Optimized training of deep neural network for image analysis using synthetic objects and augmented reality

2019 ◽  
Author(s):  
Thomas Lu ◽  
Alexander Huyen ◽  
Luan Nguyen ◽  
Joseph Osborne ◽  
Sarah Eldin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Augmented Reality ◽  
Deep Neural Network

An Automatic Interaction Method Using Part Recognition Based on Deep Network for Augmented Reality Assembly Guidance

2018 ◽  
Author(s):  
Xuyue Yin ◽  
Xiumin Fan ◽  
Jiajie Wang ◽  
Rui Liu ◽  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Augmented Reality ◽  
Real Time ◽  
Deep Neural Network ◽  
Back Propagation ◽  
User Interaction ◽  
Guidance System ◽  
Assembly Process ◽  
Interaction Method ◽  
Assembly Guidance

Assembly process of complex electromechanical products can be quite complicated and time consuming because of high quality demands. Aiming at improving the efficiency of the manual assembly process, this paper proposes an automatic interaction method using part recognition for augmented reality (AR) assembly guidance, which improves both the accuracy of part picking and the interaction efficiency of AR guidance system. Taking sample images of similar parts as input and part types as output, a deep neural network model Part R-CNN for part recognition is build based on Faster R-CNN and is further fine-tuned by back propagation. By recognizing the assembly part, the augmented assembly guidance information of the corresponding parts assembly process is triggered in real-time without direct user interaction. Experimental results show that the deep neural network based part recognition method reaches 94% on mean average precision and the average recognition speed is 200ms per image frame. The average speed of AR guidance content triggering is about 20fps. All system performance satisfies the accuracy and real-time requirements of the AR-aided assembly system.

Teeth and Landmarks Detection and Classification Based on Deep Neural Networks

2019 ◽  
pp. 129-150
Author(s):  
Lyudmila N. Tuzova ◽  
Dmitry V. Tuzoff ◽  
Sergey I. Nikolenko ◽  
Alexey S. Krasnov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Rapid Development ◽  
Image Interpretation ◽  
Semantic Segmentation ◽  
Recent Decade

In the recent decade, deep neural networks have enjoyed rapid development in various domains, including medicine. Convolutional neural networks (CNNs), deep neural network structures commonly used for image interpretation, brought the breakthrough in computer vision and became state-of-the-art techniques for various image recognition tasks, such as image classification, object detection, and semantic segmentation. In this chapter, the authors provide an overview of deep learning algorithms and review available literature for dental image analysis with methods based on CNNs. The present study is focused on the problems of landmarks and teeth detection and classification, as these tasks comprise an essential part of dental image interpretation both in clinical dentistry and in human identification systems based on the dental biometrical information.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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