scholarly journals Left and Right Consistent Stereo Image Detection and Classification Based on Deep Learning

2020 ◽  
Vol 1575 ◽  
pp. 012149
Author(s):  
Lijuan Tang ◽  
Qing Wang ◽  
Guannan Chen
Keyword(s):  
Deep Learning ◽  
Stereo Image ◽  
Image Detection ◽  
Left And Right

Advantages of Complementary Stereo Images as Viewed with the Low-Temperature Field-Emission SEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1314-1315
Author(s):  
William P. Wergin ◽  
Eric F. Erbe
Keyword(s):  
Fold Increase ◽  
Horizontal Displacement ◽  
Three Dimensions ◽  
Stereo Image ◽  
Stereo Pair ◽  
Sem Micrograph ◽  
Left And Right ◽  
The Common ◽  
The Brain ◽  
Pair Analysis

The eye-brain complex allows those of us with normal vision to perceive and evaluate our surroundings in three-dimensions (3-D). The principle factor that makes this possible is parallax - the horizontal displacement of objects that results from the independent views that the left and right eyes detect and simultaneously transmit to the brain for superimposition. The common SEM micrograph is a 2-D representation of a 3-D specimen. Depriving the brain of the 3-D view can lead to erroneous conclusions about the relative sizes, positions and convergence of structures within a specimen. In addition, Walter has suggested that the stereo image contains information equivalent to a two-fold increase in magnification over that found in a 2-D image. Because of these factors, stereo pair analysis should be routinely employed when studying specimens.Imaging complementary faces of a fractured specimen is a second method by which the topography of a specimen can be more accurately evaluated.

scholarly journals Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

2020 ◽  
Vol 10 (7) ◽  
pp. 2511
Author(s):  
Young-Joo Han ◽  
Ha-Jin Yu
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Machine Vision ◽  
Image Classification ◽  
Defect Detection ◽  
Detection System ◽  
Image Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Segmentation Image

As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.

Gradient clustering algorithm based on deep learning aerial image detection

2021 ◽  
Vol 141 ◽  
pp. 37-44
Author(s):  
Ning Liu ◽  
Bin Guo ◽  
Xinju Li ◽  
Xiangyu Min
Keyword(s):  
Deep Learning ◽  
Clustering Algorithm ◽  
Aerial Image ◽  
Image Detection

scholarly journals An Evaluation of Deep Learning-Based Computer Generated Image Detection Approaches

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 130830-130840 ◽  
Author(s):  
Xuan Ni ◽  
Linqiang Chen ◽  
Lifeng Yuan ◽  
Guohua Wu ◽  
Ye Yao
Keyword(s):  
Deep Learning ◽  
Image Detection

scholarly journals Brain-Inspired Deep Networks for Facial Expression Recognition

2020 ◽  
Author(s):  
Nafiseh Zeinali ◽  
Karim Faez ◽  
Sahar Seifzadeh
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Data Augmentation ◽  
Data Sets ◽  
Expression Recognition ◽  
Face Expression ◽  
Public Data ◽  
Improve Accuracy ◽  
Left And Right ◽  
Increase In Accuracy

Purpose: One of the essential problems in deep-learning face recognition research is the use of self-made and less counted data sets, which forces the researcher to work on duplicate and provided data sets. In this research, we try to resolve this problem and get to high accuracy. Materials and Methods: In the current study, the goal is to identify individual facial expressions in the image or sequence of images that include identifying ten facial expressions. Considering the increasing use of deep learning in recent years, in this study, using the convolution networks and, most importantly, using the concept of transfer learning, led us to use pre-trained networks to train our networks. Results: One way to improve accuracy in working with less counted data and deep-learning is to use pre-trained using pre-trained networks. Due to the small number of data sets, we used the techniques for data augmentation and eventually tripled the data size. These techniques include: rotating 10 degrees to the left and right and eventually turning to elastic transmation. We also applied deep Res-Net's network to public data sets existing for face expression by data augmentation. Conclusion: We saw a seven percent increase in accuracy compared to the highest accuracy in previous work on the considering dataset.

Medical Image Detection Using Deep Learning

2019 ◽  
pp. 3-16
Author(s):  
María Inmaculada García Ocaña ◽  
Karen López-Linares Román ◽  
Nerea Lete Urzelai ◽  
Miguel Ángel González Ballester ◽  
Iván Macía Oliver
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Image Detection

scholarly journals Evaluation of deep learning algorithms for semantic segmentation of car parts

2021 ◽  
Author(s):  
Kitsuchart Pasupa ◽  
Phongsathorn Kittiworapanya ◽  
Napasin Hongngern ◽  
Kuntpong Woraratpanya
Keyword(s):  
Deep Learning ◽  
Evaluation System ◽  
Learning Algorithms ◽  
Weather Conditions ◽  
Semantic Segmentation ◽  
Smart Device ◽  
Data Set ◽  
Basic Part ◽  
Left And Right ◽  
Instance Segmentation

AbstractEvaluation of car damages from an accident is one of the most important processes in the car insurance business. Currently, it still needs a manual examination of every basic part. It is expected that a smart device will be able to do this evaluation more efficiently in the future. In this study, we evaluated and compared five deep learning algorithms for semantic segmentation of car parts. The baseline reference algorithm was Mask R-CNN, and the other algorithms were HTC, CBNet, PANet, and GCNet. Runs of instance segmentation were conducted with those five algorithms. HTC with ResNet-50 was the best algorithm for instance segmentation on various kinds of cars such as sedans, trucks, and SUVs. It achieved a mean average precision at 55.2 on our original data set, that assigned different labels to the left and right sides and 59.1 when a single label was assigned to both sides. In addition, the models from every algorithm were tested for robustness, by running them on images of parts, in a real environment with various weather conditions, including snow, frost, fog and various lighting conditions. GCNet was the most robust; it achieved a mean performance under corruption, mPC = 35.2, and a relative degradation of performance on corrupted data, compared to clean data (rPC), of 64.4%, when left and right sides were assigned different labels, and mPC = 38.1 and rPC = $$69.6\%$$ 69.6 % when left- and right-side parts were considered the same part. The findings from this study may directly benefit developers of automated car damage evaluation system in their quest for the best design.

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