 2023. C.C. Wong, M.Y. Chien, R.J. Chen, H. Aoyama, and K.Y. Wong, {"}Moving object prediction and grasping system of robot manipulator,{"} in IEEE Access, vol. 10, pp. 20159-20172, 2022. H. Giberti, T. Abbattista, M. Carnevale, L. Giagu, and F. Cristini, {"}A methodology for flexible implementation of collaborative robots in smart manufacturing systems,{"} in Robotics, vol. 11, no. 1, p. 9, 2022. K. Nguyen, N. T. Huynh, P. C. Nguyen, K. D. Nguyen, N. D. Vo, and T. V. Nguyen, {"}Detecting objects from space: An evaluation of deep-learning modern approaches,{"} Electronics, vol. 9, no. 4, pp. 583, 2020. M. Zand, A. Etemad, and M. Greenspan, {"}Oriented Bounding Boxes for Small and Freely Rotated Objects,{"} in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art. no. 4701715, doi: 10.1109/TGRS.2021.3076050. H. Abdi and L.J. Williams, {"}Principal component analysis,{"} Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433-459, 2010. {"}Image Thresholding,{"} OpenCV. Available: https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html. Accessed on: July 14, 2023. G.B.P. da Costa, W.A. Contato, T.S. Nazare, J.E. Neto, and M. Ponti, {"}An empirical study on the effects of different types of noise in image classification tasks,{"} arXiv preprint arXiv:1609.02781, 2016.; The 39th International Manufacturing Conference : Smart Manufacturing - The Next Generation, IMC39 2023 ; Conference date: 24-08-2023 Through 25-08-2023",

year = "2023",

month = aug,

day = "25",

language = "English",

pages = "1--2",

booktitle = "The 39th International Manufacturing Conference",

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

}

Computing the Orientation of Hardware Components from Images using Traditional Computer Vision Methods. / Gambale, Antonio ; Coleman, Sonya ; Kerr, Dermot et al.
The 39th International Manufacturing Conference: Smart Manufacturing: The Next Generation . 2023. p. 1-2.

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 - Coleman, Sonya

AU - Kerr, Dermot

AU - Kerr, Emmett

N1 - D.L. Pham and T.W. Chang, "A YOLO-based real-time packaging defect detection system," in Procedia Computer Science, vol. 217, pp. 886-894, 2023. C.C. Wong, M.Y. Chien, R.J. Chen, H. Aoyama, and K.Y. Wong, "Moving object prediction and grasping system of robot manipulator," in IEEE Access, vol. 10, pp. 20159-20172, 2022. H. Giberti, T. Abbattista, M. Carnevale, L. Giagu, and F. Cristini, "A methodology for flexible implementation of collaborative robots in smart manufacturing systems," in Robotics, vol. 11, no. 1, p. 9, 2022. K. Nguyen, N. T. Huynh, P. C. Nguyen, K. D. Nguyen, N. D. Vo, and T. V. Nguyen, "Detecting objects from space: An evaluation of deep-learning modern approaches," Electronics, vol. 9, no. 4, pp. 583, 2020. M. Zand, A. Etemad, and M. Greenspan, "Oriented Bounding Boxes for Small and Freely Rotated Objects," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art. no. 4701715, doi: 10.1109/TGRS.2021.3076050. H. Abdi and L.J. Williams, "Principal component analysis," Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433-459, 2010. "Image Thresholding," OpenCV. Available: https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html. Accessed on: July 14, 2023. G.B.P. da Costa, W.A. Contato, T.S. Nazare, J.E. Neto, and M. Ponti, "An empirical study on the effects of different types of noise in image classification tasks," arXiv preprint arXiv:1609.02781, 2016.

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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

M3 - Conference contribution

SP - 1

EP - 2

BT - The 39th International Manufacturing Conference

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

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this