Image analysis and computer vision: 1999
Computer Vision and Image Understanding
An Adaptive Poly-Parental Recombination Strategy
Selected Papers from AISB Workshop on Evolutionary Computing
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Editorial: special issue on machine vision
Computers in Industry - Special issue: Machine vision
Soft computing for automated surface quality analysis of exterior car body panels
Applied Soft Computing
User-centric image segmentation using an interactive parameter adaptation tool
Pattern Recognition
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On-line evolving image classifiers and their application to surface inspection
Image and Vision Computing
An on-line interactive self-adaptive image classification framework
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
A novel feature selection based semi-supervised method for image classification
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Impact of object extraction methods on classification performance in surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Assessment of the influence of adaptive components in trainable surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
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An increasingly frequent application of Machine Vision technologies is in automated surface inspection for the detection of defects in manufactured products. Such systems offer significant benefits in terms of cost, detection rates, and user-satisfaction over conventional human inspection systems. However, they usually require significant investment of expert time to set up, are ''brittle'' in the sense of being highly specialised to the task for which they are tuned, and are consequently sensitive to changes in operating conditions or product specifications. This raises problems within an industrial setting, where operating conditions or requirements may change, and the end-users are experts in their manufacturing field, but not in image processing. In this paper, we describe the development of a rapidly reconfigurable system in which the users' tacit knowledge and requirements are elicited via a process of Interactive Evolution, finding the image processing parameters to achieve the required goals without any need for specialised knowledge of the machine vision system. We show that the resulting segmentation can be quickly and easily evolved from scratch, and achieves detection rates comparable to those of a hand-tuned system on a hot-rolled steel defect recognition problem.