New computational solution to quantify synthetic material porosity from optical microscopic images

Albuquerque, Victor Hugo C. de; Filho, Pedro Pedrosa Rebouças; Cavalcante, T. S.; Tavares, João Manuel R. S.

doi:10.1111/j.1365-2818.2010.03384.x

Cited by 24 publications

(8 citation statements)

References 31 publications

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“…The Canny algorithm was designed to have three main properties: minimum error detection, good border locations and minimal response time. Edge detection has been used in many applications for object segmentation [2,35,44,45], and the Canny detector has been commonly used to find objects, and Hough transform to recognize the right objects.…”

Section: Based On the Hough Transform And Canny Edge Detectormentioning

confidence: 99%

Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Gomes

Rebouças

Neto

et al. 2016

Neural Comput & Applic

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The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 ls to recognize a sign, while kNN took 11,721 ls and SVM 12,595 ls. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.

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Section: Based On the Hough Transform And Canny Edge Detectormentioning

confidence: 99%

Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Gomes

Rebouças

Neto

et al. 2016

Neural Comput & Applic

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“…The electrolytic etching with 10% KOH solution reveals mainly sigma phase. The amount (% volumetric fraction) of sigma phase presented in each sample was determined using a computational tool, which is based on techniques of image processing and analysis and on an artificial neural network, that has been used to characterize microstructures in previous studies [38][39][40][41][42][43]. Forty images were acquired from each material sample and the volume fraction was determined adopting a confidence interval of 95%.…”

Section: Methodsmentioning

confidence: 99%

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Silva

Marinho

Filho

et al. 2016

Metals

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Duplex stainless steels present excellent mechanical and corrosion resistance properties. However, when heat treated at temperatures above 600 • C, the undesirable tertiary sigma phase is formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, the material toughness being compromised when the amount of this phase is not less than 4%. This work aimed to develop a solution for the detection of this phase in duplex stainless steels through the computational classification of induced magnetic field signals. The proposed solution is based on an Optimum Path Forest classifier, which was revealed to be more robust and effective than Bayes, Artificial Neural Network and Support Vector Machine based classifiers. The induced magnetic field was produced by the interaction between an applied external field and the microstructure. Samples of the 2205 duplex stainless steel were thermal aged in order to obtain different amounts of sigma phases (up to 18% in content). The obtained classification results were compared against the ones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracture surface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved a classification accuracy superior to 95% and was revealed to be robust to signal noise, being therefore a valid testing tool to be used in this domain.

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“…The ANN (shallow) predecessors have been likewise proposed for the investigation of microscopy images in materials science [25]. But the feature complexity of real-world images and the need for their automatic representation make DL a more performing candidate for the task.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Applied on Copper Surface Coatings Based on Polyvinyl Alcohol and Silver Nanoparticles<strong> </strong>

Samide¹,

Stoean²,

Stoean³

et al. 2018

Preprint

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In order to design effective protective coatings against corrosion, the polyvinyl alcohol (PVA) as compound and composite with silver nanoparticles (nAg/PVA) were electrodeposited on copper surface employing electrochemical techniques such as linear potentiometry and cyclic voltammetry. A new paradigm was used to distinguish the features of coatings, i.e., a Deep Convolutional Neural Network (CNN) was implemented to automatically and hierarchically extract the discriminative characteristics from the information given by optical microscopy images. The main arguments that invoke a CNN implementation in the surface science of materials are the following: artificial intelligence techniques can be successfully applied to learn differences between surface coatings; based on their popularity for image processing, CNN can model images related to the problem of coatings; deep learning is able to extract the features that are distinguishable between material surfaces. To provide an overview of the copper surface, CNN was applied on microscope slides (CNN@microscopy) and inherently learnt distinctive characteristics for each class of surface morphology. The material surface morphology controlled by CNN without the interference of the human factor was successfully conducted, in our study, to extract the similarities/differences between unprotected and protected surfaces to establish the PVA and nAg/PVA performance to retard the copper corrosion.

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New computational solution to quantify synthetic material porosity from optical microscopic images

Cited by 24 publications

References 31 publications

Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Deep Convolutional Neural Network Applied on Copper Surface Coatings Based on Polyvinyl Alcohol and Silver Nanoparticles<strong> </strong>

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