3D Reconstruction of Deformable Colon Structures based on Preoperative Model and Deep Neural Network

2021 ◽  
Author(s):  
Shuai Zhang ◽  
Liang Zhao ◽  
Shoudong Huang ◽  
Ruibin Ma ◽  
Boni Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
3D Reconstruction ◽  
Deep Neural Network

Removing gloss using Deep Neural Network for 3D Reconstruction

2019 ◽  
Vol 2019 (1) ◽  
pp. 143-148
Author(s):  
Futa Matsushita ◽  
Ryo Takahasshi ◽  
Mari Tsunomura ◽  
Norimichi Tsumura
Keyword(s):  
Neural Network ◽  
3D Reconstruction ◽  
Conventional Method ◽  
Deep Neural Network ◽  
High Accuracy ◽  
Simple Shape ◽  
3D Shape ◽  
Illumination Direction ◽  
Rgb Images ◽  
3D Shapes

3D reconstruction is used for inspection of industrial products. The demand for measuring 3D shapes is increased. There are many methods for 3D reconstruction using RGB images. However, it is difficult to reconstruct 3D shape using RGB images with gloss. In this paper, we use the deep neural network to remove the gloss from the image group captured by the RGB camera, and reconstruct the 3D shape with high accuracy than conventional method. In order to do the evaluation experiment, we use CG of simple shape and create images which changed geometry such as illumination direction. We removed gloss on these images and corrected defect parts after gloss removal for accurately estimating 3D shape. Finally, we compared 3D estimation using proposed method and conventional method by photo metric stereo. As a result, we show that the proposed method can estimate 3D shape more accurately than the conventional method.

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

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Underwater Fish Images Classification by Deep Neural Network

2020 ◽  
Author(s):  
Ala Supriya ◽  
Chiluka Venkat ◽  
Aliketti Deepak ◽  
GV Hari Prasad
Keyword(s):  
Neural Network ◽  
Deep Neural Network
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