Paper
2 February 2001 Multivariate image analysis for process monitoring and control
John F. MacGregor, Manish H. Bharati, Honglu Yu
Author Affiliations +
Proceedings Volume 4188, Process Imaging for Automatic Control; (2001) https://doi.org/10.1117/12.417168
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States
Abstract
Information from on-line imaging sensors has great potential for the monitoring and control of quality in spatially distributed systems. The major difficulty lies in the efficient extraction of information from the images, information such as the frequencies of occurrence of specific and often subtle features, and their locations in the product or process space. This paper presents an overview of multivariate image analysis methods based on Principal Component Analysis and Partial Least Squares for decomposing the highly correlated data present in multi-spectral images. The frequencies of occurrence of certain features in the image, regardless of their spatial locations, can be easily monitored in the space of the principal components. The spatial locations of these features can then be obtained by transposing highlighted pixels from the PC score space into the original image space. In this manner it is possible to easily detect and locate even very subtle features from online imaging sensors for the purpose of statistical process control or feedback control of spatial processes. The concepts and potential of the approach are illustrated using a sequence of LANDSAT satellite multispectral images, depicting a pass over a certain region of the earth’s surface. Potential applications in industrial process monitoring using these methods will be discussed from a variety of areas such as pulp and paper sheet products, lumber and polymer films.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John F. MacGregor, Manish H. Bharati, and Honglu Yu "Multivariate image analysis for process monitoring and control", Proc. SPIE 4188, Process Imaging for Automatic Control, (2 February 2001); https://doi.org/10.1117/12.417168
Lens.org Logo
CITATIONS
Cited by 15 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image processing

Image analysis

Principal component analysis

Process control

Earth observing sensors

Satellites

Satellite imaging

Back to Top