Quantitative characterization of multi-phase materials by digital image processing

An automatic image processing and analysis technique has been developed for quantitative characterization of multi-phase materials. For the development of this technique is used the Khoros system that offers the basic morphological tools and a flexible, visual programming language. These techniques are implemented in a highly user oriented image processing environment that allows the user to adapt each step of the processing to his special requirements.To illustrate the implementation and performance of this technique, images of two different materials are processed for microstructure characterization. The result is presented through the determination of volume fraction of the different phases or precipitates.

Quantitative fractography under light microscopy: A digital image processing approach

This work is an example of the improvement on quantitative fractography by means of digital image processing and light microscopy. Two techniques are presented to investigate the quantitative fracture behavior of Ti-4Al-4V heat-treated alloy specimens, under Charpy impact testing. The first technique is the Minkowski method for fractal dimension measurement from surface profiles, revealing the multifractal character of Ti-4Al-4V fracture. It was not observed a clear positive correlation of fractal values against Charpy energies for Ti-4Al-4V alloy specimens, due to their ductility, microstructural heterogeneities and the dynamic loading characteristics at region near the V-notch. The second technique provides an entire elevation map of fracture surface by extracting in-focus regions for each picture from a stack of images acquired at successive focus positions, then computing the surface roughness. Extended-focus reconstruction has been used to explain the behavior along fracture surface. Since these techniques are based on light microscopy, their inherent low cost is very interesting for failure investigations.

Development of phantom for radiographic image optimization of standard patient in the lateral view of chest and skull examination

We present the construction of a homogeneous phantom to be used in simulating the scattering and absorption of X-rays by a standard patient chest and skull when irradiated laterally. This phantom consisted of Incite and aluminium plates with their thickness determined by a tomographic exploratory method applied to the anthropomorphic phantom. Using this phantom, an optimized radiographic technique was established for chest and skull of standard sized patient in lateral view. Images generated with this optimized technique demonstrated improved image quality and reduced radiation doses. (c) 2006 Elsevier Ltd. All rights reserved.

Rapid prototyping and inclined plane technique in the treatment of maxillofacial malformations in a fox

An approximately 9-month-old fox (Pseudalopex ventulus) was presented With malocclusion and deviation of the lower jaw to the right side. Orthodontic treatment was performed using the inclined plane technique. Virtual 3D models and prototypes of the head were based on computed tomography (CT) image data to assist in diagnosis and treatment.

Phantom development for radiographic image optimization of chest, skull and pelvis examination for nonstandard patient

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

Accuracy of zoomed digital image in the detection of periodontal bone defect: in vitro study

Objectives: (1) To evaluate the intraobserver agreement related to image interpretation and (2) to compare the accuracy of 100%, 200% and 400% zoomed digital images in the detection of simulated periodontal bone defects.Methods: Periodontal bone defects were created in 60 pig hemi-mandibles with slow-speed burs 0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm and 3.0 mm in diameter. 180 standardized digital radiographs were made using Schick sensor and evaluated at 100%, 200% and 400% zooming. The intraobserver agreement was estimated by Kappa statistic (kappa). For the evaluation of diagnostic accuracy receiver operating characteristic (ROC) analysis was performed followed by chi-square test to compare the areas under ROC curves according to each level of zooming.Results: For 100%, 200% and 400% zooming the intraobserver agreement was moderate (kappa = 0.48, kappa = 0.54 and kappa = 0.43, respectively) and there were similar performances in the discrimination capacity, with ROC areas of 0.8611 (95% CI: 0.7660-0.9562), 0.8600 (95% CI: 0.7659-0.9540), and 0.8368 (95% CI: 0.7346-0.9390), respectively, with no statistical significant differences (chi(2)-test; P = 0.8440).Conclusions: A moderate intraobserver agreement was observed in the classification of periodontal bone defects and the 100%, 200% and 400% zoomed digital images presented similar performances in the detection of periodontal bone defects.

Classification and characterization of places for mapping of environment using hierarchical neural network and omnivision

Mobile robots need autonomy to fulfill their tasks. Such autonomy is related whith their capacity to explorer and to recognize their navigation environments. In this context, the present work considers techniques for the classification and extraction of features from images, using artificial neural networks. This images are used in the mapping and localization system of LACE (Automation and Evolutive Computing Laboratory) mobile robot. In this direction, the robot uses a sensorial system composed by ultrasound sensors and a catadioptric vision system equipped with a camera and a conical mirror. The mapping system is composed of three modules; two of them will be presented in this paper: the classifier and the characterizer modules. Results of these modules simulations are presented in this paper.

Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved.

Automatic Classification of Three-Phase Flow Patterns of Heavy Oil in a Horizontal Pipe Using Support Vector Machines

The pipe flow of a viscous-oil-gas-water mixture such as that involved in heavy oil production is a rather complex thereto-fluid dynamical problem. Considering the complexity of three-phase flow, it is of fundamental importance the introduction of a flow pattern classification tool to obtain useful information about the flow structure. Flow patterns are important because they indicate the degree of mixing during flow and the spatial distribution of phases. In particular, the pressure drop and temperature evolution along the pipe is highly dependent on the spatial configuration of the phases. In this work we investigate the three-phase water-assisted flow patterns, i.e. those configurations where water is injected in order to reduce friction caused by the viscous oil. Phase flow rates and pressure drop data from previous laboratory experiments in a horizontal pipe are used for flow pattern identification by means of the 'support vector machine' technique (SVM).

Análise morfológica da corrosão por pites em aço inoxidável austenítico AISI 310S submetido à exposição em névoa salina

O artigo teve como objetivo estudar a evolução do crescimento, distribuição e classificação dos pites em aços inoxidáveis austeníticos AISI 310S no estado como recebida e tratado termicamente, submetidos a diferentes tempos de exposição em meio salino. A aplicabilidade deste trabalho baseou-se no desenvolvimento de uma técnica para caracterização morfológica da corrosão localizada, associado com os aspectos de descrição de formas, tamanho, parâmetros específicos e populacionais. A metodologia consistiu nas seguintes etapas: preparação dos corpos de prova, testes de corrosão via névoa salina em diferentes condições, análise microestrutural, análise dos perfis dos pites, processamento digital e análise de imagens, visando caracterizar a distribuição, morfologia e o tamanho dos pites. Os resultados obtidos no processamento digital e análise de imagens dos perfis foram submetidos á análise estatística, utilizando à mediana como parâmetro de avaliação na liga como recebido e tratada. A liga como recebido, exibe a seguinte morfologia: pites hemisféricos> região de transição A> região de transição B> irregulares> cônicos. A quantidade de pites na liga tratada a cada tempo de exposição é: região de transição B> hemisféricos> região de transição A> cônicos> irregulares.

Damage Characterization Using Guided-Wave Linear Arrays and Image Compounding Techniques

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Oriented texture classification based on self-organizing neural network and Hough transform

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.

Estimation of crowd density using image processing

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. This paper describes a new technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey-level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.