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7 February 2011 Towards autonomic computing in machine vision applications: techniques and strategies for in-line 3D reconstruction in harsh industrial environments
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Proceedings Volume 7877, Image Processing: Machine Vision Applications IV; 78770N (2011) https://doi.org/10.1117/12.872235
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States
Abstract
Nowadays machine vision applications require skilled users to configure, tune, and maintain. Because such users are scarce, the robustness and reliability of applications are usually significantly affected. Autonomic computing offers a set of principles such as self-monitoring, self-regulation, and self-repair which can be used to partially overcome those problems. Systems which include self-monitoring observe their internal states, and extract features about them. Systems with self-regulation are capable of regulating their internal parameters to provide the best quality of service depending on the operational conditions and environment. Finally, self-repairing systems are able to detect anomalous working behavior and to provide strategies to deal with such conditions. Machine vision applications are the perfect field to apply autonomic computing techniques. This type of application has strong constraints on reliability and robustness, especially when working in industrial environments, and must provide accurate results even under changing conditions such as luminance, or noise. In order to exploit the autonomic approach of a machine vision application, we believe the architecture of the system must be designed using a set of orthogonal modules. In this paper, we describe how autonomic computing techniques can be applied to machine vision systems, using as an example a real application: 3D reconstruction in harsh industrial environments based on laser range finding. The application is based on modules with different responsibilities at three layers: image acquisition and processing (low level), monitoring (middle level) and supervision (high level). High level modules supervise the execution of low-level modules. Based on the information gathered by mid-level modules, they regulate low-level modules in order to optimize the global quality of service, and tune the module parameters based on operational conditions and on the environment. Regulation actions involve modifying the laser extraction method to adapt to changing conditions in the environment.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Julio Molleda, Rubén Usamentiaga, Daniel F. García, and Francisco G. Bulnes "Towards autonomic computing in machine vision applications: techniques and strategies for in-line 3D reconstruction in harsh industrial environments", Proc. SPIE 7877, Image Processing: Machine Vision Applications IV, 78770N (7 February 2011); https://doi.org/10.1117/12.872235
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