Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Image categorization through optimum path forest and visual words

Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.

Improving image classification through descriptor combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

Procedimentos de seleção de cor, percepção visual de diferença de cor e fluorescência em odontologia estética

Pós-graduação em Ciências Odontológicas - FOAR

Sensoriamento remoto e geoprocessamento aplicado ao estudo temporal do uso da terra e na comparação entre classificação não supervisionada e análise visual

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Aspectos morfológicos comparativos entre neurônios da camada I do córtex visual de duas espécies de roedores: Cavia porcellus e Rattus norvegicus

A camada I tem como característica principal a baixa quantidade de neurônios e uma alta densidade de fibras nervosas. A morfologia dos neurônios da camada I ainda é pouco estudada, tanto que nos estudos que avaliaram a morfologia desses neurônios não se chegou ainda a um consenso sobre as formas e funções desses neurônios. Este estudo avaliou a morfologia dos neurônios da camada I do córtex visual de duas espécies de roedores: Cavia porcellus, popularmente conhecido no Brasil como porquinho-da-índia e Rattus norvegicus, que é o rato e foi utilizada a linhagem Wistar, comumente usado nas pesquisas científicas. O porquinho-da-índia é um modelo animal muito estudado, utilizado em diversos segmentos da ciência. Apesar dessa espécie ser bem estudada, trabalhos na camada I desse animal são relativamente raros, especialmente em relação à morfologia e eletrofisiologia dos neurônios dessa região cortical. Pesquisas em ratos sobre os neurônios da camada I são mais frequentes, tanto em relação a morfologia quanto a eletrofisiologia. Para discriminar as possibilidades de diferenças na morfologia dos neurônios da camada I do córtex visual do porquinho-da-índia e do rato, este estudo classificou esses neurônios de acordo com a trajetória de seus dendritos e analisou as medidas dendríticas utilizando a técnica de injeção intracelular de biocitina. Após a classificação dos neurônios as comparações foram feitas entre os mesmos tipos celulares de cada roedor. Foram utilizados 35 porquinhos-da-índia da variedade Dunkin-Hartley de pêlo curto de ambos os sexos com idades de 4 a 5 dias de vida pós-natal. Quanto aos ratos, foram utilizados 30 ratos da variedade Wistar, de ambos os sexos com idades de 14 a 21 dias de vida pós-natal. Os animais foram anestesiados e tiveram seus encéfalos removidos, hemisférios separados e foram realizados cortes no plano coronal na região occipital onde se localiza a área visual dos roedores. As fatias foram mantidas em líquido cérebro-espinhal artificial e em seguida levadas ao microscópio para injeção de biocitina e posteriormente foram fixadas e tratadas para montagem em lâmina e contracoradas com Nissl para melhor visualização. Os neurônios encontrados foram classificados como: horizontais, ascendentes, descendentes e radias. Foram analisadas as seguintes medidas dendríticas: área do campo receptor, comprimento dendrítico total e médio, área total do corpo celular, número de dendritos, distância da pia-máter e análise da distribuição de Sholl. Dos resultados obtidos os mais notáveis foram o alcance dos ramos dendríticos e o tamanho do corpo celular dos neurônios da camada I do porquinho-da-índia quando comparados aos do rato. Isso sugere que, nessa espécie, um maior número de microcircuitos neurais podem ser estabelecidos, e por conseguinte maior taxa metabólica, justificada pelo maior tamanho do corpo celular.

Prevalência da deficiência visual e fatores associados no município de Pratânia - SP

Pós-graduação em Bases Gerais da Cirurgia - FMB

Poesia visual e poética postal

Ce travail cherche montrer quelques caractéristiques de la Poésie Visuel, expresion artistique qui melange le langage verbal et le langage visuel à tel point qu´on ne peut pas afirmer une classification appuyée sur les genres traditionnels. Aprés une curte presentation des chemins et des points polémiques de cette esthétique, on analyse comme exemple deux compositions sorties de le grand réseau de communication de l´Art Postal qui est né les années soixante et survit aujourd´hui avec intensité. L´Art Postal a une structure fondé sur la simultanéité de l´information que ressamble à la forme de la poésie visuel. Cettes compositions contemporaines sont confrontées à la tradition du poème visuel qui commence peut-être à l´époque des premiers écrits de hiéroglyphe. Elles sont analysées, donc, dans son originalité. A partir des analyses realisées dans le but de révéler les significations, on arrive à la question proposé par Wilcon Joia Pereira : la nécessité de se comprendre d´une façon nouvelle les expériences esthétiques oú les mots deviennent des images

Swarm-based descriptor combination and its application for image classification

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

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Visual impairment in children with spastic cerebral palsy measured by psychophysical and electrophysiological grating acuity tests

Background: This study measured grating visual acuity in 173 children between 6-48 months of age who had different types of spastic cerebral palsy (CP). Method: Behavioural acuity was measured with the Teller Acuity Cards (TAC) using a staircase psychophysical procedure. Electrophysiological visual acuity was estimated using the sweep VEP (sVEP). Results: The percentage of children outside the superior tolerance limits was 44 of 63 (69%) and 50 of 55 (91%) of tetraplegic, 36 of 56 (64%) and 42 of 53 (79%) of diplegic, 10 of 48 (21%) and 12 of 40 (30%) of hemiplegic for sVEP and TAC, respectively. For the sVEP, the greater visual acuity deficit found in the tetraplegic group was significantly different from that of the hemiplegic group (p < 0.001). In the TAC procedure the mean visual acuity deficits of the tetraplegic and diplegic groups were significantly different from that of hemiplegic group (p < 0.001). The differences between sVEP and TAC means of visual acuity difference were statistically significant for the tetraplegic (p < 0.001), diplegic (p < 0.001), and hemiplegic group (p = 0.004). Discussion: Better visual acuities were obtained in both procedures for hemiplegic children compared to diplegic or tetraplegic. Tetraplegic and diplegic children showed greater discrepancies between the TAC and sVEP results. Inter-ocular acuity difference was more frequent in sVEP measurements. Conclusions: Electrophysiologically measured visual acuity is better than behavioural visual acuity in children with CP.

Supervised classification of basaltic aggregate particles based on texture properties

The strength and durability of materials produced from aggregates (e.g., concrete bricks, concrete, and ballast) are critically affected by the weathering of the particles, which is closely related to their mineral composition. It is possible to infer the degree of weathering from visual features derived from the surface of the aggregates. By using sound pattern recognition methods, this study shows that the characterization of the visual texture of particles, performed by using texture-related features of gray scale images, allows the effective differentiation between weathered and nonweathered aggregates. The selection of the most discriminative features is also performed by taking into account a feature ranking method. The evaluation of the methodology in the presence of noise suggests that it can be used in stone quarries for automatic detection of weathered materials.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

Classificazione di oggetti in immagini attraverso il modello Bag of Visual Words

Generic object recognition is an important function of the human visual system and everybody finds it highly useful in their everyday life. For an artificial vision system it is a really hard, complex and challenging task because instances of the same object category can generate very different images, depending of different variables such as illumination conditions, the pose of an object, the viewpoint of the camera, partial occlusions, and unrelated background clutter. The purpose of this thesis is to develop a system that is able to classify objects in 2D images based on the context, and identify to which category the object belongs to. Given an image, the system can classify it and decide the correct categorie of the object. Furthermore the objective of this thesis is also to test the performance and the precision of different supervised Machine Learning algorithms in this specific task of object image categorization. Through different experiments the implemented application reveals good categorization performances despite the difficulty of the problem. However this project is open to future improvement; it is possible to implement new algorithms that has not been invented yet or using other techniques to extract features to make the system more reliable. This application can be installed inside an embedded system and after trained (performed outside the system), so it can become able to classify objects in a real-time. The information given from a 3D stereocamera, developed inside the department of Computer Engineering of the University of Bologna, can be used to improve the accuracy of the classification task. The idea is to segment a single object in a scene using the depth given from a stereocamera and in this way make the classification more accurate.