scholarly journals Wavelet Based Image Coding via Image Component Prediction Using Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 137-142
Author(s):  
Takuma Takezawa ◽  
◽  
Yukihiko Yamashita
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Coding ◽  
Line Segment ◽  
Predictive Coding ◽  
One Dimensional ◽  
Line Segment Matching ◽  
Real Value ◽  
Segment Matching ◽  
Image Component

In the process of wavelet based image coding, it is possible to enhance the performance by applying prediction. However, it is difficult to apply the prediction using a decoded image to the 2D DWT which is used in JPEG2000 because the decoded pixels are apart from pixels which should be predicted. Therefore, not images but DWT coefficients have been predicted. To solve this problem, predictive coding is applied for one-dimensional transform part in 2D DWT. Zhou and Yamashita proposed to use half-pixel line segment matching for the prediction of wavelet based image coding with prediction. In this research, convolutional neural networks are used as the predictor which estimates a pair of target pixels from the values of pixels which have already been decoded and adjacent to the target row. It helps to reduce the redundancy by sending the error between the real value and its predicted value. We also show its advantage by experimental results.

scholarly journals One-dimensional convolutional neural networks for low/high arousal classification from electrodermal activity

2022 ◽  
Vol 71 ◽  
pp. 103203
Author(s):  
Roberto Sánchez-Reolid ◽  
Francisco López de la Rosa ◽  
María T. López ◽  
Antonio Fernández-Caballero
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Electrodermal Activity ◽  
High Arousal ◽  
One Dimensional

Smoothly varying projective transformation for line segment matching

2022 ◽  
Vol 183 ◽  
pp. 129-146
Author(s):  
Xianwei Zheng ◽  
Zhuang Yuan ◽  
Zhen Dong ◽  
Mingyue Dong ◽  
Jianya Gong ◽  
...  
Keyword(s):  
Line Segment ◽  
Projective Transformation ◽  
Line Segment Matching ◽  
Segment Matching

scholarly journals A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

2019 ◽  
Vol 8 (4) ◽  
pp. 160 ◽  
Author(s):  
Bingxin Liu ◽  
Ying Li ◽  
Guannan Li ◽  
Anling Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Oil Film ◽  
One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

scholarly journals Estimation of Initial Position Using Line Segment Matching in Maps

10.5772/64067 ◽  
2016 ◽  
Vol 13 (3) ◽  
pp. 117
Author(s):  
Chongyang Wei ◽  
Ruili Wang ◽  
Tao Wu ◽  
Hao Fu
Keyword(s):  
Line Segment ◽  
Initial Position ◽  
Line Segment Matching ◽  
Segment Matching
Sign in / Sign up

Export Citation Format

Share Document