Automatic visual dictionary generation through Optimum-Path Forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation. © 2012 IEEE.

Automatic landslide recognition through Optimum-Path Forest

In this paper we shed light over the problem of landslide automatic recognition using supervised classification, and we also introduced the OPF classifier in this context. We employed two images acquired from Geoeye-MS satellite at March-2010 in the northwest (high steep areas) and north sides (pipeline area) covering the area of Duque de Caxias city, Rio de Janeiro State, Brazil. The landslide recognition rate has been assessed through a cross-validation with 10 runnings. In regard to the classifiers, we have used OPF against SVM with Radial Basis Function for kernel mapping and a Bayesian classifier. We can conclude that OPF, Bayes and SVM achieved high recognition rates, being OPF the fastest approach. © 2012 IEEE.

Automatic fiscal stabilizers

Includes bibliography

Heterogeneous demand and market segmentation: the Argentine pension funds system

Includes bibliography

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Use of algorithms for semi-automatic quantification of pulmonary fibrosis and emphysema

Obtaining a semi-automatic quantification of pathologies found in the lung, through images of high resolution computed tomography (HRCT), is of great importance to aid in medical diagnosis. Paraccocidioidomycosis (PCM) is a systemic disease that affects the lung and even after effective treatment leaves sequels such as pulmonary fibrosis and emphysema. It is very important to the area of tropical diseases that the lung injury be quantified more accurately. In this stud, we propose the development of algorithms in computational environment Matlab® able to objectively quantify lung diseases such as fibrosis and emphysema. The program consists in selecting the region of interest (ROI), and through the use of density masks and filters, obtaining the lesion area quantification in relation to the healthy area of the lung. The proposed method was tested on 15 exams of HRCT of patients with confirmed PCM. To prove the validity and effectiveness of the method, we used a virtual phantom, also developed in this research. © 2013 Springer-Verlag.

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.

Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

This paper presents a novel segmentation method for cuboidal cell nuclei in images of prostate tissue stained with hematoxylin and eosin. The proposed method allows segmenting normal, hyperplastic and cancerous prostate images in three steps: pre-processing, segmentation of cuboidal cell nuclei and post-processing. The pre-processing step consists of applying contrast stretching to the red (R) channel to highlight the contrast of cuboidal cell nuclei. The aim of the second step is to apply global thresholding based on minimum cross entropy to generate a binary image with candidate regions for cuboidal cell nuclei. In the post-processing step, false positives are removed using the connected component method. The proposed segmentation method was applied to an image bank with 105 samples and measures of sensitivity, specificity and accuracy were compared with those provided by other segmentation approaches available in the specialized literature. The results are promising and demonstrate that the proposed method allows the segmentation of cuboidal cell nuclei with a mean accuracy of 97%. © 2013 Elsevier Ltd. All rights reserved.

Automated diagnosis of human intestinal parasites using optical microscopy images

Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes. © 2013 IEEE.

Estudo de métodos para classificação e localização precisa de padrões usando um sistema de luz estruturada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Geoprocessamento aplicado ao desenvolvimento de uma base de dados do Município de Botucatu

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Motherhood wage penalties and labour market segmentation: Evidence from Argentina

Includes bibliography.

Reconhecimento semiautomático de dentes para a identificação humana forense

Pós-graduação em Ciência da Computação - IBILCE

Informality and labour market segmentation: the case of Argentina

The document evaluates the presence of segmentation in the Argentinean labour market. The analysis is centred on the comparison of the earnings of formal and informal workers. Two different approaches to the definition of informality are used. The existence of a formal premium is tested using dynamic data and semiparametric techniques. The period analysed is 1996-2006 for all urban surveyed areas. Our results support the segmentation hypothesis for the Argentine urban labour market: workers with similar probabilities of entering/exiting across sectors obtain different earnings.

Uma abordagem híbrida e semiautomática para estimativa de regiões cobertas por nuvens e sombras em imagens de satélite: análise e avaliação

Os principais objetivos deste trabalho são propor um algoritmo eficiente e o mais automático possível para estimar o que está coberto por regiões de nuvens e sombras em imagens de satélite; e um índice de confiabilidade, que seja aplicado previamente à imagem, visando medir a viabilidade da estimação das regiões cobertas pelos componentes atmosféricos usando tal algoritmo. A motivação vem dos problemas causados por esses elementos, entre eles: dificultam a identificação de objetos de imagem, prejudicam o monitoramento urbano e ambiental, e desfavorecem etapas cruciais do processamento digital de imagens para extrair informações ao usuário, como segmentação e classificação. Através de uma abordagem híbrida, é proposto um método para decompor regiões usando um filtro passa-baixas não-linear de mediana, a fim de mapear as regiões de estrutura (homogêneas), como vegetação, e de textura (heterogêneas), como áreas urbanas, na imagem. Nessas áreas, foram aplicados os métodos de restauração Inpainting por suavização baseado em Transformada Cosseno Discreta (DCT), e Síntese de Textura baseada em modelos, respectivamente. É importante salientar que as técnicas foram modificadas para serem capazes de trabalhar com imagens de características peculiares que são obtidas por meio de sensores de satélite, como por exemplo, as grandes dimensões e a alta variação espectral. Já o índice de confiabilidade, tem como objetivo analisar a imagem que contém as interferências atmosféricas e daí estimar o quão confiável será a redefinição com base no percentual de cobertura de nuvens sobre as regiões de textura e estrutura. Tal índice é composto pela combinação do resultado de algoritmos supervisionados e não-supervisionados envolvendo 3 métricas: Exatidão Global Média (EGM), Medida De Similaridade Estrutural (SSIM) e Confiança Média Dos Pixels (CM). Finalmente, verificou-se a eficácia destas metodologias através de uma avaliação quantitativa (proporcionada pelo índice) e qualitativa (pelas imagens resultantes do processamento), mostrando ser possível a aplicação das técnicas para solucionar os problemas que motivaram a realização deste trabalho.