On the Evaluation of Texture and Color Features for Nondestructive Corrosion Detection

Medeiros, Fátima N. S. de; Ramalho, Geraldo Luis Bezerra; Bento, Mariana; Medeiros, Luiz C. L.

doi:10.1155/2010/817473

Cited by 73 publications

(35 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our feature extraction method is inspired by the gray-level cooccurrence matrix (GLCM) introduced by Haralick et al (1973) and often used in texture analysis (Choi, 2005;Medeiros et al, 2010). The proposed method, namely Spatial Interdependence Matrix (SIM), uses the cooccurrence statistics to analyse the structural information based on how the human visual system (HVS) interprets scenes.…”

Section: The Proposed Feature Extraction Methodsmentioning

confidence: 99%

Lung disease detection using feature extraction and extreme learning machine

Ramalho

Filho

Medeiros

et al. 2014

Rev. Bras. Eng. Bioméd.

View full text Add to dashboard Cite

Introduction: The World Health Organization estimates that by 2030 the Chronic Obstructive Pulmonary Disease (COPD) will be the third leading cause of death worldwide. Computerized Tomography (CT) images of lungs comprise a number of structures that are relevant for pulmonary disease diagnosis and analysis. Methods: In this paper, we employ the Adaptive Crisp Active Contour Models (ACACM) for lung structure segmentation. And we propose a novel method for lung disease detection based on feature extraction of ACACM segmented images within the cooccurrence statistics framework. The spatial interdependence matrix (SIM) synthesizes the structural information of lung image structures in terms of three attributes. Finally, we perform a classifi cation experiment on this set of attributes to discriminate two types of lung diseases and health lungs. We evaluate the discrimination ability of the proposed lung image descriptors using an extreme learning machine neural network (ELMNN) comprising 4-10 neurons in the hidden layer and 3 neurons in the output layer to map each pulmonary condition. This network was trained and validated by applying a holdout procedure. Results:The experimental results achieved 96% accuracy demonstrating the effectiveness of the proposed method on identifying normal lungs and diseases as COPD and fi brosis. Conclusion: Our results lead to conclude that the method is suitable to integrate clinical decision support systems for pulmonary screening and diagnosis.

show abstract

Section: The Proposed Feature Extraction Methodsmentioning

confidence: 99%

Lung disease detection using feature extraction and extreme learning machine

Ramalho

Filho

Medeiros

et al. 2014

Rev. Bras. Eng. Bioméd.

View full text Add to dashboard Cite

show abstract

“…On the outer surface of pipes and structures, [9], [10] utilised statistic measurements of the picture pixels in quantifying pitting while [11] corrosion detection was based on the morphology of the surface such as colour, shape surface roughness, etc. Medeiros [12] also proposed a method based on describing the surface texture of the material obtained from the co-occurrence matrix and colour. The learning-based approach has also been employed in image processing, as seen in the typical pattern recognition systems like a neural network [13] [14] [15].…”

Section: Related Workmentioning

confidence: 99%

Robotic Model for Unmanned Crack and Corrosion Inspection

Oyekola¹,

Mohamed*²,

Pumwa³

2019

IJITEE

View full text Add to dashboard Cite

After prolonged usage of materials, the formation of cracks and corrosion initiates due to stress, loading condition, the environment of operation, etc. and this affects the structural integrity of structures. Periodic inspection of structures is usually planned, especially in industries where the impact of failure could be devastating, such as oil and gas pipelines, storage tanks, vessels, and airplanes, etc. which are just a few amongst others. This inspection is often aimed at detecting cracks and corrosion of internal and external components using several forms of non-destructive testing mechanism usually performed by a specialist at a high rate. To reduce the cost of inspection as well as downtime due to inspections and maintenance, deployments of mobile robots with fault tracking and identification purpose are steadily increasing. This paper, therefore, details the implementation of an image processing technique using MATLAB to identify defects of structural elements.

show abstract

“…The theoretical groundwork for the article is based on a number of studies dealing with road safety and technical expert examination of a car body [2][3][4][5][6], as well as atmospheric corrosion processes that take place therein [7,8]. The studies that deal with color reproduction and image analysis [9], as well as color of rust spots and conversion of color channel vector for digital photos [10][11][12] can also be highlighted.…”

Section: Introductionmentioning

confidence: 99%

Contactless Automated Express Evaluation of Damages to a Car Body by Visual Parameters

2019

View full text Add to dashboard Cite

Explosive development of computer technologies and their availability made it possible to extensively focus nowadays on emerging state-of-the-art technologies, digitalization, artificial intelligence, and automated systems, including in the field of road safety. It would be reasonable to implement some technical devices in this respect to remove human factor and automate some procedures completed at the scene of a road accident. Automatically filled up road accident inspection records and, mainly, diagrams of the accident will reduce time required for the examining inspector and remove human factor. Ultimately, an automated road accident data sheet is suggested to be established. To tackle the issues above requires a technique to determine whether the produced damages to the car body result from the same road accident. The fact remains that there are circumstances when even vehicle trace examination would not do the job, in case of multiple corrosive damage to the body. In view of the above, a technique designed to determine whether the damages produced are caused at the same point of time gains its ground. A technique for a time-related corrosion examination is offered herein to cut expenditures for diagnostics and expert examination of road accidents. That will also eliminate the matters of argument with respect to the road accident evaluation in court. Among added benefits of the technique are that it is simple, quick to implement, and requires no human involvement. It is a well-established fact that each chemical element or a mixture of substances has its own timeinvariant color attributes which allows to determine availability of one or another substance during corrosion of metal surfaces, by emission from the surface in question.

show abstract

On the Evaluation of Texture and Color Features for Nondestructive Corrosion Detection

Cited by 73 publications

References 13 publications

Lung disease detection using feature extraction and extreme learning machine

Lung disease detection using feature extraction and extreme learning machine

Robotic Model for Unmanned Crack and Corrosion Inspection

Contactless Automated Express Evaluation of Damages to a Car Body by Visual Parameters

Contact Info

Product

Resources

About