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Programmable system on chip implementation of principal component analysis for preprocessing of multispectral image data acquired with filter wheel cameras

 
: Schellhorn, M.; Fütterer, R.; Rosenberger, M.; Notni, G.

:

Velez-Reyes, M. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV : 17-19 April 2018, Orlando, Florida, United States
Bellingham, WA: SPIE, 2018 (Proceedings of SPIE 10644)
ISBN: 978-1-5106-1799-5
ISBN: 978-1-5106-1800-8
Paper 106441O, 10 pp.
Conference "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery" <24, 2018, Orlando/Fla.>
English
Conference Paper
Fraunhofer IOF ()

Abstract
The acceleration of the acquisition of spectral images and their processing is important for the acceptance of these measurement methods in quality assurance and inspection. A frequently used preprocessing step is the Principal Component Analysis (PCA). It is used in variations, for example, for segmentation, spectral decomposition or data compression. The presented implementation calculates the PCA for the 12 spectral image channels of a filter wheel camera parallel to image acquisition. This includes the determination of the covariance matrix, the calculation of the main components and the transformation of the data. The parallel processing during the sequential imaging acquisition is performed on a System-on-A-programmable-chip (SoPC) Xilinx Zynq-7000 directly within the camera. The algorithm is partitioned into hard and software components and implemented in the field programmable gate array (FPGA) fabric as well as the ARM processor core firmware of the SoPC. In order to ensure the steps of the image acquisition chain in addition to the calculation, the system was implemented as an asymmetric multiprocessing system (AMP) with individual processors. For additional acceleration under static conditions (e.g. continuous testing in the manufacturing process), the feature vector can be stored as a calibration value. The calculation is reduced to the transformation of the data.

: http://publica.fraunhofer.de/documents/N-574565.html