Aerial scene classification in remote sensing presents a significant challenge due to high intra-class variability and the different scales and orientations of the objects within dataset images. While deep learning architectures are commonly used for scene classification tasks and in the remote sensing area, Deep metric learning (DML) offers a more adaptable solution for more challenging classification scenarios by learning the characteristics of each class. This study exploits the usage of DML approaches for aerial scene classification tasks, analyzing their behavior with different pre-trained Convolutional Neural Networks (CNNs), and their combination through evolutionary computation algorithms. Our experiments show that DML approaches can achieve better classification results as compared to traditional pre-trained CNNs for three well-known remote sensing aerial scene datasets. Furthermore, we found that using the Univariate Marginal Distribution Algorithm (UMDA) to construct the final ensemble of varied DML-based classifiers is essential for achieving consistency and high-accuracy results across all datasets, improving the state-of-the-art by over 5.6%.

Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI). Although being highly accurate, deep CNN models lack transparency and interpretability, precluding adequate clinical reasoning and not complying with most current regulatory demands. One popular choice for explaining deep image models is occluding regions of the image to isolate their influence on the prediction. However, existing methods for occluding patches of brain scans generate images outside the distribution to which the model was trained for, thus leading to unreliable explanations. In this paper, we propose an alternative explanation method that is specifically designed for the brain scan task. Our method, which we refer to as Swap Test, produces heatmaps that depict the areas of the brain that are most indicative of AD, providing interpretability for the model's decisions in a format understandable to clinicians. Experimental results using an axiomatic evaluation show that the proposed method is more suitable for explaining the diagnosis of AD using MRI while the opposite trend was observed when using a typical occlusion test. Therefore, we believe our method may address the inherent black-box nature of deep neural networks that are capable of diagnosing AD.

Evaluating medical imaging segmentation is a very complex problem. Several papers proposed methodologies and different metrics pursuing more reliable and unbiased procedures. In this paper, we propose a novel accuracy metric which is more balanced than the well known Dice and Jaccard coefficients. We also prove mathematically that the proposed metric generalizes Dice, Jaccard and the previously proposed Balanced Dice and Balanced Jaccard coefficients. Our experiments show that significant changes in brain tissue segmentation evaluation results are noticed as we applied our new metric.

High-frequency noise is present in several modalities of medical images. It originates from the acquisition process, scanner configurations, the scanned body, or to other external factors. This way, prospective filters are an important tool to improve image quality. In this paper, we propose a non-local weighted operational anisotropic diffusion filter and evaluate its effect on magnetic resonance images and on kV/CBCT radiotherapy images. We also provide a detailed analysis of non-local parameter settings. Results show that the new filter enhances previous local implementations and has potential application in radiotherapy treatments.

This work presents the development and imple- mentation of an autonomous construction system which uses a holonomic mobile robot for constructing three-dimensional modular structures from parts of different size. The main contribution of this paper is to propose a novel architectural framework for planning the construction task by reducing the total traveled distance and learning time with respect to the state of the art. A high-level planner based on Parameterized Learning Automata (PLA) and $\mathrm {A}^{\ast }$ algorithms uses environmental feedback to generate the robot actions for accomplishing the task. The robotic platform is equipped with a magnetic gripper and also a RGB-D camera and other additional sensors to aid the control and navigation of the robot. The simultaneous mapping and robot location are performed using the Real-Time Appearance-Based Mapping (RTAB-Map) algorithm. The RTAB-Map algorithm uses three-dimensional image information and odometry data to create the map environment and also to estimate the pose of the robot. Simulation and experimental trials have been performed to assess the effectiveness of the proposed robotic assembly system.

Many papers are published every year containing new methodologies for brain tissue segmentation in magnetic resonance images. The evaluation of these methods is fundamental to understand their behavior and to observe their weak and strong points. Even though improvements have been proposed, the analysis of the segmentation results can still lead to incorrect conclusions. This paper contains an investigation of the state-of-the-art in brain tissue segmentation evaluation, which includes tissue classification or segmentation, handling partial volume effect, and evaluation metrics. It uncovers previously unnoticed pitfalls and proposes standard procedures to avoid them. Experiments show that the proposed evaluation strategy gives a better insight about the method's strong and weak points.

We present a novel approach for phase denoising in Interferometric Synthetic Aperture Radar (InSAR) images, named as Block-Matching InSAR (BMInSAR). It uses k-means clustering to solve the block matching similarity search problem, thus simplifying preprocessing steps and filtering several reference-blocks at once. Also, we propose a novel methodology based on ground-truth GPS measurements to assess the filtering quality of Digital Elevation Models (DEMs) derived from a pair of Very High-Resolution (VHR) SAR complex images. Our dataset was obtained by X-Band airborne sensor OrbiSAR-2 from BRADAR. BMInSAR significantly outperforms the state-of-the-art filtering methods in both accuracy and execution time. After filtering with BMInSAR, we achieved an accuracy of 21cm in the resulting DEM of a homogeneous lawn area, which is quite similar to that obtained by LiDAR technology.

Brain connectivity analysis is a critical component of ongoing human connectome projects to decipher the healthy and diseased brain. Recent work has highlighted the power-law (multi-time scale) properties of brain signals; however, there remains a lack of methods to specifically quantify short- vs. long- time range brain connections. In this paper, using detrended partial cross-correlation analysis (DPCCA), we propose a novel functional connectivity measure to delineate brain interactions at multiple time scales, while controlling for covariates. We use a rich simulated fMRI dataset to validate the proposed method, and apply it to a real fMRI dataset in a cocaine dependence prediction task. We show that, compared to extant methods, the DPCCA-based approach not only distinguishes short and long memory functional connectivity but also improves feature extraction and enhances classification accuracy. Together, this paper contributes broadly to new computational methodologies in understanding neural information processing.

A new algorithm, named Connected Oriented Image Foresting Transform (COIFT), is proposed, which provides global optimum solutions according to a graph-cut measure, subject to high-level boundary constraints. COIFT incorporates the connectivity constraint in the Oriented Image Foresting Transform (OIFT), ensuring the generation of connected objects, and can also handle simultaneously the boundary polarity. While the connectivity constraint usually leads to NP-hard problems in other frameworks, such as the min-cut/max-flow algorithm, COIFT conserves the low complexity of the OIFT algorithm. Experiments show that COIFT can improve the segmentation of thin and elongated objects, for the same amount of user interaction.

Cocaine dependence devastates millions of human lives. Despite of a variety of treatments, there is a very high rate of individual relapse to drug use. In the last decade, functional magnetic resonance imaging (fMRI) proved to be a powerful tool to diagnose and understand different pathologies. This work provides advances in the identification of cocaine dependence and in the relapse prediction based on fMRI classification. We improve the traditional methodology of the literature called multi-voxel pattern analysis (MVPA), which is used for feature extraction and classification. In addition, we propose new features that use specific functional connectivity measures. An extensive evaluation was conducted comparing our methodology with MVPA, as well as, several learning methods with distinct feature sets. We could identify the neural patterns that lead to improve classification accuracies and evaluate the advantages of employing an information fusion approach through an ensemble of classifiers. Experimental results show an improvement of final accuracy over the state-of-the-art methods.

Remote sensing has been widely applied for environmental monitoring by means of change detection techniques, commonly for identifying deforestation signs which is the gateway for illegal activities such as uncontrolled urban growth and grazing pasture. Monthly acquired X-Band images from airborne Synthetic Aperture Radar (SAR) provided multi-temporal scenes employed in this work resulting in environmental incident reports forwarded to the responsible authorities. The present work proposes the use of both, Superpixel segmentation by Simple Linear Iterative Clustering (SLIC) and change detection by Object Correlation Images (OCI) not yet applied to multi-temporal X-Band high resolution SAR images, and the application of a simple Multilayer Perceptron (MLP) supervised learning technique for detecting and classifying the changes into relevant activities. Experiments have been performed using acquired SAR imagery from BRADAR airborne sensor OrbiSAR-2 under Brazilian Atlantic Forest which revealed possible deforestation activities comparing achieved results with those obtained with experts.

In this work, we propose a novel approach for improving center of gravity (COG) estimation of the brain in magnetic resonance (MR) images, that uses 3D Haar-like features. We hypothesize that better pose estimation will advance the posterior skull-stripping results of the popular Brain Extraction Tool (BET). The proposed methodology is quantitatively validated in 20 T1- and T2-weighted images of the brain. As compared to the native BET COG algorithm, our method produced COGs 87.3% closer to the expected coordinates for the T1-weighted dataset, and importantly this resulted in an average enhancement of 15.4% to the accuracy of skull-stripping masks. As far the authors know, we are first in analyzing the impact of COG estimation over skull-stripping of MR images.

Automatic MR-image segmentation of brain tissues is an important issue in neuroimaging. For instance, it is a key methodological component of a popular technique denominated voxel-based morphometry (VBM), which quantifies gray-matter (GM) volumes from MR images. However, segmentation accuracy in some subcortical regions on the basis of extant methods is not satisfactory, compromising VBM results. We combine a probabilistic atlas and a fast clustering approach based on optimum connectivity between voxels in their feature space. The algorithm exploits local image properties and global information from the atlas as features to group GM and white-matter (WM) voxels in distinct clusters, and uses the total probability values inside the clusters to label them as GM or WM. This new method is validated in the region of the thalamus and outperformed two widely used methods packaged in SPM and FSL.

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain.

The image foresting transform (IFT) is e a generic technique that uses simple variations of the same core algorithm to construct many image processing operators like watershed transforms, edge tracking, geodesic paths, among others. In this paper we propose the silicon IFT (SIFT), an FPGA-based architecture that leverages on the IFT flexibility to build a fast image processing architecture capable of implementing IFT operators in hardware. Our experiments have shown that SIFT can reach speedups of 5600 upon the correspondent software implementation. Moreover, they exhibit excellent execution times as compared to recent dedicated image processing architectures.