A frequency-based window width optimized two-dimensional S-Transform profilometry

2017 ◽  
Vol 402 ◽  
pp. 1-8 ◽  
Author(s):  
Min Zhong ◽  
Feng Chen ◽  
Chao Xiao
Keyword(s):  
Two Dimensional ◽  
Window Width ◽  
S Transform

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

Power System Disturbance Recognition Using Wavelet and S-Transform Techniques

2004 ◽  
Vol 1 (2) ◽  
Author(s):  
Jaya Bharata Reddy ◽  
Dusmanta Kumar Mohanta ◽  
B.M. Karan
Keyword(s):  
Wavelet Transform ◽  
Power System ◽  
Power Quality ◽  
A Priori ◽  
Electrical Power ◽  
Quality Analysis ◽  
Discrete Wavelet ◽  
Electrical Power System ◽  
Window Width ◽  
S Transform

Power quality issues have been a source of major concern in recent years due to extensive use of power electronic devices and non-linear loads in electrical power system and consequently sensitive detection and accurate classification of power disturbances have become very much necessary. To monitor electrical power quality disturbances, short time discrete Fourier transform (STFT) is most often used. But for non- stationary signals, the STFT does not track the signal dynamics properly due to the limitations of a fixed window width chosen a priori. This paper presents a new approach for power quality analysis using a modified wavelet transform, known as S–transform and the analysis of several power quality problems using both S–transform as well as discrete wavelet transform validates the superiority of S–transform.

Thresholding-Based Image Denoising Using Two-Dimensional S-Transform

2012 ◽  
Author(s):  
Yu-huan Luan ◽  
Feng-rong Sun ◽  
Paul Babyn ◽  
Shang-ling Song ◽  
Gui-hua Yao ◽  
...  
Keyword(s):  
Image Denoising ◽  
Two Dimensional ◽  
S Transform

Myoelectric signal classification based on S transform and two-directional two-dimensional principal component analysis

2017 ◽  
Vol 40 (7) ◽  
pp. 2387-2395 ◽  
Author(s):  
Yi Ji ◽  
Hong-Bo Xie
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Computational Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
S Transform ◽  
Efficiency And Effectiveness

Time-frequency representiation has been intensively employed for the analysis of biomedical signals. In order to extract discriminative information, time-frequency matrix is often transformed into a 1D vector followed by principal component analysis (PCA). This study contributes a two-directional two-dimensional principal component analysis (2D2PCA)-based technique for time-frequency feature extraction. The S transform, integrating the strengths of short time Fourier transform and wavelet transform, is applied to perform the time-frequency decomposition. Then, 2D2PCA is directly conducted on the time-frequency matrix rather than 1D vectors for feature extraction. The proposed method can significantly reduce the computational cost while capture the directions of maximal time-frequency matrix variance. The efficiency and effectiveness of the proposed method is demonstrated by classifying eight hand motions using 4-channel myoelectric signals recorded in health subjects and amputees.

Application of Two-Dimensional S-Transform in Optical 3D Shape Measurement

2017 ◽  
Vol 54 (4) ◽  
pp. 041206
Author(s):  
宋梦洒 Song Mengsa ◽  
陈文静 Chen Wenjing
Keyword(s):  
Shape Measurement ◽  
Two Dimensional ◽  
3D Shape ◽  
3D Shape Measurement ◽  
S Transform

Image Focus Measure Based on Energy of High Frequency Components in S-Transform

2013 ◽  
pp. 162-180
Author(s):  
Muhammad Tariq Mahmood ◽  
Tae-Sun Choi
Keyword(s):  
High Frequency ◽  
Window Size ◽  
Accurate Method ◽  
Image Sharpness ◽  
Focus Measure ◽  
Window Width ◽  
S Transform ◽  
Variable Window ◽  
Focus Image ◽  
Frequency Components

Focus measure computes sharpness or high frequency contents in an image. It plays an important role in many image processing and computer vision applications such as shape from focus (SFF) techniques and multi-focus image fusion algorithms. In this chapter, we discuss different focus measures in spatial as well as in the transform domains. In addition, we suggest a novel focus measure in S-transform domain, which is based on the energy of high frequency components. A localized spectrum, by using variable window size, provides a more accurate method of measuring image sharpness as compared to other focus measures proposed in spectral domains. An optimal focus measure is obtained by selecting an appropriate frequency dependent window width. The performance of the proposed focus measure is compared with those of existing focus measures in terms of three dimensional shape recovery and all-in-focus image generation. Experimental results demonstrate the effectiveness of the proposed focus measure.

Sign in / Sign up

Export Citation Format

Share Document