Classificação híbrida: pixel a pixel e baseada em objetos para o monitoramento da condição da superfície dos pavimentos rodoviários

Monitorar a condição de uso de toda a extensão das rodovias brasileiras é tarefa dispendiosa e demorada. Este trabalho trata de novas técnicas que permitem o levantamento da condição da superfície dos pavimentos rodoviários de forma ágil utilizando imagens hiperespectrais de sensor digital aeroembarcado. Nos últimos anos, um número crescente de imagens de alta resolução espacial tem surgido no mercado mundial com o aparecimento dos novos satélites e sensores aeroembarcados de sensoriamento remoto. Propõe-se uma metodologia para identificação dos pavimentos asfálticos e classificação das principais ocorrências dos defeitos na superfície do pavimento. A primeira etapa da metodologia é a identificação da superfície asfáltica na imagem, utilizando uma classificação híbrida baseada inicialmente em pixel e depois refinada por objetos. A segunda etapa da metodologia é a identificação e classificação das ocorrências dos principais defeitos nos pavimentos flexíveis que são observáveis nas imagens de alta resolução espacial. Esta última etapa faz uso intensivo das novas técnicas de classificação de imagens baseadas em objetos. O resultado final é a geração de índices da condição da superfície do pavimento a partir das imagens que possam ser comparados com os indicadores vigentes da condição da superfície do pavimento já normatizados pelos órgãos competentes no país.

Expanding the horizontal capabilities of CRT monitors using artificial inter-pixel steps for neuroscience experiments

This article describes the development of a visual stimulus generator to be used in neuroscience experiments with invertebrates such as flies. The experiment consists in the visualization of a fixed image that is displaced horizontally according to the stimulus data. The system is capable of displaying 640 x 480 pixels with 256 intensity levels at 200 frames per second (FPS) on conventional raster monitors. To double the possible horizontal positioning possibilities from 640 to 1280, a novel technique is presented introducing artificial inter-pixel steps. The implementation consists in using two video frame buffers containing each a distinct view of the desired image pattern. This implementation generates a visual effect capable of doubling the horizontal positioning capabilities of the visual stimulus generator allowing more precise and movements more contiguous. (C) 2011 Elsevier Inc. All rights reserved.

Cytometric quantification of singlet oxygen in the human malaria parasite Plasmodium falciparum

The malaria parasite Plasmodium falciparum proliferates within human erythrocytes and is thereby exposed to a variety of reactive oxygen species (ROS) such as hydrogen peroxide, hydroxyl radical, superoxide anion, and highly reactive singlet oxygen (1O2). While most ROS are already well studied in the malaria parasite, singlet oxygen has been neglected to date. In this study we visualized the generation of 1O2 by live cell fluorescence microscopy using 3-(p-aminophenyl) fluorescein as an indicator dye. While 1O2 is found restrictively in the parasite, its amount varies during erythrocytic schizogony. Since the photosensitizer cercosporin generates defined amounts of 1O2 we have established a new cytometric method that allows the stage specific quantification of 1O2. Therefore, the parasites were first classified into three main stages according to their respective pixel-area of 200600 pixels for rings, 7001,200 pixels for trophozoites and 1,4002,500 pixels for schizonts. Interestingly the highest mean concentration of endogenous 1O2 of 0.34 nM is found in the trophozoites stage, followed by 0.20 nM (ring stage) and 0.10 nM (schizont stage) suggesting that 1O2 derives predominantly from the digestion of hemoglobin. (c) 2012 International Society for Advancement of Cytometry

Functional Magnetic Resonance Imaging Data Analysis by Autoregressive Estimator

The autoregressive (AR) estimator, a non-parametric method, is used to analyze functional magnetic resonance imaging (fMRI) data. The same method has been used, with success, in several other time series data analysis. It uses exclusively the available experimental data points to estimate the most plausible power spectra compatible with the experimental data and there is no need to make any assumption about non-measured points. The time series, obtained from fMRI block paradigm data, is analyzed by the AR method to determine the brain active regions involved in the processing of a given stimulus. This method is considerably more reliable than the fast Fourier transform or the parametric methods. The time series corresponding to each image pixel is analyzed using the AR estimator and the corresponding poles are obtained. The pole distribution gives the shape of power spectra, and the pixels with poles at the stimulation frequency are considered as the active regions. The method was applied in simulated and real data, its superiority is shown by the receiver operating characteristic curves which were obtained using the simulated data.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Update on the status of the Space Telescope Imaging Spectrograph onboard the Hubble Space Telescope

The Space Telescope Imaging Spectrograph (STIS) has been on orbit for approximately 16 years as one of the 2nd generation instruments on the Hubble Space Telescope (HST). Its operations were interrupted by an electronics failure in 2004, but STIS was successfully repaired in May 2009 during Service Mission 4 (SM4) allowing it to resume science observations. The Instrument team continues to monitor its performance and work towards improving the quality of its products. Here we present updated information on the status of the FUV and NUV MAMA and the CCD detectors onboard STIS and describe recent changes to the STIS calibration pipeline. We also discuss the status of efforts to apply a pixel-based correction for charge transfer inefficiency (CTI) effects to STIS CCD data. These techniques show promise for ameliorating the effects of ongoing radiation damage on the quality of STIS CCD data.

Quantification of cellular action fibers alignment from confocal microscopic images.

The cellular rheology has recently undergone a rapid development with particular attention to the cytoskeleton mechanical properties and its main components - actin filaments, intermediate filaments, microtubules and crosslinked proteins. However it is not clear what are the cellular structural changes that directly affect the cell mechanical properties. Thus, in this work, we aimed to quantify the structural rearrangement of these fibers that may emerge in changes in the cell mechanics. We created an image analysis platform to study smooth muscle cells from different arteries: aorta, mammary, renal, carotid and coronary and processed respectively 31, 29, 31, 30 and 35 cell image obtained by confocal microscopy. The platform was developed in Matlab (MathWorks) and it uses the Sobel operator to determine the actin fiber image orientation of the cell, labeled with phalloidin. The Sobel operator is used as a filter capable of calculating the pixel brightness gradient, point to point, in the image. The operator uses vertical and horizontal convolution kernels to calculate the magnitude and the angle of the pixel intensity gradient. The image analysis followed the sequence: (1) opens a given cells image set to be processed; (2) sets a fix threshold to eliminate noise, based on Otsu's method; (3) detect the fiber edges in the image using the Sobel operator; and (4) quantify the actin fiber orientation. Our first result is the probability distribution II(Δθ) to find a given fiber angle deviation (Δθ) from the main cell fiber orientation θ0. The II(Δθ) follows an exponential decay II(Δθ) = Aexp(-αΔθ) regarding to its θ0. We defined and determined a misalignment index α of the fibers of each artery kind: coronary αCo = (1.72 ‘+ or =’ 0.36)rad POT -1; renal αRe = (1.43 + or - 0.64)rad POT -1; aorta αAo = (1.42 + or - 0.43)rad POT -1; mammary αMa = (1.12 + or - 0.50)rad POT -1; and carotid αCa = (1.01 + or - 0.39)rad POT -1. The α of coronary and carotid are statistically different (p < 0.05) among all analyzed cells. We discussed our results correlating the misalignment index data with the experimental cell mechanical properties obtained by using Optical Magnetic Twisting Cytometry with the same group of cells.

Lamb mode diversity imaging fornon-destructivetesting of plate-likestructures

Several Lamb wave modes can be coupled to a particular structure, depending on its geometry and transducer used to generate the guided waves. Each Lamb mode interacts in a particular form with different types of defects, like notches, delamination, surface defects, resulting in different information which can be used to improve damage detection and characterization. An image compounding technique that uses the information obtained from different propagation modes of Lamb waves for non-destructive testing of plate-like structures is proposed. A linear array consisting of 16 piezoelectric elements is attached to a 1 mm thickness aluminum plate, coupling the fundamental A0 and SO modes at the frequencies of 100 kHz and 360 kHz, respectively. For each mode two images are obtained from amplitude and phase information: one image using the Total Focusing Method (TFM) and one phase image obtained from the Sign Coherence Factor (SCF). Each TFM image is multiplied by the SCF image of the respective mode to improve contrast and reduce side and grating lobes effects. The high dispersive characteristic of the A0 mode is compensated for adequate defect detection. The information in the SCF images is used to select one of the TFM mode images, at each pixel, to obtain the compounded image. As a result, dead zone is reduced, resolution and contrast are improved, enhancing damage detection when compared to the use of only one mode. (C) 2013 Elsevier Ltd. All rights reserved. (AU)

Combining fractal and deterministic walkers for texture analysis and classification

In this paper,we present a novel texture analysis method based on deterministic partially self-avoiding walks and fractal dimension theory. After finding the attractors of the image (set of pixels) using deterministic partially self-avoiding walks, they are dilated in direction to the whole image by adding pixels according to their relevance. The relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors. The proposed texture analysis method is demonstrated to outperform popular and state-of-the-art methods (e.g. Fourier descriptors, occurrence matrix, Gabor filter and local binary patterns) as well as deterministic tourist walk method and recent fractal methods using well-known texture image datasets.

Dynamic texture segmentation based on deterministic partially self-avoiding walks

Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods.