Image processing pipeline for segmentation and material classification based on multispectral high dynamic range polarimetric images

Angel Martinez-Domingo, Miguel; Valero, Eva M.; Hernandez-Andres, Javier; Tominaga, Shoji; Horiuchi, Takahiko; Hirai, Keita

VL / 25 - BP / 30073 - EP / 30090
We propose a method for the capture of high dynamic range (HDR), multispectral (MS), polarimetric (Pol) images of indoor scenes using a liquid crystal tunable filter (LCTF). We have included the adaptive exposure estimation (AEE) method to fully automatize the capturing process. We also propose a pre- processing method which can be applied for the registration of HDR images after they are already built as the result of combining di ff erent low dynamic range (LDR) images. This method is applied to ensure a correct alignment of the di ff erent polarization HDR images for each spectral band. We have focused our e ff orts in two main applications: object segmentation and classification into metal and dielectric classes. We have simplified the segmentation using mean shift combined with cluster averaging and region merging techniques. We compare the performance of our segmentation with that of Ncut and Watershed methods. For the classification task, we propose to use information not only in the highlight regions but also in their surrounding area, extracted from the degree of linear polarization (DoLP) maps. We present experimental results which proof that the proposed image processing pipeline outperforms previous techniques developed specifically for MSHDRPol image cubes. (C) 2017 Optical Society of America
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