scholarly journals Source depth estimation using spectral transformations and convolutional neural network in a deep-sea environment

2020 ◽  
Vol 148 (6) ◽  
pp. 3633-3644
Author(s):  
Wenbo Wang ◽  
Zhen Wang ◽  
Lin Su ◽  
Tao Hu ◽  
Qunyan Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Sea ◽  
Depth Estimation ◽  
Source Depth ◽  
Spectral Transformations

A 3D Convolutional Neural Network for Light Field Depth Estimation

2019 ◽  
Author(s):  
Agota Faluvegi ◽  
Quentin Bolsee ◽  
Sergiu Nedevschi ◽  
Vasile-Teodor Dadarlat ◽  
Adrian Munteanu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Light Field ◽  
Depth Estimation

scholarly journals Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1795 ◽  
Author(s):  
Xiao Lin ◽  
Dalila Sánchez-Escobedo ◽  
Josep R. Casas ◽  
Montse Pardàs
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Input Image ◽  
Estimation Accuracy ◽  
Single Task ◽  
Qualitative And Quantitative ◽  
Highly Correlated

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.

EPI-Patch Based Convolutional Neural Network for Depth Estimation on 4D Light Field

2017 ◽  
pp. 642-652 ◽  
Author(s):  
Yaoxiang Luo ◽  
Wenhui Zhou ◽  
Junpeng Fang ◽  
Linkai Liang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Light Field ◽  
Depth Estimation
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