22 resultados para Multi-resolution segmentation
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Integer carrier phase ambiguity resolution is the key to rapid and high-precision global navigation satellite system (GNSS) positioning and navigation. As important as the integer ambiguity estimation, it is the validation of the solution, because, even when one uses an optimal, or close to optimal, integer ambiguity estimator, unacceptable integer solution can still be obtained. This can happen, for example, when the data are degraded by multipath effects, which affect the real-valued float ambiguity solution, conducting to an incorrect integer (fixed) ambiguity solution. Thus, it is important to use a statistic test that has a correct theoretical and probabilistic base, which has became possible by using the Ratio Test Integer Aperture (RTIA) estimator. The properties and underlying concept of this statistic test are shortly described. An experiment was performed using data with and without multipath. Reflector objects were placed surrounding the receiver antenna aiming to cause multipath. A method based on multiresolution analysis by wavelet transform is used to reduce the multipath of the GPS double difference (DDs) observations. So, the objective of this paper is to compare the ambiguity resolution and validation using data from these two situations: data with multipath and with multipath reduced by wavelets. Additionally, the accuracy of the estimated coordinates is also assessed by comparing with the ground truth coordinates, which were estimated using data without multipath effects. The success and fail probabilities of the RTIA were, in general, coherent and showed the efficiency and the reliability of this statistic test. After multipath mitigation, ambiguity resolution becomes more reliable and the coordinates more precise. © Springer-Verlag Berlin Heidelberg 2007.
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A simple method to determine Cu, Fe, Mn and Zn in single aliquots of medicinal plants by HR-CS FAAS is proposed. The main lines for Cu, Mn and Zn, and the alternate line measured at the wing of the main line for Fe at 248.327 nm allowed calibration within the 0.025 - 2.0 mg L-1 Cu, 1.0 - 20.0 mg L-1 Fe, 0.05 - 2.0 mg L-1 Mn, 0.025 - 0.75 mg L-1 Zn ranges. Nineteen medicinal plants and two certified plant reference materials were analyzed. Results were in agreement at a 95% confidence level (paired t-test) with reference values. Limits of detection were 0.12 μg L-1 Cu, 330 μg L-1 Fe, 1.42 μg L-1 Mn and 8.12 μg L-1 Zn. Relative standard deviations (n=12) were ≤ 3% for all analytes. Recoveries in the 89 - 105% (Cu), 95 - 108% (Fe), 94 - 107% (Mn), and 93 - 110% (Zn) ranges were obtained.
Resumo:
The major contribution of this paper relates to the practical advantages of combining Ground Control Points (GCPs), Ground Control Lines (GCLs) and orbital data to estimate the exterior orientation parameters of images collected by CBERS-2B (China-Brazil Earth Resources Satellite) HRC (High-resolution Camera) and CCD (High-resolution CCD Camera) sensors. Although the CBERS-2B is no longer operational, its images are still being used in Brazil, and the next generations of the CBERS satellite will have sensors with similar technical features, which motivates the study presented in this paper. The mathematical models that relate the object and image spaces are based on collinearity (for points) and coplanarity (for lines) conditions. These models were created in an in-house developed software package called TMS (Triangulation with Multiple Sensors) with multi-feature control (GCPs and GCLs). Experiments on a block of four CBERS-2B HRC images and on one CBERS-2B CCD image were performed using both models. It was observed that the combination of GCPs and GCLs provided better bundle block adjustment results than conventional bundle adjustment using only GCPs. The results also demonstrate the advantages of using primarily orbital data when the number of control entities is reduced. © 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
Resumo:
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Prostate cancer is a serious public health problem accounting for up to 30% of clinical tumors in men. The diagnosis of this disease is made with clinical, laboratorial and radiological exams, which may indicate the need for transrectal biopsy. Prostate biopsies are discerningly evaluated by pathologists in an attempt to determine the most appropriate conduct. This paper presents a set of techniques for identifying and quantifying regions of interest in prostatic images. Analyses were performed using multi-scale lacunarity and distinct classification methods: decision tree, support vector machine and polynomial classifier. The performance evaluation measures were based on area under the receiver operating characteristic curve (AUC). The most appropriate region for distinguishing the different tissues (normal, hyperplastic and neoplasic) was defined: the corresponding lacunarity values and a rule's model were obtained considering combinations commonly explored by specialists in clinical practice. The best discriminative values (AUC) were 0.906, 0.891 and 0.859 between neoplasic versus normal, neoplasic versus hyperplastic and hyperplastic versus normal groups, respectively. The proposed protocol offers the advantage of making the findings comprehensible to pathologists. (C) 2014 Elsevier Ltd. All rights reserved.
Resumo:
A novel AC Biosusceptometry (ACB) system with thirteen sensors it was implemented and characterized in vitro using magnetic phantoms. The system presenting coils in a coaxial arrangement with one pair of excitation coil outside and thirteen pairs of detection coils inside. A first-order gradiometric configuration was utilized for optimal detection of magnetic signals. Several physical parameters such as baseline, number of turns, excitation field and diameters were studied for improvement of the signal/noise ratio. This system exhibits an enhanced sensitivity and spatial resolution, due to the higher density of sensors/area. In the future those characteristics will turn possible to obtain images of magnetic marker or tracer in the gastrointestinal tract focusing on physiological and pharmaceutical studies. ACB emerged due to its interesting nature, noninvasiveness and low cost to investigate gastrointestinal parameters and this system can contribute for more accurate interpretation of biomedical signals and images
Resumo:
Image segmentation is a process frequently used in several different areas including Cartography. Feature extraction is a very troublesome task, and successful results require more complex techniques and good quality data. The aims of this paper is to study Digital Image Processing techniques, with emphasis in Mathematical Morphology, to use Remote Sensing imagery, making image segmentation, using morphological operators, mainly the multi-scale morphological gradient operator. In the segmentation process, pre-processing operators of Mathematical Morphology were used, and the multi-scales gradient was implemented to create one of the images used as marker image. Orbital image of the Landsat satellite, sensor TM was used. The MATLAB software was used in the implementation of the routines. With the accomplishment of tests, the performance of the implemented operators was verified and carried through the analysis of the results. The extration of linear feature, using mathematical morphology techniques, can contribute in cartographic applications, as cartographic products updating. The comparison to the best result obtained was performed by means of the morphology with conventional techniques of features extraction. © Springer-Verlag 2004.