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, P. Payeur, A.-M. Cretu, "Multispectral image analysis for object recognition and classification," Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440N (12 May 2016);