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The sequences were captured with an ASUS Xtion Pro Live camera in both RGB and depth channels.
We propose a deep network which seeks to construct a joint and shared multi-modal representation through bilinearly combining the convolutional neural network (CNN) streams of the RGB and depth channels.
Fluorescence spectra and diffuse reflectance spectra are collected through each of the two depth channels.
A decrease in the intensity of the reflectance spectra from neoplastic tissue is also observed in both the medium and shallow depth channels.
The use of a limited set of depth channels and illumination wavelengths enables compact design and rapid data acquisition, while still allowing depth selectivity and measurement of key features.
The most striking characteristic of the spectra shown in Fig. 2 is the substantial decrease in blue-green autofluorescence of neoplastic tissue compared to non-neoplastic tissue, observed in both the medium and shallow depth channels.
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Once adaptive neighborhoods are found, one must find some modeling for depth channel before utilizing this structural information.
This approach helps to illustrate artifacts in the depth channel as well as their effect on the rendered images.
Direct solution for discharge for known end depth, channel bed slope, and Manning's coefficient is provided in the graphical form for supercritical flow.
The accuracy of the model is proven by comparing the ID VDS curves with numerical simulations of devices designed with different gate depth, channel width, and epilayer thickness.
One can see that the depth postprocessing clearly makes a difference allowing to use stronger quantization of the depth channel and still to achieve good quality.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com