É apresentado o desenvolvimento de um modelo computacional que visa relacionar imagens digitais obtidas em diferentes escalas e resoluções com aplicação de Wavelets. Seu desenvolvimento encontra-se no contexto multidisciplinar e situa-se na intersecção das linhas de pesquisa de áreas da Física, da Matemática e da Computação. Desta forma, optou-se na implementação por uma abordagem computacional dos estudos, com aplicação em imagens digitais provenientes da reconstrução de dados de tomografia computadorizada de Raios-X. Resultados indicam que a implementação do modelo computacional desenvolvido tem sua funcionalidade comprovada, uma vez que os atributos vetoriais dos objetos considerados para análise (poros) foram mantidos estáveis em diferentes resoluções estudadas. O modelo foi implementado em linguagem de programação C++ com uso de orientação a objetos e organizado em classes. Adicionalmente, sua aplicação é viabilizada para diversas plataformas computacionais no que tange a sistemas operacionais e processadores. Do ponto de vista científico, o sistema resultante, além de ser uma ferramenta importante no estudo de meios porosos através de imagens de tomografia computadorizada por Raios-X, contribui com métodos inovadores que fazem uso de Wavelets e são aplicados na suavização de bordas por técnica sub-pixel...

Nesta dissertação está proposta uma metodologia para fins de detecção de mudanças do uso do solo em imagens multitemporais de sensoriamento remoto. Em lugar de classificar os pixels de imagens que cobrem uma cena, em duas classes exaustivas e mutuamente excludentes (mudança, não-mudança), propõe-se adotar uma abordagem do tipo fuzzy, na qual são estimados os graus de pertinência às classes mudança e não-mudança. Com este objetivo adota-se aqui uma abordagem em nível de sub-pixel na estimação dos graus de pertinência para cada pixel. Esta abordagem se mostra mais adequada para fins de modelagem do que ocorre em cenas naturais, onde as alterações que acontecem ao longo de um período de tempo tendem a apresentar uma variação contínua em lugar de discreta. Em uma segunda etapa, os graus de pertinência estimados recebem um ajustamento adicional por meio da introdução de informações de contexto espacial. A metodologia proposta foi testada por meio de três experimentos, um empregando uma imagem sintética e dois utilizando imagens reais. A partir da análise quantitativa dos resultados e comparação com estudos semelhantes, comprova-se a adequação da metodologia proposta.; In this dissertation it is proposed a new methodology to land use change detection in remote sensing multitemporal image data. Rather than applying a rigid labeling of the pixels in the image data into two classes (change...

Sub-pixel analysis is capable of generating continuous fields, which represent the spatial variability of certain thematic classes. The aim of this work was to develop numerical models to represent the variability of tree cover and bare surfaces within the study area. This research was conducted in the riparian buffer within a watershed of the São Francisco River in the North of Minas Gerais, Brazil. IKONOS and Landsat TM imagery were used with the GUIDE algorithm to construct the models. The results were two index images derived with regression trees for the entire study area, one representing tree cover and the other representing bare surface. The use of non-parametric and non-linear regression tree models presented satisfactory results to characterize wetland, deciduous and savanna patterns of forest formation.

Live-cell imaging by light microscopy has demonstrated that all cells are spatially and temporally organized. Quantitative, computational image analysis is an important part of cellular imaging, providing both enriched information about individual cell properties and the ability to analyze large datasets. However, such studies are often limited by the small size and variable shape of objects of interest. Here, we address two outstanding problems in bacterial cell division by developing a generally applicable, standardized, and modular software suite termed Projected System of Internal Coordinates from Interpolated Contours (PSICIC) that solves common problems in image quantitation. PSICIC implements interpolated-contour analysis for accurate and precise determination of cell borders and automatically generates internal coordinate systems that are superimposable regardless of cell geometry. We have used PSICIC to establish that the cell-fate determinant, SpoIIE, is asymmetrically localized during Bacillus subtilis sporulation, thereby demonstrating the ability of PSICIC to discern protein localization features at sub-pixel scales. We also used PSICIC to examine the accuracy of cell division in Esherichia coli and found a new role for the Min system in regulating division-site placement throughout the cell length...

Miniaturization of imaging systems can significantly benefit clinical diagnosis in challenging environments, where access to physicians and good equipment can be limited. Sub-pixel resolving optofluidic microscope (SROFM) offers high-resolution imaging in the form of an on-chip device, with the combination of microfluidics and inexpensive CMOS image sensors. In this work, we report on the implementation of color SROFM prototypes with a demonstrated optical resolution of 0.66 µm at their highest acuity. We applied the prototypes to perform color imaging of red blood cells (RBCs) infected with Plasmodium falciparum, a particularly harmful type of malaria parasites and one of the major causes of death in the developing world.

The goal of this paper was to develop and demonstrate practical methods for computing sub-pixel areas (SPAs) from coarse-resolution satellite sensor data. The methods were tested and verified using: (a) global irrigated area map (GIAM) at 10-km resolution based, primarily, on AVHRR data, and (b) irrigated area map for India at 500-m based, primarily, on MODIS data. The sub-pixel irrigated areas (SPIAs) from coarse-resolution satellite sensor data were estimated by multiplying the full pixel irrigated areas (FPIAs) with irrigated area fractions (IAFs). Three methods were presented for IAF computation: (a) Google Earth Estimate (IAF-GEE); (b) High resolution imagery (IAF-HRI); and (c) Sub-pixel de-composition technique (IAF-SPDT). The IAF-GEE involved the use of “zoom-in-views” of sub-meter to 4-meter very high resolution imagery (VHRI) from Google Earth and helped determine total area available for irrigation (TAAI) or net irrigated areas that does not consider intensity or seasonality of irrigation. The IAF-HRI is a well known method that uses finer-resolution data to determine SPAs of the coarser-resolution imagery. The IAF-SPDT is a unique and innovative method wherein SPAs are determined based on the precise location of every pixel of a class in 2-dimensional brightness-greenness-wetness (BGW) feature-space plot of red band versus near-infrared band spectral reflectivity. The SPIAs computed using IAF-SPDT for the GIAM was within 2 % of the SPIA computed using well known IAF-HRI. Further the fractions from the 2 methods were significantly correlated. The IAF-HRI and IAF-SPDT help to determine annualized or gross irrigated areas (AIA) that does consider intensity or seasonality (e.g....

Sub-pixel analysis is capable of generating continuous fi elds, which represent the spatial variability of certain thematic classes. The aim of this work was to develop numerical models to represent the variability of tree cover and bare surfaces within the study area. This research was conducted in the riparian buffer within a watershed of the São Francisco River in the North of Minas Gerais, Brazil. IKONOS and Landsat TM imagery were used with the GUIDE algorithm to construct the models. The results were two index images derived with regression trees for the entire study area, one representing tree cover and the other representing bare surface. The use of non-parametric and non-linear regression tree models presented satisfactory results to characterize wetland, deciduous and savanna patterns of forest formation.

Sub-pixel analysis is capable of generating continuous fields, which represent the spatial variability of certain thematic classes. The aim of this work was to develop numerical models to represent the variability of tree cover and bare surfaces within the study area. This research was conducted in the riparian buffer within a watershed of the São Francisco River in the North of Minas Gerais, Brazil. IKONOS and Landsat TM imagery were used with the GUIDE algorithm to construct the models. The results were two index images derived with regression trees for the entire study area, one representing tree cover and the other representing bare surface. The use of non-parametric and non-linear regression tree models presented satisfactory results to characterize wetland, deciduous and savanna patterns of forest formation.

The current work aimed at testing a methodology which can be applied to low spatial resolution satellite data to assess inter-annual crop area variations on sub-pixel to regional scales. The methodology is based on the assumption that within mixed pixels land cover variations are reflected by changes in the related hyper-temporal profiles of the Normalised Difference Vegetation Index (NDVI). We evaluated if changes in the fractional winter crop coverage are reflected in changing shapes of annual NDVI profiles and can be detected by using neural networks. The neural nets were trained on reference data obtained from high resolution Landsat TM/ETM images and additional ancillary data readily available (CORINE land cover). The proposed methodology was applied in a study region in central Italy to estimate winter crop areas between 1988 and 2002 from 1 km resolution NOAA-AVHRR profiles. The accuracy of the estimates was assessed by comparison to official agricultural statistics using a bootstrap approach. The method showed promise for estimating crop area variation on sub-pixel (cross-validated R2 between 0.7 and 0.8) to regional scales (normalized RMSE: 10%) and proved to have a significantly higher forecast capability than other methods used previously for the same study area.; JRC.DG.G.3-Monitoring agricultural resources

For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach...

Due to the United States dependency on maritime travel, the proliferation of efficient and inexpensive naval mines poses a tremendous risk. Current MCM technologies have narrow FOVs, preventing timely, wide-area searches. These technologies require the operator to be in proximity to the targets, a dangerous scenario made worse when in denied territory. In an effort to mitigate these risks, the use of a high altitude hyperspectral sensor is proposed. The operational ability of a hyperspectral sensor to detect sub-pixel surface and submerged mines in non-littoral environments was evaluated using visual inspection and two common anomaly detectors Mixture Tuned Matched Filtering (MTMF) and Reed-Xiaoli (RX). Due to the unavailability of the DoDs Spectral Infrared Imaging Technology Testbed (SPIRITT), ProSpecTIR-VS3, a sensor similar spatially and spectrally to SPIRITT was flown over a range offshore California. This experiment included three surface and three submerged targets, each with a 0.8 meter diameter. Both 0.5 and 1 meter spatial resolution data were collected, allowing for both a resolved and unresolved analysis. While both anomaly detection techniques have their flaws, the success of this study is in proving the usefulness of hyperspectral data for sub-pixel mine detection.

Desert areas cover approximately one-fifth of the Earth, making it important to understand how disturbance affects arid regions on a spectral level. Remote sensing technology can be used to detect and characterize surface disturbance both literally (visually) and non-literally (analytically). Non-literal approaches may even allow detection of anthropogenic-related surface disturbances that are not visible in individual images or color composites. This is achievable through identification of differences in spectral reflectance among like soil components, both chemical and biological. Previous research suggests that surface disturbances cause alteration of soil properties, making it feasible to detect variation in reflectance signatures. This research supports that assumption and has determined that disturbance-related changes do have unique spectral characteristics in hyperspectral imagery that are detectable, even at the sub-pixel level and using endmembers from geographically different yet geologically similar regions.

Resumo. As inundações no Pantanal condicionam a produção de peixes e influenciam a dinâmica da vegetação, afetando a criação de gado bovino. O entendimento da dinâmica das inundações é de fundamental importância para inferir sobre o nível das cheias, já que causam mudanças em toda a planície. As imagens do sensor MODIS (MODerate Resolution Imaging Spectroradiometer) fornecem uma ampla cobertura da superfície da Terra com alta periodicidade, que possibilitam o monitoramento das inundações. Entretanto, a sua moderada resolução espacial faz com que possa ocorrer a mistura espectral de diferentes classes de cobertura da terra dentro de um mesmo pixel. Nesse contexto, o objetivo do trabalho foi aplicar uma metodologia de classificação sub-pixel utilizando séries temporais de dados MODIS para quantificar as áreas inundadas do Pantanal. Os dados da banda do infravermelho médio do MODIS possibilitaram o monitoramento de áreas inundadas no Pantanal durante os anos hidrológicos de 2007/2008 e 2008/2009. O período de seca e enchente é bastante variável, ocorrendo de norte para sul e de leste para oeste. A Medida de Compromisso do classificador Fuzzy ARTMAP possibilitou a quantificação de áreas inundadas, demostrando potencial para a análise sub-pixel de séries temporais de dados MODIS. Os resultados podem auxiliar os sistemas de monitoramento de inundações do Pantanal.; 2014; Geopantanal 2014.

In ultrasound strain and elasticity imaging, an accurate and cost-effective sub-pixel displacement estimator is required because strain/elasticity imaging quality relies on the displacement SNR, which can often be higher if more computational resources are provided. In this paper, we introduce an autocorrelation-based method to cost-effectively improve sub-pixel displacement estimation quality. To quantitatively evaluate the performance of the autocorrelation method, simulated and tissue-mimicking phantom experiments were performed. The computational cost of the autocorrelation method is also discussed. The results of our study suggest the autocorrelation method can be used for a real-time elasticity imaging system.

Wide-field survey instruments are used to efficiently observe large regions of the sky. To achieve the necessary field of view, and to provide a higher signal-to-noise ratio for faint sources, many modern instruments are undersampled. However, precision photometry with undersampled imagers requires a detailed understanding of the sensitivity variations on a scale much smaller than a pixel. To address this, a near-infrared spot projection system has been developed to precisely characterize near-infrared focal plane arrays and to study the effect of sub-pixel non uniformity on precision photometry. Measurements of large format near-infrared detectors demonstrate the power of this system for understanding sub-pixel response.; Comment: 9 pages, 13 figures, submitted to PASP

One of the main limitations for the resolution of optical instruments is the size of the sensor's pixels. In this paper we introduce a new sub pixel resolution algorithm to enhance the resolution of images. This method is based on the analysis of multi-images which are fast recorded during the fine relative motion of image and pixel arrays of CCDs. It is shown that by applying this method for a sample noise free image one will enhance the resolution with order of error.; Comment: World academy of science, engineering and technology (WASET), 70, 2010

We have used a mesh experiment in order to measure the sub-pixel structure of the EPIC MOS CCDs on-board the XMM/NEWTON satellite. The EPIC MOS CCDs have 40 $\mu$m-square pixels which have an open electrode structure in order to improve the detection efficiency for low-energy X-rays. We obtained restored pixel images for various X-ray event grades (e.g. split-pixel events, single pixel events, etc.) at various X-ray energies. We confirmed that the open electrode structure results in a distorted horizontal pixel boundary. The open electrode region generates both single pixel events and vertically split events, but no horizontally split events. Because the single pixel events usually show the best energy resolution, we discuss a method of increasing the fraction of single pixel events from the open electrode region. Furthermore, we have directly measured the thickness of the electrodes and dead-layers by comparing spectra from the open electrode region with those from the other regions: electrodes, electrode finger and channel stop. We can say that EPIC MOS CCDs are more radiation hard than front-illumination chips of ACIS on-board Chandra X-ray Observatory because of their extra absorption thickness above the charge transfer channel. We calcurated the mean pixel response and found that our estimation has a good agreement with that of the ground calibration of EPIC MOS CCD.; Comment: 20pages including 2 tables...

Motion Estimation (ME) is one of the most time-consuming parts in video coding. The use of multiple partition sizes in H.264/AVC makes it even more complicated when compared to ME in conventional video coding standards. It is important to develop fast and effective sub-pixel ME algorithms since (a) The computation overhead by sub-pixel ME has become relatively significant while the complexity of integer-pixel search has been greatly reduced by fast algorithms, and (b) Reducing sub-pixel search points can greatly save the computation for sub-pixel interpolation. In this paper, a novel fast sub-pixel ME algorithm is proposed which performs a 'rough' sub-pixel search before the partition selection, and performs a 'precise' sub-pixel search for the best partition. By reducing the searching load for the large number of non-best partitions, the computation complexity for sub-pixel search can be greatly decreased. Experimental results show that our method can reduce the sub-pixel search points by more than 50% compared to existing fast sub-pixel ME methods with negligible quality degradation.; Comment: This manuscript is the accepted version for TCSVT (IEEE Transactions on Circuits and Systems for Video Technology)

The color X-ray camera SLcam(R) is a full-field, single photon detector providing scanning free, energy and spatially resolved X-ray imaging. Spatial resolution is achieved with the use of polycapillary optics guiding X-ray photons from small regions on a sample to distinct energy dispersive pixels on a CCD. Applying sub-pixel resolution, signals from individual capillary channels can be distinguished. Accordingly the SLcam(R) spatial resolution can be released from pixel size being confined rather to a diameter of individual polycapillary channels. In this work a new approach to sub-pixel resolution algorithm comprising photon events also from the pixel centers is proposed. The details of the employed numerical method and several sub-pixel resolution examples are presented and discussed.; Comment: 8 pages, 7 figures

We report the implementation of a fully on-chip, lensless, sub-pixel resolving optofluidic microscope (SROFM). The device utilizes microfluidic flow to deliver specimens directly across a complementary metal oxide semiconductor (CMOS) sensor to generate a sequence of low-resolution (LR) projection images, where resolution is limited by the sensor's pixel size. This image sequence is then processed with a pixel super-resolution algorithm to reconstruct a single high resolution (HR) image, where features beyond the Nyquist rate of the LR images are resolved. We demonstrate the device's capabilities by imaging microspheres, protist Euglena gracilis, and Entamoeba invadens cysts with sub-cellular resolution and establish that our prototype has a resolution limit of 0.75 microns. Furthermore, we also apply the same pixel super-resolution algorithm to reconstruct HR videos in which the dynamic interaction between the fluid and the sample, including the in-plane and out-of-plane rotation of the sample within the flow, can be monitored in high resolution. We believe that the powerful combination of both the pixel super-resolution and optofluidic microscopy techniques within our SROFM is a significant step forwards toward a simple, cost-effective...