scholarly journals Improving GPR Imaging of the Buried Water Utility Infrastructure by Integrating the Multidimensional Nonlinear Data Decomposition Technique into the Edge Detection

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3148
Author(s):  
Chih-Sung Chen ◽  
Yih Jeng
Keyword(s):  
Edge Detection ◽  
Hot Spring ◽  
Signal To Noise Ratio ◽  
Data Interpretation ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Data Reconstruction ◽  
Water Utility ◽  
Edge Detecting

Although ground-penetrating radar (GPR) is effective to detect shallow-buried objects, it still needs more effort for the application to investigate a buried water utility infrastructure. Edge detection is a well-known image processing technique that may improve the resolution of GPR images. In this study, we briefly review the theory of edge detection and discuss several popular edge detectors as examples, and then apply an enhanced edge detecting method to GPR data processing. This method integrates the multidimensional ensemble empirical mode decomposition (MDEEMD) algorithm into standard edge detecting filters. MDEEMD is implemented mainly for data reconstruction to increase the signal-to-noise ratio before edge detecting. A quantitative marginal spectrum analysis is employed to support the data reconstruction and facilitate the final data interpretation. The results of the numerical model study followed by a field example suggest that the MDEEMD edge detector is a competent method for processing and interpreting GPR data of a buried hot spring well, which cannot be efficiently handled by conventional techniques. Moreover, the proposed method should be readily considered a vital tool for processing other kinds of buried water utility infrastructures.

scholarly journals An Improved ant based Algorithm and its Implementation for the Broken Edge Linking Problem

2020 ◽  
Vol 9 (5) ◽  
pp. 1792-1798
Keyword(s):  
Edge Detection ◽  
Processing Technique ◽  
Detection Technique ◽  
Image Processing Technique ◽  
Ant System ◽  
Edge Linking ◽  
Overall Performance ◽  
Robust Image ◽  
Improved Technique ◽  
Significant Factors

As a robust image processing technique, the broken edge linking technique is considered to be the complementary one to the edge detection technique. Here in case of the edge linking problem, we form closed contours by linking the broken edges. This action indeed needed to split the areas in the image into different parts known as segment. However the traditional edge linking technique is always succeeded by the traditional edge detection technique. The traditional edge linking technique, for all time, neglects some significant part of the boundaries to consider, as a result, appropriate and perfect solutions to the edge related linking problem cannot be reached always. With this article, we presented an improved method (technique) for the edge related linking difficulty (problem), which is actually the expansion of the original old Ant System (AS) algorithm. Here in the experiment, we mainly consider the two decisive and significant factors: first one is the length of the linking edge as well as second one is the grayscale visibility of the pixels, apart from an sobel edge binary image , so that the effective solution evaluation and enhancement of the overall performance can be achieved. The experiment showed the expected result, are presented herewith this to ensure the successfulness of the projected improved technique /algorithm.

Performance Evaluation of Empirical Mode Decomposition for EEG Artifact Removal

2017 ◽  
Author(s):  
MD Erfanul Alam ◽  
Biswanath Samanta
Keyword(s):  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Fluorescent Light ◽  
Eeg Signals ◽  
Signal Processing Technique ◽  
Mode Decomposition

Electroencephalography measures the sum of the post-synaptic potentials generated by many neurons having the same radial orientation with respect to the scalp. The electroen-cephalographic signals (EEG) are weak and often contaminated with different artifacts that have biological and external sources. Reliable pre-processing of the noisy, non-linear, and non-stationary brain activity signals is needed for successful extraction of characteristic features in motor imagery based brain-computer interface (MI-BCI). In this work, a signal processing technique, namely, empirical mode decomposition (EMD), has been proposed for processing EEG signals acquired from volunteer subjects for characterization and identification of motor imagery (MI) activities. EMD has been used for removal of artifacts like electrooculography (EOG) that strongly appears in frontal electrodes of EEG and the power line noise that is mainly produced by the fluorescent light. The performance of EMD has been compared with two extensions, ensemble empirical mode decomposition (EEMD) and multivariate empirical mode decomposition (MEMD)using signal to noise ratio (SNR). The maximum SNR values found for EMD, EEMD and MEMD are 4.30, 7.64 and 10.62 respectively for the EEG signals considered.

scholarly journals Edge detection for detection of brain tumour in CT images

2018 ◽  
Vol 7 (2.25) ◽  
pp. 95
Author(s):  
T R. Thamizhvani ◽  
A Josephin Arockia Dhivya ◽  
S Akshaya ◽  
K Dhanalakshmi ◽  
R Chandrasekaran ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Edge Detection ◽  
Brain Tumour ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Standard Data ◽  
Computed Tomography Images ◽  
Edge Operator ◽  
The Brain

Brain tumour can be defined as the continuous and uncontrolled growth of the cells in the regions of brain. Analysis and detection of brain tumours from the computed tomography images can be performed by various image processing algorithms. Edge detection is special type of image processing technique, which uses operators for functioning. The Computed Tomography images are obtained from the standard data-base which undergoes pre-processing technique. Contrast adjustment is performed to enhance the region of brain tumour. Edge operators of different types are applied to the images for identification of the boundary of the brain tumour region. Appropriate edge operator for de-termination of the boundary is defined by comparing the image quality and accuracy parameters. These parameters illustrate that canny oper-ator is described to be more definite for the detection and analysis of the boundary and region of brain tumour in Computed Tomography images.   

Image Processing Techniques to Analyse a 3D Model

2015 ◽  
Vol 787 ◽  
pp. 922-926
Author(s):  
Mirji Sairaj Gururaj ◽  
A Arockia Selvakumar
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Three Dimensional ◽  
Human Perception ◽  
Spur Gear ◽  
Recognition System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Three Dimensional Model ◽  
Detection Techniques

The Image processing technique incorporates human perception and intelligence which makes this field more interesting to the research community. The edge detection process is the most important step in image recognition system. In this paper a simple three dimensional model is created by taking the best edge detected image followed by comparison with various edge detection techniques using Labview software. HereCatia model of spur gear isdrawn by observing and analysing best suited edge detected image in order to make the design more precise in edges and geometry and also to make object recognition simple and further scope is given to design a Catia model using dimensional parameters with the help of vision assistant tool.

A New Image Processing Technique for STEM

1974 ◽  
Vol 32 ◽  
pp. 432-433
Author(s):  
Yasushi Kokubo ◽  
Hirotami Koike ◽  
Teruo Someya
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Elastic Scattering ◽  
Field Emission ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Energy Analyzer ◽  
Scanning Microscope ◽  
Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Sign in / Sign up

Export Citation Format

Share Document