Human-machine interaction issues in quality control based on online image classification

Lughofer, Edwin; Smith, Jim; Tahir, Muhammad A.; Caleb-Solly, Praminda; Eitzinger, Christian; Sannen, Davy; Nuttin, Marnix

Abstract

This paper considers on a number of issues that arise when a trainable machine vision system learns directly from humans. We contrast this to the "normal" situation where machine learning (ML) techniques are applied to a "cleaned" data set which is considered to be perfectly labeled with complete accuracy. This paper is done within the context of a generic system for the visual surface inspection of manufactured parts; however, the issues treated are relevant not only to wider computer vision applications such as medical image screening but also to classification more generally. Many of the issues we consider arise from the nature of humans themselves: They will be not only internally inconsistent but also will often not be completely confident about their decisions, particularly if they are making decisions rapidly. People will also often differ systematically from each other in the decisions they make. Other issues may arise from the nature of the process, which may require the ML to have the capacity for real-time online adaptation in response to users' input. Because of this, it may be that the users cannot always provide input to a consistent level of detail. We describe how all of these issues may be tackled within a coherent methodology. By using a range of classifiers trained on data sets from a compact disc imprint production process, we present results which demonstrate that training methods designed to take proper consideration of these issues may actually lead to improved performance. © 2009 IEEE.

• Journal Article Type: Article
• Publication Date: Aug 11, 2009
• Journal: IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
• Print ISSN: 1083-4427
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Not Peer Reviewed
• Volume: 39
• Issue: 5
• Pages: 960-971
• DOI: https://doi.org/10.1109/TSMCA.2009.2025025
• Keywords: computer vision, image classification, learning, artificial intelligence, man-machine systems, production engineering computing, quality control, human-machine interaction, machine learning, online image classification, quality control, real-time online adaptation, trainable machine vision system, variable input levels, human–machine interaction, HMI, image classification, insight into classifier structures, online adaptation, partial confidence, resolving contradictory inputs, variable input levels
• Public URL: https://uwe-repository.worktribe.com/output/1004073
• Publisher URL: http://dx.doi.org/10.1109/TSMCA.2009.2025025