Abstract
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 different low dynamic range (LDR) images. This method is applied to ensure a correct alignment of the different polarization HDR images for each spectral band. We have focused our efforts 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.
Highlights
Multispectral imaging as well as high dynamic range (HDR) imaging and polarimetric imaging are techniques developed to overcome the limitations of common scientific imaging systems
We propose a new method of material classification based on the degree of linear polarization (DoLP) maps which consists in thresholding a ratio between the DoLP values within the highlight and in the surrounding area (see Fig. 11(d) and Fig. 11(e))
Our capture device was successfully calibrated and the Camera Response Function (CRF) determined. We used this function to build four HDR images at different polarization angles for each spectral band, which were afterwards correctly registered after range compression, normalization and contrast enhancement
Summary
Multispectral imaging as well as HDR imaging and polarimetric imaging are techniques developed to overcome the limitations of common scientific imaging systems. Multispectral imaging allows us to retrieve a larger amount of information compared with monochrome or RGB color images It can help recovering spectral radiance or reflectance information pixel-wise, using some spectral estimation algorithms [1]. HDR imaging is used to capture useful image data from regions in the scene that present higher dynamic range than it is possible to capture with a single shot for non-HDR imaging systems [2, 3]. This work focuses on two main applications: image segmentation and objects’ material classification. These two tasks have been addressed by many authors before
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