scholarly journals Application of Pulsed Thermography and Post-processing Techniques for CFRP Industrial Components

2021 ◽  
Vol 40 (2) ◽  
Author(s):  
F. W. Panella ◽  
A. Pirinu
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Industrial Process ◽  
Research Field ◽  
Pulsed Thermography ◽  
Inspection Method ◽  
Type Size ◽  
Industrial Process Control ◽  
Processing Techniques ◽  
Damage Type

AbstractSeveral studies demonstrate the effectiveness of pulsed thermography for detection and visualization of sub-superficial flaws in composites. Continuous improvement of thermal data manipulation makes active thermography an attractive and powerful inspection method for industrial process control and maintenance aims. Therefore, temperature image-processing is the major ongoing challenge in the thermographic research field. However, the particular interest for thermographic inspections is to be more addressed to its simple and relatively fast industrial application; an appropriate image processing tool should be implemented and verified on industrial components, containing manufacturing and in-service defects. In the proposed research, well-established and previously proposed methods were analysed and compared for different defect typology inside three CFRP components. The main goal is not solely focused on establishing the suitable data processing approach, providing detection limits of processed data in terms of damage type, size and distribution. The aim of proposed work is to present detailed examples of thermal imaging methods applied on similar critical defects, evaluating different results among methods in terms of defects mapping capabilities and Tanimoto evaluation criterion, coupled also with the signal-to-noise ratio as assessment of defect detectability.

scholarly journals COMPARATIVE STUDY OF THE DIFFERENT VERSIONS OF THE GENERAL IMAGE QUALITY EQUATION

2019 ◽  
Vol IV-2/W5 ◽  
pp. 493-500 ◽  
Author(s):  
A. Q. Valenzuela ◽  
J. C. G. Reyes
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Paradigm Shift ◽  
Rating Scale ◽  
Signal To Noise Ratio ◽  
National Image ◽  
Image Processing Techniques ◽  
Quality Rating ◽  
Processing Techniques ◽  
Image Quality Rating

<p><strong>Abstract.</strong> The General Image Quality Equation (GIQE) is an analytical tool derived by regression modelling that is routinely employed to gauge the interpretability of raw and processed images, computing the most popular quantitative metric to evaluate image quality; the National Image Interpretability Rating Scale (NIIRS). There are three known versions of this equation; GIQE&amp;nbsp;3, GIQE&amp;nbsp;4 and GIQE&amp;nbsp;5, but the last one is scarcely known. The variety of versions, their subtleties, discontinuities and incongruences, generate confusion and problems among users. The first objective of this paper is to identify typical sources of confusion in the use of the GIQE, suggesting novel solutions to the main problems found in its application and presenting the derivation of a continuous form of GIQE&amp;nbsp;4, denominated GIQE&amp;nbsp;4C, that provides better correlation with GIQE&amp;nbsp;3 and GIQE&amp;nbsp;5. The second objective of this paper is to compare the predictions of GIQE&amp;nbsp;4C and GIQE&amp;nbsp;5, regarding the maximum image quality rating that can be achieved by image processing techniques. It is concluded that the transition from GIQE&amp;nbsp;4 to GIQE&amp;nbsp;5 is a major paradigm shift in image quality metrics, because it reduces the benefit of image processing techniques and enhances the importance of the raw image and its signal to noise ratio.</p>

scholarly journals Agro Image De-Noising (Aid) for Enhanced Agricultural Images

2019 ◽  
Vol 9 (2) ◽  
pp. 2474-2478
Keyword(s):  
Image Processing ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Photographic Images ◽  
Spline Wavelets ◽  
Soft Threshold ◽  
Noise Ratio ◽  
Processing Techniques

Several Noises may be present in acquired images. This is an undesired feature for image processing techniques that analyze these images. Image de-noising helps improve efficiency of image processing. Many image de-noising methods have been proposed and exist in literature. Image de-noising methods for agricultural images have been proposed to a lesser extent when compared to the bright medical or photographic images. This paper proposes Agricultural Image De-noising (AID) which uses a discrete wavelet transform (DWT) to eliminate noise in agricultural images. This study uses specific kind of wavelet family spline wavelet transforms with appropriate decomposition level and the wavelet coefficients are analysed with hard and soft threshold methods. The denoised image using various spline wavelets is compared of hard threshold and soft threshold are assessed. The performance of AID is calculated using the peak signal to noise ratio (PSNR) and signal to noise ratio (SNR).

Videoscope-based inspection of turbofan engine blades using convolutional neural networks and image processing

2019 ◽  
Vol 18 (5-6) ◽  
pp. 2020-2039 ◽  
Author(s):  
Yong-Ho Kim ◽  
Jung-Ryul Lee
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Aircraft Engine ◽  
Recognition Algorithm ◽  
Inspection Method ◽  
Image Processing Techniques ◽  
Damage Recognition ◽  
Using Data ◽  
Processing Techniques

A typical aircraft engine consists of fans, compressors, turbines, and so on, and each is made of multiple layers of blades. Discovering the site of damages among the large number of blades during aircraft engine maintenance is quite important. However, it is impossible to look directly into the engine unless it is disassembled. For this reason, optical equipment such as a videoscope is used to visually inspect the blades of an engine through inspection holes. The videoscope inspection method has some obvious drawbacks such as the long-time attention on microscopic video feed and high labor intensity. In this research, we developed a damage recognition algorithm using convolutional neural networks and some image-processing techniques related to feature point extraction and matching in order to improve the videoscope inspection method. The image-processing techniques were mainly used for the preprocessing of the videoscope images, from which a suspected damaged region is selected after the preprocessing. The suspected region is finally classified as damaged or normal by the pre-trained convolutional neural networks. We trained the convolutional neural networks 2000 times by using data from 380 images and calculated the classification accuracy using data from 40 images. After repeating the above procedure 50 times with the data randomly divided into training and test groups, an average classification accuracy of 95.2% for each image and a damage detectability of 100% in video were obtained. For verification of the proposed approach, the convolutional neural network part was compared with the traditional neural network, and the preprocessing was compared with the region proposal network of the faster region–based convolutional neural networks. In addition, we developed a platform based on the developed damage recognition algorithm and conducted field tests with a videoscope for a real engine. The damage detection AI platform was successfully applied to the inspection video probed in an in-service engine.

Automated Crowd Controlling System Using Image Processing and Video Processing Technique to Avoid Stamped

2019 ◽  
Vol 10 (3) ◽  
pp. 19-26
Author(s):  
Syeda Ruheena Quadri
Keyword(s):  
Image Processing ◽  
Video Processing ◽  
Measurement Techniques ◽  
Processing Technique ◽  
Research Field ◽  
Controlling System ◽  
Crowd Control ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Processing Techniques

Crowd control is needed to prevent the outbreak of disorder and prevent possible stampedes. An automated detection of people crowds from images has become a very important research field. Due to the importance of the topic, many researchers tried to solve this problem using CCTV street cameras. There are still significant problems in managing public pedestrian transport areas such as railway stations, stadiums, shopping malls, and religious gatherings. Using CCTV cameras, some image processing techniques are suitable for an automatic crowd monitoring system. The feasibility of such a system has been tested by analyzing the crowd behavior, crowd density and motion. Traditional measurement techniques, based on manual observations, are not suitable for comprehensive data collection of patterns of density and movement. Real-time monitoring is tedious and tiring, but critical for safety. The author has investigated a number of techniques for crowd density estimation, movement estimation, incident detection and their merits using image processing.

Development of Methodology for Enhancing Visual Bridge Condition Assessment Using Image Processing Techniques

2012 ◽  
Vol 256-259 ◽  
pp. 1563-1570
Author(s):  
Hui Ju Wi ◽  
Jae Ho Lee ◽  
Michael Blumenstein ◽  
Hong Guan ◽  
Yew Chaye Loo
Keyword(s):  
Image Processing ◽  
Cost Effective ◽  
Accurate Estimation ◽  
Bridge Management ◽  
Bridge Inspection ◽  
Inspection Method ◽  
Subjective Judgment ◽  
Image Processing Techniques ◽  
Bridge Condition Assessment ◽  
Processing Techniques

Many bridge authorities have implemented Bridge Information Systems (BISs) or Bridge Management Systems (BMSs) to effectively manage their routine inspection information. The success of a BMS is highly dependent on the quality of bridge inspection outcomes and accurate estimation of future bridge condition ratings. To ensure such successful outcomes, a BMS must (1) contain reliable, consistent and accurate condition data from routine bridge inspections; and (2) encompass reliable deterioration modelling that overcomes the shortcomings of a lack of historical bridge inspection records. However published literature demonstrates that several limitations exist particularly in terms of inconsistency of inspection outcomes due to subjective judgment. To minimise such limitations, this paper presents a feasibility study for the enhancement of the current visual bridge inspection method using optical image processing techniques. The development work consists of image processing and knowledge-based approaches. It is anticipated that the proposed method is capable of minimising the shortcomings of subjective judgment on condition rating assessment and providing cost effective solutions to bridge agencies. Ultimately, the proposed bridge inspection methodology can provide consistent and accurate evaluation on the condition states of bridge elements. This in turn will lead to more reliable predictions of long-term bridge performance.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

Specific Labeling of Protein Domains with Antibody Fragments

1981 ◽  
Vol 39 ◽  
pp. 416-417
Author(s):  
U. Aebi ◽  
L.E. Buhle ◽  
W.E. Fowler
Keyword(s):  
Image Processing ◽  
Specific Protein ◽  
Antibody Fragments ◽  
Supramolecular Organization ◽  
Two Dimensional ◽  
Ordered Structures ◽  
Virus Capsids ◽  
Processing Techniques

Many important supramolecular structures such as filaments, microtubules, virus capsids and certain membrane proteins and bacterial cell walls exist as ordered polymers or two-dimensional crystalline arrays in vivo. In several instances it has been possible to induce soluble proteins to form ordered polymers or two-dimensional crystalline arrays in vitro. In both cases a combination of electron microscopy of negatively stained specimens with analog or digital image processing techniques has proven extremely useful for elucidating the molecular and supramolecular organization of the constituent proteins. However from the reconstructed stain exclusion patterns it is often difficult to identify distinct stain excluding regions with specific protein subunits. To this end it has been demonstrated that in some cases this ambiguity can be resolved by a combination of stoichiometric labeling of the ordered structures with subunit-specific antibody fragments (e.g. Fab) and image processing of the electron micrographs recorded from labeled and unlabeled structures.

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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