2021 ◽  
Vol 13 (3) ◽  
pp. 455
Author(s):  
Md Nazrul Islam ◽  
Murat Tahtali ◽  
Mark Pickering

Multispectral polarimetric light field imagery (MSPLFI) contains significant information about a transparent object’s distribution over spectra, the inherent properties of its surface and its directional movement, as well as intensity, which all together can distinguish its specular reflection. Due to multispectral polarimetric signatures being limited to an object’s properties, specular pixel detection of a transparent object is a difficult task because the object lacks its own texture. In this work, we propose a two-fold approach for determining the specular reflection detection (SRD) and the specular reflection inpainting (SRI) in a transparent object. Firstly, we capture and decode 18 different transparent objects with specularity signatures obtained using a light field (LF) camera. In addition to our image acquisition system, we place different multispectral filters from visible bands and polarimetric filters at different orientations to capture images from multisensory cues containing MSPLFI features. Then, we propose a change detection algorithm for detecting specular reflected pixels from different spectra. A Mahalanobis distance is calculated based on the mean and the covariance of both polarized and unpolarized images of an object in this connection. Secondly, an inpainting algorithm that captures pixel movements among sub-aperture images of the LF is proposed. In this regard, a distance matrix for all the four connected neighboring pixels is computed from the common pixel intensities of each color channel of both the polarized and the unpolarized images. The most correlated pixel pattern is selected for the task of inpainting for each sub-aperture image. This process is repeated for all the sub-aperture images to calculate the final SRI task. The experimental results demonstrate that the proposed two-fold approach significantly improves the accuracy of detection and the quality of inpainting. Furthermore, the proposed approach also improves the SRD metrics (with mean F1-score, G-mean, and accuracy as 0.643, 0.656, and 0.981, respectively) and SRI metrics (with mean structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (IMMSE), and mean absolute deviation (MAD) as 0.966, 0.735, 0.073, and 0.226, respectively) for all the sub-apertures of the 18 transparent objects in MSPLFI dataset as compared with those obtained from the methods in the literature considered in this paper. Future work will exploit the integration of machine learning for better SRD accuracy and SRI quality.


1856 ◽  
Vol 7 ◽  
pp. 60-66

The explanation given by Dr. Goring and others of the advantage of increased angular aperture in microscopic objective-glasses appears to the author to be correct, as applied to the case of opake objects, and accordingly his remarks in the present communication have reference to transparent objects only. It is known that delicate markings on a transparent object, such as the valve of a Gyrosigma , may be rendered more distinctly visible by using an object-glass of large aperture, by bringing the mirror to one side, and by placing a central stop in the object-glass or the condenser or in both; the increased distinctness produced in these several ways being due to the illumination of the object by oblique light. Experiment also shows that the degree of obliquity of the light requisite varies with the delicacy or fineness of the markings, being greater as these are more delicate; so that the finest markings require the most oblique light which can possibly be obtained to render them evident, and the angular aperture of the object-glass must necessarily be proportionately large, otherwise none of these oblique rays could enter it.


2015 ◽  
Vol 15 (12) ◽  
pp. 823 ◽  
Author(s):  
Shohei Ueda ◽  
Yusuke Tani ◽  
Takehiro Nagai ◽  
Kowa Koida ◽  
Shigeki Nakauchi ◽  
...  

2019 ◽  
Vol 127 (10) ◽  
pp. 1527-1544
Author(s):  
Guanying Chen ◽  
Kai Han ◽  
Kwan-Yee K. Wong
Keyword(s):  

2019 ◽  
Vol 5 (3) ◽  
pp. 465-477
Author(s):  
Yichao Xu ◽  
Hajime Nagahara ◽  
Atsushi Shimada ◽  
Rin-ichiro Taniguchi

Sign in / Sign up

Export Citation Format

Share Document