A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to genetic algorithms
An introduction to genetic algorithms
Automatic PCB inspection algorithms: a survey
Computer Vision and Image Understanding
Statistical grey-level models for object location and identification
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Practical genetic algorithms
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Equivalent Characterization of a Class of Conditional Probabilistic Independencies
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Mathematical Foundations of Navigation and Perception for an Autonomous Mobile Robot
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
Speeding-up NCC-Based Template Matching Using Parallel Multimedia Instructions
CAMP '05 Proceedings of the Seventh International Workshop on Computer Architecture for Machine Perception
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
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An important inspection task in the automated assembly of printed circuit boards (PCBs) is that of detecting if all components have been placed correctly on the board. This paper describes a constrained evolutionary search based inspection technique for simultaneously detecting multiple component objects in a source image. The approach has the advantage that it does not rely on image alignment (registration) as do conventional optical inspection methods such as image subtraction. It is a template based search method which achieves speed and quality requirements by making use of an evolutionary algorithm and a simultaneous search for multiple objects in a source image using a generalised template. The generalised template matching method defines a template model that takes into account the statistical variations between the grey-level appearances of components. The evolutionary search for specific components is constrained to Canny edges making this a fast method for locating multiple targets. Results are presented for locating multiple surface mount resistors on a PCB so that missing components can be reported.