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SAR image segmentation plays a central role in geoscience and remote sensing of the environment. Recently, methodologies that apply traditional segmentation algorithms to maps of statistical information extracted from SAR image rather than to the raw data itself have shown promising results. Nonetheless, the application of more powerful segmentation methods to these maps is constrained by the lack of adequate statistical models for such data. In this letter, we present a level-set-based algorithm that embodies much of the data statistics without assuming any prior model for it. We also evaluated its performance on both real and synthetic SAR images.

This letter proposes an unsupervised algorithm for synthetic aperture radar (SAR) image segmentation based on the statistical properties of log-cumulants estimates of SAR data modeled by the $G^{0}$ distribution. We also investigate its application in SAR images for remote sensing of environment. The proposed method adjusts a multi-class two-Gaussian mixture to the distributional parameters estimated by the log-cumulants method. Our method generates an initial classification using the Gaussian mixture and the probability density of the $G^{0}$ parameters to provide the training samples for the support vector machine (SVM) algorithm. For comparison purpose, we used three classifiers from literature: a Bayesian approach, an enhanced $K$ -nearest neighbors method, and an iterative SVM. The evaluation of the results was performed in terms of the error of segmentation (EoS) and cross region fitting (CRF) measures. The segmentation results, based on EoS and CRF, showed that our method was more efficient than the three classifiers for synthetic and real SAR images.

An efficient strategy of image processing algorithms to deal with the speckle noise is to incorporate data knowledge and models into them. In this letter, we introduce a hierarchical level set algorithm, which is fast and precise for multiregion segmentation of synthetic aperture radar (SAR) images. Our algorithm performs curve regularization with a nonparametric median filter instead of using the curvature formulation, and hence it reduces the computation time. The proposed algorithm also replaces the front propagation derivatives by morphological operations, and finally, the arithmetic-geometric distance measures the contrast between regions and controls the hierarchical segmentation. We conducted experiments on synthetic and real SAR images modeled by the $\mathcal {G}_{I}^{0}$ distribution. The performance evaluation of the proposed algorithm and two related methods comprises the computation time and measures based on segmentation accuracy and stochastic distance. Overall, our segmentation algorithm performed faster and more precise on both synthetic and real SAR images.

Engineering education as a field of study is young, and the number of experienced researchers investigating the complex learning questions associated with becoming an engineer still sparse [44]. In this perspective, the following research question can be outlined: Do educational resources that are available in online engineering courses, such as Virtual Learning Environment, Content Instructional Design and Simulations, impact the effectiveness of learning? The main objective of this article is to statistically assess the effectiveness of e-learning courses in engineering. To fulfill the research objective, the use of the investigated learning resources was observed in a distance learning course, and an evaluation instrument was adapted, validated and applied to the students. To test the learning effectiveness, multivariate statistical methods were used to analyze the instrument, namely: Principal Component Analysis (PCA) and canonical correlation analysis. The multivariate analysis with the canonical correlation discloses how the positive contribution of the two groups arises from the PCA application: Functionality of Virtual Environment and Design Content and Simulations in learning effectiveness.

Synthetic aperture radar (SAR) image segmentation is an important task in image processing. However, classic segmentation techniques are inadequate due to the presence of speckle noise. In this paper, we present a methodology for SAR image segmentation that uses the matrix of Renyi's entropy. This matrix arises from SAR data that follows the $G_A^0$ model, and here, it is an input to segmentation methods. For performance evaluation of the proposed methodology, we employ the error of segmentation, the cross-region fitting index, the Dice measure, as well as the rates of false positives and negatives. Tests have been performed on synthetic and real SAR images and the matrix of entropy has improved the results, regardless of the increase of the number of looks. The Otsu's method of global thresholding produced good segmentation results when applied to this matrix.

This paper presents a simulator to help electrical engineering students and professionals in the learning process. The proposed tool embodies Virtual Reality technologies on the improvement of understanding equipment maneuvers in distribution power systems. The tool has a virtual didactic distribution network in which the students handle several real tasks related to operation and maintenance of distribution network through applied technology. We hope that this tool can contribute to develop and improve professional skills in engineering at a low cost and furthermore it supports laboratory and associated technical activities.

This paper presents a new method for Optic Disc (OD) detection in color retinal images. Processing and analyzing these images constitute a relevant task to help specialists in eye diseases detection. Particularly, finding OD in a retinal fundus image, improves significantly the chances to detect diseases. OD location serves as input to the detection of other retinal anatomical structures such as macula, blood vessels and some anomalies, such as exudates, hemorrhages and drusen. These anomalies will serve to determine the presence of retinal diseases. We have implemented five OD detection methods from state of art and created a committee of algorithms. Unlike other proposals, based on simple majority vote, the output of the proposed committee is established using a weighted voting obtained by each algorithm. For the definition of the weights we use a portion of available image databases and calculate the success rate of each of the five methods. Tests were carried on six public benchmark databases, which constitute a total of 1566 images.