scholarly journals Lightweight Convolutional Neural Network for Image Processing Method for Gaze Estimation and Eye Movement Event Detection

2020 ◽  
Vol 13 (0) ◽  
pp. 7-15
Author(s):  
Joshua Emoto ◽  
Yutaka Hirata
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Eye Movement ◽  
Event Detection ◽  
Processing Method ◽  
Gaze Estimation ◽  
Image Processing Method

scholarly journals Classification of rat behavior with an image-processing method and a neural network

2000 ◽  
Vol 32 (1) ◽  
pp. 63-71 ◽  
Author(s):  
J. B. I. Rousseau ◽  
P. B. A. Van Lochem ◽  
W. H. Gispen ◽  
B. M. Spruijt
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Processing Method ◽  
Image Processing Method

scholarly journals Research on Image Processing Method of High Voltage Switchgear Temperature Measurement

2020 ◽  
Vol 440 ◽  
pp. 032111
Author(s):  
Longji Du ◽  
Shi Zhang ◽  
Liming Chen ◽  
Jinglong Ye ◽  
Meiting Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Temperature Measurement ◽  
High Voltage ◽  
Processing Method ◽  
Image Processing Method

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

Defect Measurement in Welded Objects by Radiography Testing and Chambolle’s Image Processing Method

2021 ◽  
Vol 40 (2) ◽  
Author(s):  
Amir Movafeghi ◽  
Effat Yahaghi ◽  
Mahdi Mirzapour ◽  
Pouyan ShayganFar
Keyword(s):  
Image Processing ◽  
Processing Method ◽  
Image Processing Method
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