Automatic PCB inspection algorithms: a survey
Computer Vision and Image Understanding
Swarm intelligence
A System for Person-Independent Hand Posture Recognition against Complex Backgrounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Filtering Using a Tree-Based Estimator
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A sequential niche technique for multimodal function optimization
Evolutionary Computation
An Improved Chaotic Particle Swarm Optimization and Its Application in Investment
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Fast multi-template matching using a particle swarm optimization algorithm for PCB inspection
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
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An improved particle swarm optimization algorithm, CSPSO (Chaotic Species-based particle swarm optimization), is proposed for solving the template matching problem. Template matching is one of the image comparison techniques widely applied to component existence checking in the printed circuit board (PCB) and electronics assembly industries. The proposed approach adopts the special nonlinear characteristic and ergodicity of chaos to enrich the search ability of the species-based particle swarm optimization (SPSO). To test its performance, the proposed CSPSO-based approach is compared with SPSO-based approach using two experimental studies. The CSPSO-based approach is proven to be superior to the original SPSO-based one in term of efficiency.