12 May 2016 Multispectral image analysis for object recognition and classification
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Abstract
Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance.

The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM’s class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.
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C. R. Viau, C. R. Viau, P. Payeur, P. Payeur, A.-M. Cretu, A.-M. Cretu, } "Multispectral image analysis for object recognition and classification", Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440N (12 May 2016); doi: 10.1117/12.2223360; https://doi.org/10.1117/12.2223360
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