Geometric Enhancement Using Image Domain Techniques

1993 ◽  
pp. 113-132 ◽  
Author(s):  
John A. Richards
Keyword(s):  
Image Domain

scholarly journals Improving Semi-Supervised Learning for Audio Classification with FixMatch

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1807
Author(s):  
Sascha Grollmisch ◽  
Estefanía Cano
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Transfer Learning ◽  
Data Transfer ◽  
State Of The Art ◽  
Training Data ◽  
Audio Classification ◽  
Image Domain ◽  
Full Dataset ◽  
Audio Data

Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.

Efficient Face Detection in the Fisheye Image Domain

2021 ◽  
pp. 1-1
Author(s):  
Cheng-Yun Yang ◽  
Homer H. Chen
Keyword(s):  
Face Detection ◽  
Image Domain

MR venography of the brain with enhanced vessel contrast using image-domain high-pass filtering of the susceptibility phase shift

2011 ◽  
Vol 34 (5) ◽  
pp. 1218-1225 ◽  
Author(s):  
Zhaoyang Jin ◽  
Ling Xia ◽  
Min Lou ◽  
Minming Zhang ◽  
Yiping P. Du
Keyword(s):  
Phase Shift ◽  
Mr Venography ◽  
Image Domain ◽  
High Pass ◽  
The Brain

scholarly journals The Electrostatic Energy of a Two-Dimensional System

1959 ◽  
Vol 42 ◽  
pp. 1-2
Author(s):  
LL. G. Chambers
Keyword(s):  
Complex Variable ◽  
Complex Potential ◽  
Unit Length ◽  
Electrostatic Energy ◽  
Dimensional System ◽  
Two Dimensional ◽  
Image System ◽  
Actual System ◽  
Image Domain ◽  
Two Dimensional System

The use of the complex variable z( = x + iy) and the complex potential W(= U + iV) for two-dimensional electrostatic systems is well known and the actual system in the (x, y) plane has an image system in the (U, V) plane. It does not seem to have been noticed previously that the electrostatic energy per unit length of the actual system is simply related to the area of the image domain in the (U, V) plane.

scholarly journals Horizontal contraction in image domain for velocity inversion

Geophysics ◽  
2015 ◽  
Vol 80 (3) ◽  
pp. R95-R110 ◽  
Author(s):  
Peng Shen ◽  
William W. Symes
Keyword(s):  
Image Domain ◽  
Velocity Inversion
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