A population-based automatic clustering algorithm for image segmentation

Mousavirad, Seyed Jalaleddin; Schaefer, Gerald; Moghadam, Mahshid Helali; Saadatmand, Mehrdad; Pedram, Mahdi

doi:10.1145/3449726.3463148

Cited by 2 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cluster analysis is commonly used for data classification. Cluster analysis is often used in machine learning, data mining, and other areas [15] and classifies different data objects by specific rules so that the objects in the same class exhibit a high degree of similarity and the ones in different classes show a high degree of variability [16] . In this study, data noise points are removed by clustering effective points and noise points in different classes.…”

Section: K-medoids Noise Preprocessing Algorithmmentioning

confidence: 99%

Error Analysis and Accuracy Optimization for the Hand-eye Calibration of Line Structured Light

Zhang

Yin

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The rapid development of machine vision technology in recent years has resulted in applications in engineering scenarios such as weld tracking, visual measurement, and precision gripping. Focusing on the fundamental steps of applying machine vision technology, the “hand-eye calibration” process is analyzed based on scenarios with a line-structured light camera and calibration ball. We found out that noise and camera pose are the main sources of calibration error. To address these issues, an improved K-medoids noise pre-processing algorithm and a camera shooting attitude control scheme are proposed and applied. The experimental results showed that when the radius of the tangent circle/radius of the calibration sphere approximates 0.618, the root means a square error of the repeatability accuracy of hand-eye calibration could be controlled within 0.23 mm, which fully meets the application requirement. Considering the low cost of line-structured light cameras, the findings of this study contribute to the large-scale diffusion of machine vision technology in engineering practices.

show abstract

Section: K-medoids Noise Preprocessing Algorithmmentioning

confidence: 99%