scholarly journals Image Edge-Segmentation Techniques : A Review

1999 ◽  
pp. 252-257
Author(s):  
Rana Riad K. Al-Taie ◽  
Basma Jumaa Saleh ◽  
Lamees Abdalhasan Salman
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
The Other ◽  
Detection Methods ◽  
Mean Square ◽  
Application Area ◽  
Signal To Noise ◽  
Automatic Pattern Recognition ◽  
Image Edge ◽  
Edge Based

Image segmentation is commonly applied technique in different domains such as automatic pattern recognition, image retrieval based content, machine vision, face detection, medical imaging, and object detection. Image segmentation involves classifying or identifying sub patterns in a given image. Many of algorithms and techniques for image segmentation have been proposed to optimize segmentation problems in a specific application area. In this work, different image segmentation techniques had been applied (threshold based, region based segmentation and edge based preserving methods. This Experiment have been done using MATLAB R2018b. Different edge detection methods such as Sobel, Prewitt, Roberts, Laplacian, Kiresh and Canny methods are performed on the benchmark image and the performance is analyzed with respect to the standard measure peak signal-to-noise ratio (PSNR), and mean square error. The results present that the Laplacian method is more effective than the other methods.

scholarly journals Applying Ateb–Gabor Filters to Biometric Imaging Problems

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 717
Author(s):  
Mariia Nazarkevych ◽  
Natalia Kryvinska ◽  
Yaroslav Voznyi
Keyword(s):  
Wavelet Transformation ◽  
Symmetric Functions ◽  
Gabor Filter ◽  
Signal To Noise Ratio ◽  
The Other ◽  
Mean Square ◽  
Image Transformation ◽  
Gabor Filtering ◽  
Signal To Noise ◽  
Filtration Method

This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well.

Weak Transient Electromagnetic Radiation Signal Detection Method Considering the New Watershed Image Segmentation Algorithm

2019 ◽  
Vol 34 (03) ◽  
pp. 2054009
Author(s):  
Wenjun Huo ◽  
Peng Chu ◽  
Kai Wang ◽  
Liangting Fu ◽  
Zhigang Niu ◽  
...  
Keyword(s):  
Electromagnetic Radiation ◽  
Cross Correlation ◽  
Detection Efficiency ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Detection Methods ◽  
Signal To Noise ◽  
Long Distance ◽  
Transient Electromagnetic ◽  
Noise Ratio

In order to study the detection methods of weak transient electromagnetic radiation signals, a detection algorithm integrating generalized cross-correlation and chaotic sequence prediction is proposed in this paper. Based on the dual-antenna test and cross-correlation information estimation method, the detection of aperiodic weak discharge signals under low signal-to-noise ratio is transformed into the estimation of periodic delay parameters, and the noise is reduced at the same time. The feasibility of this method is verified by simulation and experimental analysis. The results show that under the condition of low signal-to-noise ratio, the integrated method can effectively suppress the influence of 10 noise disturbances. It has a high detection probability for weak transient electromagnetic radiation signals, and needs fewer pulse accumulation times, which improves the detection efficiency and is more suitable for long-distance detection of weak electromagnetic radiation sources.

scholarly journals Geometric Approach to Analytic Marginalisation of the Likelihood Ratio for Continuous Gravitational Wave Searches

Universe ◽  
2021 ◽  
Vol 7 (6) ◽  
pp. 174
Author(s):  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Likelihood Ratio ◽  
Signal To Noise Ratio ◽  
Geometric Approach ◽  
The Other ◽  
Efficient Computation ◽  
Likelihood Ratios ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Optimal Signal

The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as a function of the orientation of two vectors; one representing the optimal signal-to-noise ratio, and the other representing the maximised likelihood ratio or F-statistic. Analytic marginalisation over the angle between the vectors yields a marginalised likelihood ratio, which is a function of the F-statistic. Further analytic marginalisation over the optimal signal-to-noise ratio is explored using different choices of prior. Monte-Carlo simulations show that the marginalised likelihood ratios had identical detection power to the F-statistic. This approach demonstrates a route to viewing the F-statistic in a Bayesian context, while retaining the advantages of its efficient computation.

Peak Signal-to-Noise Ratio Evaluation of Server Display Monitors and Client Display Monitors in a Digital Subtraction Angiography Devices

2020 ◽  
Vol 3 (1) ◽  
pp. 33-41
Author(s):  
Hwunjae Lee ◽  
◽  
Junhaeng Lee ◽  
Keyword(s):  
Signal To Noise Ratio ◽  
Information Loss ◽  
The Other ◽  
Digital Subtraction ◽  
Signal Distortion ◽  
Signal To Noise ◽  
Image Storage ◽  
And Performance ◽  
Pacs Server ◽  
Display Monitor

This study evaluated PSNR of server display monitor and client display monitor of DSA system. The signal is acquired and imaged during the surgery and stored in the PACS server. After that, distortion of the original signal is an important problem in the process of observation on the client monitor. There are many problems such as noise generated during compression and image storage/transmission in PACS, information loss during image storage and transmission, and deterioration in image quality when outputting medical images from a monitor. The equipment used for the experiment in this study was P's DSA. We used two types of monitors in our experiment, one is P’s company resolution 1280×1024 pixel monitor, and the other is W’s company resolution 1536×2048 pixel monitor. The PACS Program used MARO-view, and for the experiment, a PSNR measurement program using Visual C++ was implemented and used for the experiment. As a result of the experiment, the PSNR value of the kidney angiography image was 26.958dB, the PSNR value of the lung angiography image was 28.9174 dB, the PSNR value of the heart angiography image was 22.8315dB, and the PSNR value of the neck angiography image was 37.0319 dB, and the knee blood vessels image showed a PSNR value of 43.2052 dB, respectively. In conclusion, it can be seen that there is almost no signal distortion in the process of acquiring, storing, and transmitting images in PACS. However, it suggests that the image signal may be distorted depending on the resolution and performance of each monitor. Therefore, it will be necessary to evaluate the performance of the monitor and to maintain the performance.

scholarly journals АНАЛИЗ И ПРОГНОЗИРОВАНИЕ ЭФФЕКТИВНОСТИ ФИЛЬТРАЦИИ ОДНОМЕРНЫХ СИГНАЛОВ НА ОСНОВЕ ДИСКРЕТНОГО КОСИНУСНОГО ПРЕОБРАЗОВАНИЯ

2019 ◽  
pp. 19-29
Author(s):  
Сергей Клавдиевич Абрамов ◽  
Виктория Валерьевна Абрамова ◽  
Сергей Станиславович Кривенко ◽  
Владимир Васильевич Лукин
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Statistical Parameter ◽  
Mean Square ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Standard Size ◽  
Filter Parameter ◽  
Filtering Efficiency ◽  
Threshold Setting

The article deals with the analysis of the efficiency and expedience of applying filtering based on the discrete cosine transform (DCT) for one-dimensional signals distorted by white Gaussian noise with a known or a priori estimated variance. It is shown that efficiency varies in wide limits depending upon the input ratio of signal-to-noise and degree of processed signal complexity. It is offered a method for predicting filtering efficiency according to the traditional quantitative criteria as the ratio of mean square error to the variance of additive noise and improvement of the signal-to-noise ratio. Forecasting is performed based on dependences obtained by regression analysis. These dependencies can be described by simple functions of several types parameters of which are determined as the result of least mean square fitting. It is shown that for sufficiently accurate prediction, only one statistical parameter calculated in the DCT domain can be preliminarily evaluated (before filtering), and this parameter can be calculated in a relatively small number of non-overlapping or partially overlapping blocks of standard size (for example, 32 samples). It is analyzed the variations of efficiency criteria variations for a set of realizations; it is studied factors that influence prediction accuracy. It is demonstrated that it is possible to carry out the forecasting of filtering efficiency for several possible values of the DCT-filter parameter used for threshold setting and, then, to recommend the best value for practical use. An example of using such an adaptation procedure for the filter parameter setting for processing the ECG signal that has not been used in the determination of regression dependences is given. As a result of adaptation, the efficiency of filtering can be essentially increased – benefit can reach 0.5-1 dB. An advantage of the proposed procedures of adaptation and prediction is their universality – they can be applied for different types of signals and different ratios of signal-to-noise.

scholarly journals Audio steganography based on least significant bits algorithm with 4D grid multi-wing hyper-chaotic system

2022 ◽  
Vol 12 (1) ◽  
pp. 320
Author(s):  
Hussein Abdulameer Abdulkadhim ◽  
Jinan Nsaif Shehab
Keyword(s):  
Mean Square Error ◽  
Chaotic System ◽  
Signal To Noise Ratio ◽  
Secret Message ◽  
Security Level ◽  
Mean Square ◽  
Signal To Noise ◽  
Protection Level ◽  
Voice Message ◽  
Least Significant Bits

Although variety in hiding methods used to protect data and information transmitted via channels but still need more robustness and difficulty to improve protection level of the secret messages from hacking or attacking. Moreover, hiding several medias in one media to reduce the transmission time and band of channel is the important task and define as a gain channel. This calls to find other ways to be more complexity in detecting the secret message. Therefore, this paper proposes cryptography/steganography method to hide an audio/voice message (secret message) in two different cover medias: audio and video. This method is use least significant bits (LSB) algorithm combined with 4D grid multi-wing hyper-chaotic (GMWH) system. Shuffling of an audio using key generated by GMWH system and then hiding message using LSB algorithm will provide more difficulty of extracting the original audio by hackers or attackers. According to analyses of obtained results in the receiver using peak signal-to-noise ratio (PSNR)/mean square error (MSE) and sensitivity of encryption key, the proposed method has more security level and robustness. Finally, this work will provide extra security to the mixture base of crypto-steganographic methods.

scholarly journals Artefact Removal from EEG Signals using Total Variation De-noising

2020 ◽  
Vol 9 (5) ◽  
pp. 2357-2361
Keyword(s):  
Root Mean Square Error ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Health Issues ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Medical Practitioners ◽  
Artefact Removal ◽  
Two Parameters

Artefacts removing (de-noising) from EEG signals has been an important aspect for medical practitioners for diagnosis of health issues related to brain. Several methods have been used in last few decades. Wavelet and total variation based de-noising have attracted the attention of engineers and scientists due to their de-noising efficiency. In this article, EEG signals have been de-noised using total variation based method and results obtained have been compared with the results obtained from the celebrated wavelet based methods . The performance of methods is measured using two parameters: signal-to-noise ratio and root mean square error. It has been observed that total variation based de-noising methods produce better results than the wavelet based methods.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

scholarly journals Discovery of allenyl acetylene, H2CCCHCCH, in TMC-1

2021 ◽  
Vol 647 ◽  
pp. L3 ◽  
Author(s):  
J. Cernicharo ◽  
C. Cabezas ◽  
M. Agúndez ◽  
B. Tercero ◽  
N. Marcelino ◽  
...  
Keyword(s):  
Column Density ◽  
State Of The Art ◽  
Rotational Temperature ◽  
Signal To Noise Ratio ◽  
Laboratory Data ◽  
The Other ◽  
Signal To Noise ◽  
Chemical Models ◽  
Upper Limit

We present the discovery in TMC-1 of allenyl acetylene, H2CCCHCCH, through the observation of nineteen lines with a signal-to-noise ratio ∼4–15. For this species, we derived a rotational temperature of 7 ± 1 K and a column density of 1.2 ± 0.2 × 1013 cm−2. The other well known isomer of this molecule, methyl diacetylene (CH3C4H), has also been observed and we derived a similar rotational temperature, Tr = 7.0 ± 0.3 K, and a column density for its two states (A and E) of 6.5 ± 0.3 × 1012 cm−2. Hence, allenyl acetylene and methyl diacetylene have a similar abundance. Remarkably, their abundances are close to that of vinyl acetylene (CH2CHCCH). We also searched for the other isomer of C5H4, HCCCH2CCH (1.4-Pentadiyne), but only a 3σ upper limit of 2.5 × 1012 cm−2 to the column density can be established. These results have been compared to state-of-the-art chemical models for TMC-1, indicating the important role of these hydrocarbons in its chemistry. The rotational parameters of allenyl acetylene have been improved by fitting the existing laboratory data together with the frequencies of the transitions observed in TMC-1.

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