Examinando por Autor "Gambini, Juliana"
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Ponencia en Congreso Clasificación de Imágenes SAR utilizando descriptores de textura(2021-10) Gambini, Juliana; Rey, Andrea; Delrieux, Claudio"Las imágenes SAR (Sythetic Aperture Radar) y PolSAR (Polarimetric Synthetic Aperture Radar) cumplen un rol fundamental en el monitoreo ambiental y observación terrestre debido a que proveen información que las imágenes ópticas no proporcionan. Sin embargo, estas imágenes están contaminadas con un ruido inherente al méetodo de captura llamado ruido speckle que dificulta su análisis e interpretación automática. Los modelos avanzados de segmentación de imágenes SAR están dedicados a resolver las dificultades que este ruido provoca. En este sentido, resulta de suma importancia el estudio de parámetros que describan las características estructurales de textura de imagen en presencia de ruido speckle y permitan su interpretación automática. En este trabajo, se propone un nuevo modelo de clasificación de imágenes SAR basado en el cálculo de descriptores de textura locales, formando un vector característico, el cual involucra estimaciones de parámetros de una distribución de probabilidad, estimaciones de la dimensión fractal y entropía de Tsallis. Luego, el etiquetado de cada pixel se realiza utilizando el método de clasificación supervisada SVM (Support Vector Machine). Se analizan los resultados de aplicar el algoritmo propuesto en imágenes SAR sintéticas, simples y con valores extremos agregados, los cuales resultan altamente prometedores para aplicarse en imágenes reales."Artículo de Publicación Periódica Comparing samples from the 𝒢0 distribution using a geodesic distance(2020-06) Frery, Alejandro C.; Gambini, Juliana"The 𝒢0 distribution is widely used for monopolarized SAR image modeling because it can characterize regions with different degrees of texture accurately. It is indexed by three parameters: the number of looks (which can be estimated for the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for comparing samples from the 𝒢0 distribution using a geodesic distance (GD) as a measure of dissimilarity between models. The objective is quantifying the difference between pairs of samples from SAR data using both local parameters (scale and texture) of the 𝒢0 distribution. We propose three tests based on the GD which combine the tests presented in Naranjo-Torres et al. (IEEE J Sel Top Appl Earth Obs Remote Sens 10(3):987–997, 2017), and we estimate their probability distributions using permutation methods."Ponencia en Congreso Evaluación del error en la detección de puntos de borde en imágenes SAR polarimétricas(2017-04) Monferrán, Daniel; Gambini, Juliana; Frery, Alejandro C."El Radar de Apertura Sintética polarimétrico (PolSAR - Polarimentric Synthetic Aperture Radar) es ampliamente utilizado en teledetección porque permite capturar imágenes terrestres de alta resolución. La interpretación automática de imágenes PolSAR es una tarea muy difícil porque éstas contienen un gran volumen de información y además se encuentran contaminadas con ruido speckle. Las características de este ruido hacen necesario utilizar métodos estadísticos para el procesamiento digital de este tipo de imágenes. En esta línea de investigación se pretende evaluar el error que se comete al calcular las posiciones de los puntos de borde dentro de la imagen, utilizando la distribución Wishart compleja y experimentos de Montecarlo en imágenes PolSAR simuladas."Artículo de Publicación Periódica The geodesic distance between 𝒢I0 models and its application to region discrimination(2017-03) Naranjo-Torres, José; Gambini, Juliana; Frery, Alejandro C."The 𝒢I0 distribution is able to characterize different regions in monopolarized SAR imagery. It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter, and a texture parameter. This paper presents a new proposal for feature extraction and region discrimination in SAR imagery, using the geodesic distance as a measure of dissimilarity between 𝒢I0 models. We derive geodesic distances between models that describe several practical situations, assuming the number of looks known, for same and different texture and for same and different scale. We then apply this new tool to the problems of identifying edges between regions with different texture, and quantify the dissimilarity between pairs of samples in actual SAR data. We analyze the advantages of using the geodesic distance when compared to stochastic distances."Póster Imágenes SAR polarimétricas: evaluación del error en la detección de puntos de borde(2017) Monferrán, Daniel; Gambini, Juliana; Frery, Alejandro C."El Radar de Apertura Sintética polarimétrico (PolSAR - Polarimentric Synthetic Aperture Radar) es ampliamente utilizado en teledetección porque permite capturar imágenes terrestres de alta resolución. La interpretación automática de imágenes PolSAR es una tarea muy difícil porque éstas contienen un gran volumen de información y además se encuentran contaminadas con ruido speckle. Las características de este ruido hacen necesario utilizar métodos estadísticos para el procesamiento digital de este tipo de imágenes. En esta línea de investigación se pretende evaluar el error que se comete al calcular las posiciones de los puntos de borde dentro de la imagen, utilizando la distribución Wishart compleja y experimentos de Montecarlo en imágenes PolSAR simuladas."Artículo de Publicación Periódica Low-cost robust estimation for the single-look 𝒢I0 model using the Pareto distribution(2020) Chan, Debora; Rey, Andrea; Gambini, Juliana; Frery, Alejandro C."The statistical properties of Synthetic Aperture Radar (SAR) image texture reveal useful target characteristics. It is well-known that these images are affected by speckle and prone to extreme values due to double bounce and corner reflectors. The G0 I distribution is flexible enough to model different degrees of texture in speckled data. It is indexed by three parameters: α, related to the texture, γ , a scale parameter, and L, the number of looks. Quality estimation of α is essential due to its immediate interpretability. In this letter, we exploit the connection between the G0 I and Pareto distributions. With this, we obtain six estimators that have not been previously used in the SAR literature. We compare their behavior with others in the noisiest case for monopolarized intensity data, namely single look case. We evaluate them using Monte Carlo methods for noncontaminated and contaminated data, considering convergence rate, bias, mean squared error, and computational time. We conclude that two of these estimators based on the Pareto law are the safest choices when dealing with actual data and small samples, as is the case of despeckling techniques and segmentation, to name just two applications. We verify the results with an actual SAR image."Ponencia en Congreso Methods and frameworks for sampling 𝒢I0 data(2017) Chan, Debora; Rey, Andrea; Gambini, Juliana; Cassetti, Julia; Frery, Alejandro C."The 𝒢I0 distribution is a competitive tool for SAR image description. This distribution is useful for describing speckled imagery because it models adequately areas with different degrees of texture. Data simulation is crucial for the development of new methods of automatic interpretation of this type of images. We compare four alternatives for generating data under the 𝒢I0 distribution. The experiments are performed on a variety of programming languages and, a number of criteria to test the fidelity of the generated data are applied."Artículo de Publicación Periódica Object detection and statistical analysis of microscopy image sequences(2022) Hurovitz, Sasha Ivan; Chan, Debora; Ramele, Rodrigo; Gambini, Juliana"Confocal microscope images are wide useful in medical diagnosis and research. The automatic interpretation of this type of images is very important but it is a challenging endeavor in image processing area, since these images are heavily contaminated with noise, have low contrast and low resolution. This work deals with the problem of analyzing the penetration velocity of a chemotherapy drug in an ocular tumor called retinoblastoma. The primary retinoblastoma cells cultures are exposed to topotecan drug and the penetration evolution is documented by producing sequences of microscopy images. It is possible to quantify the penetration rate of topotecan drug because it produces fluorescence emission by laser excitation which is captured by the camera. In order to estimate the topotecan penetration time in the whole retinoblastoma cell culture, a procedure based on an active contour detection algorithm, a neural network classifier and a statistical model and its validation, is proposed. This new inference model allows to estimate the penetration time. Results show that the penetration mean time strongly depends on tumorsphere size and on chemotherapeutic treatment that the patient has previously received."Artículo de Publicación Periódica Sampling from the 𝒢I0 distribution(2018-12) Chan, Debora; Rey, Andrea; Gambini, Juliana; Frery, Alejandro C."Synthetic Aperture Radar (SAR) images are widely used in several environmental applications because they provide information which cannot be obtained with other sensors. The 𝒢I0 distribution is an important model for these images because of its flexibility (it provides a suitable way for modeling areas with different degrees of texture, reflectivity and signal-to-noise ratio) and tractability (it is closely related to the Snedekor-F, Pareto Type II, and Gamma distributions). Simulated data are important for devising tools for SAR image processing, analysis and interpretation, among other applications. We compare four ways for sampling data that follow the 𝒢I0 distribution, using several criteria for assessing the quality of the generated data and the consumed processing time. The experiments are performed running codes in four different programming languages. The experimental results indicate that although there is no overall best method in all the considered programming languages, it is possible to make specific recommendations for each one."Ponencia en Congreso SAR Image segmentation based on multifractal features(2019-09) Pacheco, Cristian; Gambini, Juliana; Delrieux, Claudio"Synthetic Aperture Radar (SAR) imaging is based on airborne or satellite active microwave sensors that can capture the earth surface by emitting a signal and receiving the backscattered signal that forms the resulting image. Since microwave radiation is not interfered by sunlight and can pass through clouds, SAR imagery can be generated oblivious to weather and daylight conditions. However, the active nature of the imaging process determines that SAR images are contaminated by an inherent speckle noise that may degrade significantly the quality and usefulness of the images, and specific noise-removal processes may also filter out relevant textural information. In this article, we propose a texture-based method that can be applied for region segmentation in SAR imagery. The method is based on local analysis of the multifractal spectrum and a clustering procedure. The outcomes obtained both with synthetic and real SAR images show better region segmentation results than with state-of-the-art proposals."Ponencia en Congreso Speckle noise reduction in SAR images using information theory(2020) Chan, Debora; Gambini, Juliana; Frery, Alejandro C."In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Renyi entropies is proposed. The measure of similarity between a central window and patches of the image is based on a statistical test for comparing if two samples have the same entropy and hence have the same distribution. The results are encouraging, as the filtered image has better signal-to-noise ratio, it preserves the mean, and the edges are not severely blurred."Artículo de Publicación Periódica Statistical properties of the entropy from ordinal patterns(2022) Chagas, Eduarda T. C.; Frery, Alejandro C.; Gambini, Juliana; Lucini, María M.; Ramos, Heitor S.; Rey, Andrea"The ultimate purpose of the statistical analysis of ordinal patterns is to characterize the distribution of the features they induce. In particular, knowing the joint distribution of the pair entropy-statistical complexity for a large class of time series models would allow statistical tests that are unavailable to date. Working in this direction, we characterize the asymptotic distribution of the empirical Shannon’s entropy for any model under which the true normalized entropy is neither zero nor one. We obtain the asymptotic distribution from the central limit theorem (assuming large time series), the multivariate delta method, and a third-order correction of its mean value. We discuss the applicability of other results (exact, first-, and second-order corrections) regarding their accuracy and numerical stability. Within a general framework for building test statistics about Shannon’s entropy, we present a bilateral test that verifies if there is enough evidence to reject the hypothesis that two signals produce ordinal patterns with the same Shannon’s entropy. We applied this bilateral test to the daily maximum temperature time series from three cities (Dublin, Edinburgh, and Miami) and obtained sensible results."Artículo de Publicación Periódica Texture descriptors for robust SAR image segmentation(2022-12-28) Rey, Andrea; Gambini, Juliana; Delrieux, Claudio"SAR (synthetic aperture radar) and PolSAR (polarimetric synthetic aperture radar) images fulfill a fundamental role in Earth observation, due to their advantages over optical images. However, the presence of speckle noise hinders their automatic interpretation and unsupervised use, rendering traditional segmentation tools ineffective. For this reason, advanced image segmentation models are sought to overcome the limitations that make an adequate treat ment of speckled images difficult. We propose a procedure for SAR and PolSAR image clas sification, based on texture descriptors, that combines fractal dimension, a specific probability distribution function, Tsallis entropy, and the entropic index. A vector of local texture features is built using a set of reference regions, then a support vector machine classifier is applied. The proposed algorithm is tested with synthetic and actual monopolarimetric and polarimetric SAR imagery, exhibiting visually remarkable and robust results in coincidence with quantitative qual ity metrics as accuracy and F1-score."Ponencia en Congreso Time estimation of topotecan penetration in retinoblastoma cells through image sequence analysis(2020) Chan, Debora; Winter, Úrsula; Schaiquevich, Paula; Ramele, Rodrigo; Gambini, Juliana"Retinoblastoma is the most common intraocular tumor in childhood. Topotecan has been widely used as an antineoplastic agent for retinoblastoma treatment. Topotecan penetration into living tumorspheres is quantified using confocal microscopy. This fluorescent drug dyes the living tissue and it can be recorded in a sequence of images over a period of time. The effective penetration of the drug depends on culture characteristics and requires a very specific timing. This penetration time is calculated empirically by an expert. The purpose of this work is to offer a statistical model to automatically estimate the penetration time of topotecan in the cell, based on the information obtained from a sequence of tumorsphere images."Ponencia en Congreso Topotecan penetration assessment in retinoblastoma cells using Shannon entropy and coefficient of variation(2019-09) Howlin, Marcelo; Chan, Debora; Ramele, Rodrigo; Gambini, Juliana"Retinoblastoma is a common intraocular tumor of childhood. One of the medications used as an antineoplastic agent for retinoblastoma treatment is topotecan. Its penetration into living tumorspheres is quantified using confocal microscopy. Topotecan is a fluorescent drug and it dyes the living tissue. Then, it is recorded in a sequence of images over a period of time. The effective penetration of the drug depends on culture characteristics and requires a very specific timing which is calculated empirically by an expert. The purpose of this work is to offer a model to automatically estimate and evaluate the penetration time of topotecan in a cell, based on the information obtained from a sequence of tumorsphere images and using Shannon entropy and coefficient of variation."