A New Processing Method for Signal and Image Analysis Using Discrete Wavelet Transform,

2019 ◽  
Vol 13 (6) ◽  
pp. 945-953
Keyword(s):  
Image Analysis ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Processing Method ◽  
Discrete Wavelet ◽  
New Processing

Discrete Wavelet Transform on Near-Infrared Spectra Analysis for Moisture Determination in Lignitic Coal Samples

2014 ◽  
Vol 513-517 ◽  
pp. 319-322
Author(s):  
Xiao Li Yang ◽  
Neng Bang Hou ◽  
Jun You Shi ◽  
Yan Fang Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Near Infrared ◽  
Processing Method ◽  
Discrete Wavelet ◽  
Moisture Determination ◽  
Least Squares Regression ◽  
Spectra Analysis ◽  
Near Infrared Spectra ◽  
Compressed Data

We studied moisture determination in lignitic coal samples through near-infrared (NIR) technique. This research was developed by applying partical least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, the NIR spectra were pre-processed by DWT for fitting and compression. Then, the compressed data were used to build regression model with PLS for moisture determination in coal samples. Three type DWTs were investigated.Determination performance at different resolution scales was studied. The results show that DWT is a very efficient pre-processing method for NIR spectra analysis.

Improving Space Localization Properties of the Discrete Wavelet Transform

Informatica ◽  
2013 ◽  
Vol 24 (4) ◽  
pp. 657-675
Author(s):  
Jonas Valantinas ◽  
Deividas Kančelkis ◽  
Rokas Valantinas ◽  
Gintarė Viščiūtė
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Space Localization

An Adaptive Binarization Method for Cost-efficient Document Image System in Wavelet Domain

2020 ◽  
Vol 64 (3) ◽  
pp. 30401-1-30401-14 ◽  
Author(s):  
Chih-Hsien Hsia ◽  
Ting-Yu Lin ◽  
Jen-Shiun Chiang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Document Image ◽  
Background Information ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Image System ◽  
Low Frequencies ◽  
Cost Efficient ◽  
Binarization Method

Abstract In recent years, the preservation of handwritten historical documents and scripts archived by digitized images has been gradually emphasized. However, the selection of different thicknesses of the paper for printing or writing is likely to make the content of the back page seep into the front page. In order to solve this, a cost-efficient document image system is proposed. In this system, the authors use Adaptive Directional Lifting-Based Discrete Wavelet Transform to transform image data from spatial domain to frequency domain and perform on high and low frequencies, respectively. For low frequencies, the authors use local threshold to remove most background information. For high frequencies, they use modified Least Mean Square training algorithm to produce a unique weighted mask and perform convolution on original frequency, respectively. Afterward, Inverse Adaptive Directional Lifting-Based Discrete Wavelet Transform is performed to reconstruct the four subband images to a resulting image with original size. Finally, a global binarization method, Otsu’s method, is applied to transform a gray scale image to a binary image as the output result. The results show that the difference in operation time of this work between a personal computer (PC) and Raspberry Pi is little. Therefore, the proposed cost-efficient document image system which performed on Raspberry Pi embedded platform has the same performance and obtains the same results as those performed on a PC.

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