scholarly journals Data-driven thresholding in denoising with Spectral Graph Wavelet Transform

2021 ◽  
Vol 389 ◽  
pp. 113319
Author(s):  
Basile de Loynes ◽  
Fabien Navarro ◽  
Baptiste Olivier
Keyword(s):  
Wavelet Transform ◽  
Data Driven ◽  
Spectral Graph

Infrared and visible image fusion with spectral graph wavelet transform

2015 ◽  
Vol 32 (9) ◽  
pp. 1643 ◽  
Author(s):  
Xiang Yan ◽  
Hanlin Qin ◽  
Jia Li ◽  
Huixin Zhou ◽  
Jing-guo Zong
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Visible Image ◽  
Spectral Graph

Processing of Images and Videos for Extracting Text Information from Clustered Features Using Graph Wavelet Transform

2019 ◽  
Vol 16 (2) ◽  
pp. 557-561
Author(s):  
Merlin L. M. Livingston ◽  
Agnel L. G. X. Livingston
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Video Analysis ◽  
Hill Climbing ◽  
Primary Role ◽  
Hill Climbing Algorithm ◽  
Meaningful Information ◽  
Spectral Graph ◽  
Text Information ◽  
Sensitivity Specificity

Image processing is an interesting domain for extracting knowledge from real time video and images for surveillance, automation, robotics, medical and entertainment industries. The data obtained from videos and images are continuous and hold a primary role in semantic based video analysis, retrieval and indexing. When images and videos are obtained from natural and random sources, they need to be processed for identifying text, tracking, binarization and recognising meaningful information for succeeding actions. This proposal defines a solution with assistance of Spectral Graph Wave Transform (SGWT) technique for localizing and extracting text information from images and videos. K Means clustering technique precedes the SGWT process to group features in an image from a quantifying Hill Climbing algorithm. Precision, Sensitivity, Specificity and Accuracy are the four parameters which declares the efficiency of proposed technique. Experimentation is done from training sets from ICDAR and YVT for videos.

Multi-focus image fusion based on improved spectral graph wavelet transform

2015 ◽  
Author(s):  
Xiang Yan ◽  
Hanlin Qin ◽  
Zhimin Chen ◽  
Huixin Zhou ◽  
Jia Li ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Spectral Graph ◽  
Focus Image

Shape Analysis Using the Spectral Graph Wavelet Transform

2013 ◽  
Author(s):  
Jorge De Jesus Gomes Leandro ◽  
Roberto Marcondes Cesar Jr ◽  
Rogerio Schmidt Feris
Keyword(s):  
Wavelet Transform ◽  
Shape Analysis ◽  
Spectral Graph

scholarly journals Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform

2020 ◽  
Author(s):  
Mohammadreza Kaji ◽  
Jamshid Parvizian ◽  
Hans Wernher van de Venn
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Maintenance Cost ◽  
Continuous Wavelet ◽  
Health Indicator ◽  
Failure Data ◽  
Convolutional Autoencoder

Estimating the remaining useful life (RUL) of components is a crucial task to enhance the reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator (HI) to infer the current condition of the component, and modelling the degradation process, to estimate the future behavior. Although many signal processing and data-driven based methods were proposed to construct the HI, most of the existing methods are based on manual feature extraction techniques, and need the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the HI. For this purpose, the continuous wavelet transform (CWT) technique is used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model, which learns the healthy operation data distribution, is used to construct the HI. The proposed method is tested on a benchmark bearing dataset and compared with several other traditional HI construction models. Experimental results indicate that the constructed HI exhibits a monotonically increasing degradation trend and has a good performance to detect incipient faults.

scholarly journals Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform

2020 ◽  
Vol 10 (24) ◽  
pp. 8948
Author(s):  
Mohammadreza Kaji ◽  
Jamshid Parvizian ◽  
Hans Wernher van de Venn
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Maintenance Cost ◽  
Continuous Wavelet ◽  
Health Indicator ◽  
Failure Data ◽  
Convolutional Autoencoder

Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator (HI) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the HI, most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the HI. For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the HI. The proposed method was tested on a benchmark bearing dataset and compared with several other traditional HI construction models. Experimental results indicate that the constructed HI exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.

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