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Machine vision can be described as the integration of imaging devices, computers, algorithms, and robotics to automate manufacturing inspection, characterization, and control. Today's high-speed, complex manufacturing systems require the development of automation technologies that can efficiently collect data, use historical information to provide context, and generate process knowledge. Intelligently designed systems can use this knowledge for automatic characterization and control of product quality and the manufacturing process. In this regard, machine vision technology continues to provide new and innovative opportunities to automate manufacturing systems.
The semiconductor and electronic industries are excellent examples of the successful application of machine vision technology today. Almost all semiconductor and electronic manufacturing equipment include machine vision systems to perform essential tasks such as alignment and positioning, and information gathering tasks such as automatic defect and signature classification for yield management. Many other industries are witnessing a rapid adoption of this technology including aluminum, forest products, textiles, glass, steel, metal casting, and chemicals. There are numerous innovative methods for adding machine vision systems to manufacturing processes to improve productivity, quality, and compliance with product standards, thus providing a competitive advantage. This conference brings together practitioners and researchers in machine vision to share recent developments in computer vision architectures, hardware, algorithms, and software for industrial inspection, characterization, and control. Papers emphasizing the integration of machine vision systems into the manufacturing infrastructure are especially welcome.
Papers are solicited in four broad topical areas:
image processing and metrology
measurement of color and appearance
feature analysis and pattern recognition
machine vision systems integration and process characterization.
Individual papers are solicited but not limited to the following topics:
new or improved algorithms for industrial inspection
novel hardware designs for machine vision systems
robot vision and tracking
performance evaluation of algorithms
use of 3D or color imaging techniques
industrial applications of machine vision
food, agriculture, and pharmaceuticals applications of machine vision
machine learning, pattern recognition, and feature analysis techniques
use of machine vision information for process control and diagnosis, trend analysis, and preventive maintenance
case studies of the impact of machine vision in manufacturing.
All submissions will be peer reviewed. Please note that abstracts must be at least 500 words in length in order to receive full consideration.
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