Material recognition by feature classification using time-of-flight camera

We propose a method for solving one of the significant open issues in computer vision: material recognition. A time-of-flight range camera has been employed to analyze the characteristics of different materials. Starting from the information returned by the depth sensor, different features of interest have been extracted using transforms such as Fourier, discrete cosine, Hilbert, chirp-z, and Karhunen-Loève. Such features have been used to build a training and a validation set useful to feed a classifier (J48) able to accomplish the material recognition step. The effectiveness of the proposed methodology has been experimentally tested. Good predictive accuracies of materials have been obtained. Moreover, experiments have shown that the combination of multiple transforms increases the robustness and reliability of the computed features, although the shutter value can heavily affect the prediction rates

Helipad detection for accurate UAV pose estimation by means of a visual sensor

In this article, we tackle the problem of developing a visual framework to allow the autonomous landing of an unmanned aerial vehicle onto a platform using a single camera. Specifically, we propose a vision-based helipad detection algorithm in order to estimate the attitude of a drone on which the camera is fastened with respect to target. Since the algorithm should be simple and quick, we implemented a method based on curvatures in order to detect the heliport marks, that is, the corners of character H. By knowing the size of H mark and the actual location of its corners, we are able to compute the homography matrix containing the relative pose information. The effectiveness of our methodology has been proven through controlled indoor and outdoor experiments. The outcomes have shown that the method provides high accuracies in estimating the distance and the orientation of camera with respect to visual target. Specifically, small errors lower than 1% and 4% have been achieved in the computing of measurements, respectively

A Modified Iterative Closest Point Algorithm for 3D Point Cloud Registration

In this article, an accurate method for the registration of point clouds returned by a 3D rangefinder is presented. The method modifies the well-known iterative closest point (ICP) algorithm by introducing the concept of deletion mask. This term is defined starting from virtual scans of the reconstructed surfaces and using inconsistencies between measurements. In this way, spatial regions of implicit ambiguities, due to edge effects or systematical errors of the rangefinder, are automatically found. Several experiments are performed to compare the proposed method with three ICP variants. Results prove the capability of deletion masks to aid the point cloud registration, lowering the errors of the other ICP variants, regardless the presence of artifacts caused by small changes of the sensor view-point and changes of the environment

A technology platform for automatic high-level tennis game analysis

Sports video research is a popular topic that has been applied to many prominent sports for a large spectrum of applications. In this paper we introduce a technology platform which has been developed for the tennis context, able to extract action sequences and provide support to coaches for players performance analysis during training and official matches. The system consists of an hardware architecture, devised to acquire data in the tennis context and for the specific domain requirements, and a number of processing modules which are able to track both the ball and the players, to extract semantic information from their interactions and automatically annotate video sequences. The aim of this paper is to demonstrate that the proposed combination of hardware and software modules is able to extract 3D ball trajectories robust enough to evaluate ball changes of direction recognizing serves, strokes and bounces. Starting from these information, a finite state machine based decision process can be employed to evaluate the score of each action of the game. The entire platform has been tested in real experiments during both training sessions and matches, and results show that automatic annotation of key events along with 3D positions and scores can be used to support coaches in the extraction of valuable information about players intentions and behaviours

Assessing the effects of hydrological and chemical stressors on macroinvertebrate community in an Alpine river: the Adige river as a case study

In this study, the combined effects of hydrological and chemical stressors on benthic macroinvertebrates were evaluated in order to explore the response of the biological community to multiple stressors. The Adige River, located in the south‐eastern Alps, was selected as a case study because representative of the situation of a large river in which the variety of stressors present in the Alpine region act simultaneously. As expected, streamflow showed a seasonal pattern, with high flows in the spring-summer period; however, locally, the natural hydrological regime was altered by the presence of hydropower systems, which chiefly affected low flows. Multivariate analysis showed seasonal and spatial patterns in both chemical and hydrological parameters with a clear gradient in the concentration of nitrate, personal care, and pharmaceutical products moving from headwaters to the main stem of the river. The macroinvertebrate community composition was significantly different in summer and winter and between up and downstream sites. Streamflow alteration chiefly due to water use by hydropower affected community composition but not richness or diversity. Gammarus sp., Hirudinea, and Psychomyia sp., were positively correlated with flow variability, increasing their densities in the sites with higher streamflow variability because of hydropeaking. The results obtained in this study show that the composition of the macroinvertebrate community responded to seasonality and to changes in the main stressors along the river and highlights the importance of the spatial and temporal variability of stressors in this Alpine river. Taking into account, this variability will help the decision‐making process for improving basin management

Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry

: In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy

Multicritical behavior in the fully frustrated XY model and related systems

We study the phase diagram and critical behavior of the two-dimensional square-lattice fully frustrated XY model (FFXY) and of two related models, a lattice discretization of the Landau-Ginzburg-Wilson Hamiltonian for the critical modes of the FFXY model, and a coupled Ising-XY model. We present a finite-size-scaling analysis of the results of high-precision Monte Carlo simulations on square lattices L x L, up to L=O(10^3). In the FFXY model and in the other models, when the transitions are continuous, there are two very close but separate transitions. There is an Ising chiral transition characterized by the onset of chiral long-range order while spins remain paramagnetic. Then, as temperature decreases, the systems undergo a Kosterlitz-Thouless spin transition to a phase with quasi-long-range order. The FFXY model and the other models in a rather large parameter region show a crossover behavior at the chiral and spin transitions that is universal to some extent. We conjecture that this universal behavior is due to a multicritical point. The numerical data suggest that the relevant multicritical point is a zero-temperature transition. A possible candidate is the O(4) point that controls the low-temperature behavior of the 4-vector model.Comment: 62 page

Minimally-invasive treatments for benign thyroid nodules: a Delphi-based consensus statement from the Italian minimally-invasive treatments of the thyroid (MITT) group

Benign thyroid nodules are a common clinical occurrence and usually do not require treatment unless symptomatic. During the last years, ultrasound-guided minimally invasive treatments (MIT) gained an increasing role in the management of nodules causing local symptoms. In February 2018, the Italian MIT Thyroid Group was founded to create a permanent cooperation between Italian and international physicians dedicated to clinical research and assistance on MIT for thyroid nodules. The group drafted this list of statements based on literature review and consensus opinion of interdisciplinary experts to facilitate the diffusion and the appropriate use of MIT of thyroid nodules in clinical practice. (#1) Predominantly cystic/cystic symptomatic nodules should first undergo US-guided aspiration; ethanol injection should be performed if relapsing (level of evidence [LoE]: ethanol is superior to simple aspiration = 2); (#2) In symptomatic cystic nodules, thermal ablation is an option when symptoms persist after ethanol ablation (LoE = 4); (#3) Double cytological benignity confirmation is needed before thermal ablation (LoE = 2); (#4) Single cytological sample is adequate in ultrasound low risk (EU-TIRADS 643) and in autonomously functioning nodules (LoE = 2); (#5) Thermal ablation may be proposed as first-line treatment for solid, symptomatic, nonfunctioning, benign nodules (LoE = 2); (#6) Thermal ablation may be used for dominant lesions in nonfunctioning multinodular goiter in patients refusing/not eligible for surgery (LoE = 5); (#7) Clinical and ultrasound follow-up is appropriate after thermal ablation (LoE = 2); (#8) Nodule re-treatment can be considered when symptoms relapse or partially resolve (LoE = 2); (#9) In case of nodule regrowth, a new cytological assessment is suggested before second ablation (LoE = 5); (#10) Thermal ablation is an option for autonomously functioning nodules in patients refusing/not eligible for radioiodine or surgery (LoE = 2); (#11) Small autonomously functioning nodules can be treated with thermal ablation when thyroid tissue sparing is a priority and 6580% nodule volume ablation is expected (LoE = 3)