Abstract
The purpose of this work is to address the imageCLEF 2019 coral challenge - to develop a system for the detection and identification of substrates in coral images. Initially a revision of the 13 classes was carried out by identifying a number of sub-classes for some substrates. Four features were considered - 3 related to greyscale intensity (1) and texture (2), and 1 related to the colour content. The Breiman's Random forest algorithm was used to classify the corals in one of 13 classes defined. A classification accuracy of about 49% was obtained.

Abstract
A method was developed to measure the surface area of walnut fruit phytopathogenic lesions from images acquired with a basic calibration target. The fruit is modelled by a spheroid, established from the 2D view ellipse using an iterative process. The method was tested with images of colour circular marks placed on a wooden spheroid. It proved effective in the estimation of the spheroid semi-diameters (average relative errors of 0.8% and 1.0%), spheroid surface (1.77%) and volume (2.71%). The computation of the colour mark surface area was within the expected error, considering the image resolution (up to about 4%), for 22 out of 28 images tested. © 2019, Springer Nature Switzerland AG.

Abstract
The analysis of the intern anatomy of wood samples for species identification is a complex task that only experts can perform accurately. Since there are not many experts in the world and their training can last decades, there is great interest in developing automatic processes to extract high-level information from microscopic wood images. The purpose of this work was to develop algorithms that could provide meaningful information for the classification process. The work focuses on hardwoods, which have a very diverse anatomy including many different features. The ray width is one of such features, with high diagnostic value, which is visible on the tangential section. A modified distance function for the DBSCAN algorithm was developed to identify clusters that represent rays, in order to count the number of cells in width. To test both the segmentation and the modified DBSCAN algorithms, 20 images were manually segmented, obtaining an average Jaccard index of 0.66 for the segmentation and an average index M=0.78 for the clustering task. The final ray count had an accuracy of 0.91. © 2019, Springer Nature Switzerland AG.

Abstract
Image segmentation is widely used in image processing, particularly when there is one or few objects of interest. The segmentation of multi-spectral remote sensing images is more challenging due to the large number and diversity of the objects of interest, and the difficulty in having ground truth to tune the segmentation algorithm parameters and to evaluate the results produced. The Synthetic Image TEsting Framework (SITEF) is a tool to address these issues. As the shape and location of the objects in a synthetic image are known, it provides references to be used for quantitative evaluation of the segmentation results. This paper presents SITEF with an experiment to evaluate the segmentation of a SENTINEL-2 image using the software SNAP.

Abstract
Machine learning, a subfield of artificial intelligence, has been widely used to automate tasks usually performed by humans. Some applications of these techniques are understanding network traffic behavior, predicting it, classifying it, fixing its faults, identifying malware applications, and preventing deliberate attacks. The goal of this work is to use machine learning algorithms to classify, in separate procedures, the errors of the network, their causes, and possible fixes. Our application case considers the WiBACK wireless system, from which we also obtained the data logs used to produce this paper. WiBACK is a collection of software and hardware with auto-configuration and self-management capabilities, designed to reduce CAPEX and OPEX costs. A principal components analysis is performed, followed by the application of decision trees, k nearest neighbors, and support vector machines. A comparison between the results obtained by the algorithms trained with the original data sets, balanced data sets, and the principal components data is performed. We achieve weighted F1-score between 0.93 and 0.99 with the balanced data, 0.88 and 0.96 with the original unbalanced data, and 0.81 and 0.89 with the Principal Components Analysis. © 2020, Springer Nature Switzerland AG.

Abstract
The extraction of accurate geometric measurements from images normally requires the use of metric cameras and stereoscopic observations. However, as good-quality digital cameras are widely available in mobile devices (smartphones, tablets), there is great interest to develop alternative approaches, suitable for these devices. This paper presents a methodology to compute the surface area and volume of a spheroid-shaped object, such as many types of fruit, from a single image acquired by a standard (non-metric) camera and a basic calibration target. An iterative process is used to establish a 3D spheroid out of the observed 2D ellipse, after which auxiliary images of height, resolution and surface area of each pixel on the 3D object surface are created. The method was tested with a data set of 2400 images, of 10 different objects, 2 calibration targets, 2 cameras and 2 mark types. The average relative errors (< d>) in establishing the 3D object semi-diameters were 0.863% and 0.791%. The semi-diameters are used to compute the object’s surface area (< d> = 1.557%) and volume (< d> = 2.365%). The estimation of the sub-region (mark) surface area over the modelled 3D object resulted in < d> = 2.985%, much lower that what is obtained ignoring the fact that the mark is not on the reference (calibration) plane (< d> = 50.7%), thus proving the effectiveness of the proposed iterative process to model the 3D object (spheroid). © 2020, Springer Nature Singapore Pte Ltd.