Abstract—One important inspection step in spring clamp is
the determination of roundness value. Detect the circle from
digital images is very important in computer vision. In view of
clamp and the geometrical characteristics of the circular, a
new optimization algorithm was proposed to extract image
circle. Circle extraction is equivalent to the points search and
extraction in the circle. Point search strategy is an optimization
problem. Particle swarm optimization (PSO) is an effective
global optimization method. Self-tuning parameters were
achieved by using the improved particle swarm algorithm
along with the geometric properties of the round, and detected
effective point of band image so as to realize the recognition
and detection of the circle. A large number of tests show that
the proposed algorithm has good robustness and precision
compared with the existing PSO algorithms. The main
advantages of this method as compared with other methods
include the considerable lower cost of facilities, easier
maintenance and getting more accurate.
Index Terms—Spring clamp, diameter, circle detection, particle swarm optimization.
The authors are with the State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Xia Zhu and Renwen Chen, "Roundness Measurement in Spring Clamps Based on a Novel Particle Swarm Optimization," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 219-224, 2015.