Illumination-robust feature detection based on adaptive threshold function

Computing ◽  
2022 ◽  
Author(s):  
Ruiping Wang ◽  
Liangcai Zeng ◽  
Shiqian Wu ◽  
Kelvin K. L. Wong
Keyword(s):  
Feature Detection ◽  
Adaptive Threshold ◽  
Threshold Function

scholarly journals DENOISING OF LIDAR ECHO SIGNAL BASED ON WAVELET ADAPTIVE THRESHOLD METHOD

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 215-220
Author(s):  
S. H. Long ◽  
G. Q. Zhou ◽  
H. Y. Wang ◽  
X. Zhou ◽  
J. L. Chen ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Threshold Method ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Signal Characteristics ◽  
Soft Threshold ◽  
Two Parameters ◽  
Unbiased Risk

Abstract. The wavelet threshold method is widely used in signal denoising. However, traditional hard threshold method or soft threshold method is deficient for depending on fixed threshold and instability. In order to achieve efficient denoising of echo signals, an adaptive wavelet threshold denoising method, absorbing the advantages of the hard threshold and the soft threshold, is proposed. Based on the advantages of traditional threshold method, new threshold function is continuous, steerable and flexibly changeable by adjusting two parameters. The threshold function is flexibly changed between the hard threshold and the soft threshold function by two parameter adjustments. According to the Stein unbiased risk estimate (SURE), this new method can determine thresholds adaptively. Adopting different thresholds adaptively at different scales, this method can automatically track noise, which can effectively remove the noise on each scale. Therefore, the problems of noise misjudgement and incomplete denoising can be solved, to some extent, in the process of signal processing. The simulation results of MATLAB show that compared with hard threshold method and soft threshold method, the signal-to-noise ratio (SNR) of the proposed de-noising method is increased by nearly 2dB, and 4dB respectively. It is safely to conclude that, when background noise eliminated, the new wavelet adaptive threshold method preserves signal details effectively and enhances the separability of signal characteristics.

Speech detection enhancement in optical fiber acoustic sensor via adaptive threshold function

2019 ◽  
Vol 47 ◽  
pp. 1-6 ◽  
Author(s):  
Zhengxian Zhou ◽  
Yangsheng Yuan ◽  
Xinyan Yang ◽  
Lu Gan ◽  
Youwu Du ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Acoustic Sensor ◽  
Speech Detection ◽  
Detection Enhancement

scholarly journals Empirical Mode Decomposition De-Noising Method Based on Improved Soft-Threshold

2011 ◽  
Vol 1 ◽  
pp. 421-425 ◽  
Author(s):  
Jian Hui Xi ◽  
Jia Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Soft Threshold ◽  
Simulation Results ◽  
Improved Function ◽  
Constant Deviation

In this paper, an improved soft-threshold function is constructed, combined the improved function and empirical mode decomposition (EMD) methods, a new de-noising method has been proposed. Set the adaptive threshold for the intrinsic mode functions (IMFs) of the EMD, and then de-noise the each IMF respectively. Finally, the de-noised signal is reconstructed by the de-noised IMF components. Through the simulation results of quantitative analysis by signal-to-noise ratio (SNR) and mean square error (MSE), the algorithm in this paper has better de-noising effect. Also, this method can effectively improve the constant deviation between the original signal and the de-noised signal by traditional soft-threshold.

scholarly journals Less computational approach to detect QRS complexes in ECG rhythms

2020 ◽  
Vol 2 (3) ◽  
pp. 113-120
Author(s):  
Tariq M. Younes ◽  
Mohammad Alkhedher ◽  
Mohamad Al Khawaldeh ◽  
Jalal Nawash ◽  
Ibrahim Al-Abbas
Keyword(s):  
Signal Analysis ◽  
Adaptive Threshold ◽  
Computational Approach ◽  
Threshold Function ◽  
Heart Rate Monitoring ◽  
Ecg Signals ◽  
Filter Structure ◽  
Reference Signals ◽  
Low Pass ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signals are normally affected by artifacts that require manual assessment or use of other reference signals. Currently, Cardiographs are used to achieve basic necessary heart rate monitoring in real conditions. This work aims to study and identify main ECG features, QRS complexes, as one of the steps of a comprehensive ECG signal analysis. The proposed algorithm suggested an automatic recognition of QRS complexes in ECG rhythm. This method is designed based on several filter structure composes low pass, difference and summation filters. The filtered signal is fed to an adaptive threshold function to detect QRS complexes. The algorithm was validated and results were checked with experimental data based on sensitivity test.

Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method

2019 ◽  
Vol 71 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Jianhua Cai
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Accurate Information ◽  
Threshold Method ◽  
Content Type ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Soft Threshold ◽  
Unbiased Risk

Purpose This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which are usually used in gear fault diagnosis. Design/methodology/approach A new threshold function and a new determined method of threshold for each layer are proposed. The principle and the implementation of the algorithm are given. The simulated signal and the measured gear fault signal are analyzed, and the obtained results are compared with those from wavelet soft-threshold method, wavelet hard-threshold method and wavelet modulus maximum method. Findings The presented wavelet adaptive threshold method overcomes the defects of the traditional wavelet threshold method, and it can effectively eliminate the noise hidden in the gear fault signal at different decomposition scales. It provides more accurate information for the further fault diagnosis. Originality/value A new threshold function is adopted and the multi-resolution unbiased risk estimation is used to determine the adaptive threshold, which overcomes the defect of the traditional wavelet method.

Sign in / Sign up

Export Citation Format

Share Document