Statistical analysis of IR thermographic sequences by PCA
Marinetti, S., Grinzato, E., Bison, P. G., Bozzi, E., Chimenti, M., Pieri, G. and Salvetti, O.
2004 Infrared Physics & Technology, 46(1-2): 85-91
IR image sequence; Principal component analysis; Learning and measuring; Data compression; Feature extraction
Marinetti, S., Grinzato, E., Bison, P. G., Bozzi, E., Chimenti, M., Pieri, G. and Salvetti, O., (2004), "Statistical analysis of IR thermographic sequences by PCA", Infrared Physics & Technology, 46(1-2): 85-91.
Abstract:
Automatic processing of IR sequences is a desirable target in Thermal Non-Destructive Evaluation (TNDE) of materials. Unfortunately, this task is made difficult by the presence of many undesired signals that corrupt the useful information detected by the IR camera. In this paper the Principal Component Analysis (PCA) is used to process IR image sequences to extract features and reduce redundancy by projecting the original data onto a system of orthogonal components. As a thermographic sequence contains information both in space and time, the way of applying the PCA to these data cannot be straightforwardly borrowed from typical applications of the PCA where the information is mainly spatial (e.g. remote sensing, face recognition). This peculiarity has been analysed and the results are reported. Finally, in addition to the use of the PCA as an unsupervised method, its use in a "learning and measuring" configuration is considered.
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