TY - GEN
T1 - Robotic machine vision system for defective screw Identification using Fisher's LDA Techniques
AU - Sahoo, Santosh Kumar
AU - Godi, Rakesh Kumar
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The research work dedicated for the object identification difficulty resolved by the techniques of principal component analysis (PCA) and linear discriminant analysis (LDA) along with robotic machine vision system. The effectiveness of the proposed model is carefully considered for a case when the pictures of an object like Screw have not initially processed but managed them into a standard form in terms of back-ground, scaling, positioning and adjustment of intensity or brightness. Similarly, when processed a huge amount of image data sets it is necessary to use PCA and LDA for optimizing the computational difficulty. Here using the LDA and PCI the effectiveness of the model is analyzed. By an introducing the robotic machine vision system the identification accuracy can be enhanced to a greater label.
AB - The research work dedicated for the object identification difficulty resolved by the techniques of principal component analysis (PCA) and linear discriminant analysis (LDA) along with robotic machine vision system. The effectiveness of the proposed model is carefully considered for a case when the pictures of an object like Screw have not initially processed but managed them into a standard form in terms of back-ground, scaling, positioning and adjustment of intensity or brightness. Similarly, when processed a huge amount of image data sets it is necessary to use PCA and LDA for optimizing the computational difficulty. Here using the LDA and PCI the effectiveness of the model is analyzed. By an introducing the robotic machine vision system the identification accuracy can be enhanced to a greater label.
UR - https://www.scopus.com/pages/publications/85163156927
UR - https://www.scopus.com/inward/citedby.url?scp=85163156927&partnerID=8YFLogxK
U2 - 10.1109/AISP57993.2023.10135002
DO - 10.1109/AISP57993.2023.10135002
M3 - Conference contribution
AN - SCOPUS:85163156927
T3 - 2023 3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023
BT - 2023 3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023
Y2 - 18 March 2023 through 20 March 2023
ER -