scholarly journals TransCut: Transparent Object Segmentation from a Light-Field Image

2015 ◽  
Author(s):  
Yichao Xu ◽  
Hajime Nagahara ◽  
Atsushi Shimada ◽  
Rin-ichiro Taniguchi
Keyword(s):  
Light Field ◽  
Object Segmentation ◽  
Light Field Image ◽  
Transparent Object

scholarly journals TransCut2: Transparent Object Segmentation From a Light-Field Image

2019 ◽  
Vol 5 (3) ◽  
pp. 465-477
Author(s):  
Yichao Xu ◽  
Hajime Nagahara ◽  
Atsushi Shimada ◽  
Rin-ichiro Taniguchi
Keyword(s):  
Light Field ◽  
Object Segmentation ◽  
Light Field Image ◽  
Transparent Object

No-Reference Light Field Image Quality Assessment Based on Depth, Structural and Angular Information

2021 ◽  
pp. 108063
Author(s):  
Jianjun Xiang ◽  
Gangyi Jiang ◽  
Mei Yu ◽  
Yongqiang Bai ◽  
Zhongjie Zhu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Light Field ◽  
Image Quality Assessment ◽  
Light Field Image

scholarly journals Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

2021 ◽  
Vol 13 (3) ◽  
pp. 455
Author(s):  
Md Nazrul Islam ◽  
Murat Tahtali ◽  
Mark Pickering
Keyword(s):  
Light Field ◽  
Mean Squared Error ◽  
Specular Reflection ◽  
Similarity Index ◽  
Detection Algorithm ◽  
Color Channel ◽  
Absolute Deviation ◽  
Significant Information ◽  
Transparent Objects ◽  
Transparent Object

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.

Light Field Image Quality Assessment Using Contourlet Transform

2021 ◽  
Author(s):  
Hailiang Huang ◽  
Huanqiang Zeng ◽  
Jing Chen ◽  
Canhui Cai ◽  
Kai-Kuang Ma
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Light Field ◽  
Image Quality Assessment ◽  
Contourlet Transform ◽  
Light Field Image

High-Resolution Fully Automatic Light Field Image Acquisition System Using One-Dimensional Camera Array

2021 ◽  
Author(s):  
Munkh-Uchral Erdenebat ◽  
Ki-Chul Kwon ◽  
Nyamsuren Darkhanbaatar ◽  
Jin Kyu Jung ◽  
Sang-Keun Gil ◽  
...  
Keyword(s):  
High Resolution ◽  
Light Field ◽  
Image Acquisition ◽  
Acquisition System ◽  
One Dimensional ◽  
Camera Array ◽  
Image Acquisition System ◽  
Fully Automatic ◽  
Light Field Image
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