Effects of aluminum-copper alloy filtration on photon spectra, air kerma rate and image contrast

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Use of comet assay to assess DNA damage in patients infected by Helicobacter pylori - Comparisons between visual and image analyses

Studies of DNA damage in gastric epithelial cells of Helicobacter pylori (H. pylori)-infected patients are conflicting, possibly due to different methods used for scoring DNA damage by Comet assay. Therefore, we compared the sensitivity of visual microscopic analysis (arbitrary units-scores and comets%) and image analysis system (tail moment), in the gastric epithelial cells from the antrum and corpus of 122 H. pylori-infected and 32 non-infected patients. The feasibility of cryopreserved peripheral blood lymphocytes and whole-blood cells for DNA damage biomonitoring was also investigated. In the antrum, the levels of DNA damage were significantly higher in H. pylori-infected patients with gastritis than in non-infected patients with normal mucosa, when evaluated by image analysis system, arbitrary units and comets%. In the corpus, the comets% was not sufficiently sensitive to detect the difference between H. pylori-infected patients with gastritis and non-infected patients with normal mucosa. The image analysis system was sensitive enough to detect differences between non-infected patients and H. pylori-infected patients with mild gastritis and between infected patients with moderate and severe gastritis, in both antrum, and corpus, while arbitrary units and comets% were unable to detect these differences. In cryopreserved peripheral blood lymphocytes, the levels of DNA damage (tail moment) were significantly higher in H. pylori-infected patients with moderate and severe gastritis than in non-infected patients. Overall, our results indicate that the image analysis system is more sensitive and adequate to measure the levels of DNA damage in gastric epithelial cells than the other methods assayed. (c) 2005 Elsevier B.V. All rights reserved.

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

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Image registration and image interpolation in volume rendering of cross sections

We outline a method for registration of images of cross sections using the concepts of The Generalized Hough Transform (GHT). The approach may be useful in situations where automation should be a concern. To overcome known problems of noise of traditional GHT we have implemented a slight modified version of the basic algorithm. The modification consists of eliminating points of no interest in the process before the application of the accumulation step of the algorithm. This procedure minimizes the amount of accumulation points while reducing the probability of appearing of spurious peaks. Also, we apply image warping techniques to interpolate images among cross sections. This is needed where the distance of samples between sections is too large. Then it is suggested that the step of registration with GHT can help the interpolation automation by simplifying the correspondence between points of images. Some results are shown.

Sparse arrays and image compounding techniques for non-destructive testing using guided acoustic waves

Piezoelectric array transducers applications are becoming usual in the ultrasonic non-destructive testing area. However, the number of elements can increase the system complexity, due to the necessity of multichannel circuitry and to the large amount of data to be processed. Synthetic aperture techniques, where one or few transmission and reception channels are necessary, and the data are post-processed, can be used to reduce the system complexity. Another possibility is to use sparse arrays instead of a full-populated array. In sparse arrays, there is a smaller number of elements and the interelement spacing is larger than half wavelength. In this work, results of ultrasonic inspection of an aluminum plate with artificial defects using guided acoustic waves and sparse arrays are presented. Synthetic aperture techniques are used to obtain a set of images that are then processed with an image compounding technique, which was previously evaluated only with full-populated arrays, in order to increase the resolution and contrast of the images. The results with sparse arrays are equivalent to the ones obtained with full-populated arrays in terms of resolution. Although there is an 8 dB contrast reduction when using sparse arrays, defect detection is preserved and there is the advantage of a reduction in the number of transducer elements and data volume. © 2013 Brazilian Society for Automatics - SBA.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Novas formas de pensar/dizer, dizer/pensar: a imagem como linguagem mediadora da experiência do formador de professores

Pós-graduação em Artes - IA

Cliques poéticos de instantes ficcionais: a elipse e o fora de campo fotográfico em Mínimos, múltiplos, comuns de João Gilberto Noll

Pós-graduação em Letras - IBILCE

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.

Swarm-based descriptor combination and its application for image classification

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

Bundle Block Adjustment of CBERS-2B HRC Imagery Combining Control Points and Lines

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

IFTrace: Video segmentation of deformable objects using the Image Foresting Transform

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

Image analysis of modified cellulose fibers from sugarcane bagasse by zirconium oxychloride

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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.

Mixing and motion of rice particles in a rotating drum

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