In this paper, we propose and evaluate six methods for the segmentation of skin lesions in dermoscopic images. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al.[(C-LS)]. It also includes a set of methods developed by the authors which were tailored to this particular application (adaptive thresholding (AT), adaptive snake (AS), EM level set (EM-LS), and fuzzy-based split-and-merge algorithm (FBSM)]. The segmentation methods were applied to 100 dermoscopic images and evaluated with four different metrics, using the segmentation result obtained by an experienced dermatologist as the ground truth. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. The best fully automatic method was FBSM, with results only slightly worse than AS and EM-LS.

In the age of big data, time series are being generated in massive amounts. In the energy field, smart grids are enabling a unprecedented data acquisition with the integration of sensors and smart devices. In the context of renewable energies, there has been an increasing interest in solar photovoltaic energy generation. These installations are often integrated with smart sensors that measure the energy production. Such amount of data collected makes the quest for developing smart monitoring systems that can detect anomalous behaviour in these systems, trigger alerts and enable maintenance operations. In this paper, we propose a generic, unsupervised and scalable framework for anomaly detection in time series data, based on a variational recurrent autoencoder. Furthermore, we introduce attention in the model, by means of a variational self-attention mechanism (VSAM), to improve the performance of the encoding-decoding process. Afterwards, we perform anomaly detection based on the probabilistic reconstruction scores provided by our model. Our results on solar energy generation time series show the ability of the proposed approach to detect anomalous behaviour in time series data, while providing structured and expressive representations. Since it does not need labels to be trained, our methodology enables new applications for anomaly detection in energy time series data and beyond.

This letter presents a method for the separation between land and water in synthetic aperture radar (SAR) amplitude images. The proposed technique uses region-based level sets and adopts a mixture of lognormal densities as the probabilistic model for the pixel intensities in both water and land classes. The expectation-maximization algorithm is used to estimate the probability density functions for each class. Experimental results with real SAR images of riverbeds, flood extent areas, and shorelines demonstrate the good performance of the proposed algorithm compared with state-of-the-art approaches.

An approach to automatically detect impact craters on planetary surfaces is presented in this letter. It is built up from a boosting algorithm proposed by Viola and Jones (2004) whose simplicity combined with an original learning strategy leads to a fast and robust process with consistent results. The approach is validated with image data sets from Mars surface captured by the Mars Orbiter Camera onboard Mars Global Surveyor probe.