A robust feature tracker for active surveillance of outdoor scenes

In this paper, we propose a robust real-time object detection system for outdoor image sequences acquired by an active camera. The system is able to compensate background changes due to the camera motion and to detect mobile objects in the scene. Background compensation is performed by assuming a simple translation (displacement vector) of the background from the previous to the current frame and by applying the well-known tracker proposed by Lucas and Kanade. A reference map containing all well trackable features is maintained and updated by the system at each frame by introducing new good features related to new regions that appear in the current image. A new method is applied to reject badly tracked features. The current frame and the background after compensation are processed by a change detection method in order to locate mobile objects. Results are presented in the contest of a visual-based surveillance system for monitoring outdoor enviroments

A Neural Network for Image Anomaly Detection with Deep Pyramidal Representations and Dynamic Routing

Image anomaly detection is an application-driven problem where the aim is to identify novel samples, which differ significantly from the normal ones. We here propose Pyramidal Image Anomaly DEtector (PIADE), a deep reconstruction-based pyramidal approach, in which image features are extracted at different scale levels to better catch the peculiarities that could help to discriminate between normal and anomalous data. The features are dynamically routed to a reconstruction layer and anomalies can be identified by comparing the input image with its reconstruction. Unlike similar approaches, the comparison is done by using structural similarity and perceptual loss rather than trivial pixel-by-pixel comparison. The proposed method performed at par or better than the state-of-the-art methods when tested on publicly available datasets such as CIFAR10, COIL-100 and MVTec

Variabilidade genética de duas populações de Astyanax altiparanae da bacia do alto rio Paraná.

O presente trabalho tem como objetivo caracterizar a estrutura genética de duas populações de A. altiparanae da bacia do alto rio Paraná (Paranapanema e Tietê) através da análise de 11 marcadores moleculares do tipo microssatélite.Organizado por: Sílvio Ricardo Maurano; AQUACIÊNCIA 2012

Performance evaluation of a Wi-Fi-based multi-node network for distributed audio-visual sensors

The experimental research described in this manuscript proposes a complete network system for distributed multimedia acquisition by mobile remote nodes, streaming to a central unit, and centralized real-time processing of the collected signals. Particular attention is placed on the hardware structure of the system and on the research of the best network performances for an efficient and secure streaming. Specifically, these acoustic and video sensors, microphone arrays and video cameras respectively, can be employed in any robotic vehicles and systems, both mobile and fixed. The main objective is to intercept unidentified sources, like any kind of vehicles or robotic vehicles, drones, or people whose identity is not a-priory known whose instantaneous location and trajectory are also unknown. The proposed multimedia network infrastructure is analysed and studied in terms of efficiency and robustness, and experiments are conducted on the field to validate it. The hardware and software components of the system were developed using suitable technologies and multimedia transmission protocols to meet the requirements and constraints of computation performance, energy efficiency, and data transmission security

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight

Dynamic instance generation for few-shot handwritten document layout segmentation (short paper)

Historical handwritten document analysis is an important activity to retrieve information about our past. Given that this type of process is slow and time-consuming, the humanities community is searching for new techniques that could aid them in this activity. Document layout analysis is a branch of machine learning that aims to extract semantic informations from digitised documents. Here we propose a new framework for handwritten document layout analysis that differentiates from the current state-of-the-art by the fact that it features few-shot learning, thus allowing for good results with little manually labelled data and the dynamic instance generation process. Our results were obtained using the DIVA - HisDB dataset

Efeito da tostagem de barricas de carvalho sobre o perfil químico e sensorial de vinhos tintos.

XV Congresso Latino-Americano de Viticultura e Enologia E XIII Congresso Brasileiro de Viticultura e Enologia. Bento Gonçalves-RS, 3 a 7 de Novembro de 2015

Bionics-based surgical training using 3D printed photopolymers and smart devices

Additive manufacturing technologies support the realization of surgical training devices using, typically, photopolymers-based materials. Unfortunately, the material jetting family, able to print a large range of soft and hard polymers, requires expensive machines and materials, which are not always available. On the other hand, vat polymerization fails in the resolution/volume ratio and in the mechanical properties reconstruction. Stereolithographic 3D printers, mostly used in dental surgery, make possible to realize cheap and sustainable models for training activity using only one material, reducing the possibility to obtain different mechanical characteristics. Moreover, the printed objects have to be treated (i.e. curing post-processing) in order to obtain the required performances, that could be preserved for long term storing. The aim of the proposed approach is to assure the surgeons' skills improvement through bionic-based surgical 3D printed models and smart devices, able to reproduce the same perception of a real surgical activity. We demonstrated how it is possible develop smart devices capable to take into account the same characteristics of different materials (i.e. bone and spongy bone) even if stored for a long time