First, we train a CNN model for depth images by teaching the network to reproduce the mid-level semantic representation learned from RGB images for which there are paired images.
As shown in Fig. 2, the upper branch of our saliency detection pipeline is a deep CNN architecture with global context for RGB images, and the lower branch of our saliency detection pipeline is a deep CNN architecture with global context for depth images.
The scheme for training the depth CNN for depth images of modality (mathcal {M}_{d}) is to learn the parameters of CNN Ψ such that feature vectors from (psi ^{L}_{D_{d}}(I_{d})) for image I d match the feature vectors from (psi ^{i^_{mathcal {M}_{s},D_{s}}(I_{s})) for its image pair I s in modality (mathcal {M}_{s}) for some chosen and fixed layer i∗∈ [ 1⋯K].
The related work on multi-track-based forensic techniques include the following: In the prior work, we proposed a resampling detecting method for depth-image-based rendering (DIBR) images [14].
Before proceeding with the actual depth processing task, the characteristics of the representation view-plus-depth were overviewed, including methods of depth image based rendering for virtual view generation, and formulation of the depth map filtering problem.
The run time of ACSD is for per depth image; GMR, MC and MDF are for per RGB image; and LMH, GP and BFSD are for per RGB-D image pair.
These operations are highly effective for the depth image noise reduction.
In this article, we propose a new method for denoising depth image sequences, taking into account information from the associated luminance sequences.
Most of studies based on DIBR in recent years is some improvements based on the framework proposed Do [24], for example, depth image preprocessing [27], improved hole-filling algorithm [28], allocation of resources [9], and parallelization acceleration.
