Contextual ensemble network for semantic segmentation

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

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DEVELOPMENT OF AN OPEN SOURCE, MACHINE LEARNING BASED TOOLSET FOR THE IDENTIFICATION OF DIKES IN SATELLITE IMAGES THROUGH SEMANTIC SEGMENTATION

10.1130/abs/2020am-357672 ◽

2020 ◽

Author(s):

Ryan Gray ◽

◽

Tushar Mittal

Keyword(s):

Machine Learning ◽

Open Source ◽

Satellite Images ◽

Semantic Segmentation

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Utilizing semantic segmentation method with convolutional neural net to model a partial nephrectomy simulator for 3D printing

European Urology Supplements ◽

10.1016/s1569-9056(19)31264-3 ◽

2019 ◽

Vol 18 (1) ◽

pp. e1747-e1748

Author(s):

Y.S. Kyung ◽

B. Lim ◽

S.Y. Choi ◽

J.H. Han ◽

W. Lee ◽

...

Keyword(s):

3D Printing ◽

Partial Nephrectomy ◽

Semantic Segmentation ◽

Neural Net ◽

Segmentation Method

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Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119855606 ◽

2019 ◽

Vol 2673 (12) ◽

pp. 494-507 ◽

Cited By ~ 1

Author(s):

Mhafuzul Islam ◽

Mashrur Chowdhury ◽

Hongda Li ◽

Hongxin Hu

Keyword(s):

Object Detection ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Semantic Segmentation ◽

Steering Wheel ◽

Potential Hazard ◽

Driving System ◽

Hazardous Object ◽

Vision Based Navigation ◽

Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination

IEEE Access ◽

10.1109/access.2020.2991354 ◽

2020 ◽

Vol 8 ◽

pp. 81724-81734 ◽

Cited By ~ 2

Author(s):

Aichen Wang ◽

Yifei Xu ◽

Xinhua Wei ◽

Bingbo Cui

Keyword(s):

Image Enhancement ◽

Semantic Segmentation ◽

Enhancement Method

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Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽

10.3390/s21113848 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3848

Author(s):

Wei Cui ◽

Meng Yao ◽

Yuanjie Hao ◽

Ziwei Wang ◽

Xin He ◽

...

Keyword(s):

Remote Sensing ◽

Prior Knowledge ◽

Contextual Information ◽

Information Aggregation ◽

Semantic Segmentation ◽

Spatial Correlations ◽

Convolutional Network ◽

Object Based ◽

Graph Neural Networks ◽

Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

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