In this paper a fast and innovative three-dimensional vision system, having high resolution in the surface reconstruction, is discussed. It is based on a triangulation 3D laser scanner with a linear beam shape. The high precision (few microns) is guaranteed by very small laser line width, small camera pixel-size and proper optical properties of the Telecentric Lens. The entire system has been tested on two kinds of sample objects such as a 20 cent coin and a set of precision drilling tools. The main purpose of this work is the detection and reconstruction of the 3D surface of tiny objects and the measurement of their surface defects with high accuracy. Furthermore the occlusion problem is faced and solved by properly handling the camera-laser setup. Experimental tests prove the high precision of the system that can reach a resolution of 15 ?m. © 2013 IEEE.

In this paper, an accurate range sensor for the three-dimensional reconstruction of environments is designed and developed. Following the principles of laser profilometry, the device exploits a set of optical transmitters able to project a laser line on the environment. A high-resolution and high-frame-rate camera assisted by a telecentric lens collects the laser light reflected by a parabolic mirror, whose shape is designed ad hoc to achieve a maximum measurement error of 10 mm when the target is placed 3 m away from the laser source. Measurements are derived by means of an analytical model, whose parameters are estimated during a preliminary calibration phase. Geometrical parameters, analytical modeling and image processing steps are validated through several experiments, which indicate the capability of the proposed device to recover the shape of a target with high accuracy. Experimental measurements show Gaussian statistics, having standard deviation of 1.74 mm within the measurable range. Results prove that the presented range sensor is a good candidate for environmental inspections and measurements.

Wide field of view 3-D scanners are strategic in many contexts, above all, infrastructure inspection and vehicle/robot automatic motion. In this paper we present a patented sensor able to recover three-dimensional data and perform an omnidirectional highly accurate environmental reconstruction, mainly focusing on a design strategy for setting up the geometric parameters. Performance of a prototypal implementation are reported, and 3-D reconstructions are shown.

One of the first tasks executed by a vision system made of fixed cameras is the background (BG) subtraction and a particularly challenging context for real time applications is the athletic one because of illumination changes, moving objects and cluttered scenes. The aim of this work is to extract a BG model based on statistical likelihood able to process color filter array (CFA) images taking into account the intrinsic variance of each gray level of the sensor, named Likelihood Bayer Background (LBB). The BG model should be not so computationally complex while highly responsive to extract a robust foreground. Moreover, the mathematical operations used in the formulation should be parallelizable, working on image patches, and computationally efficient, exploiting the dynamics of a pixel within its integer range. Both simulations and experiments on real video sequences demonstrate that this BG model approach shows great performances and robustness during the real time processing of scenes extracted from a soccer match.

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.

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.

Background (BG) modelling is a key task in every computer vision system (CVS) independently of the final purpose for which it is designed. Even if many BG approaches exist (for example Mixture of Gaussians or Eigenbackground), they can not efficiently process real time videos due to the model complexity and to the high throughput of the video flux. One of the most challenging real time applications is the athletic scene processing, because, in this context, there are many critical aspects for defining a BG model: no a-priori knowledge of the static scene, sudden illumination changes and many moving objects that slow down the upgrade phase. The aim of this work is to provide an adaptive BG model able to deal with high frame rate videos (>= 100 fps) in real time processing, and suitable for smart cameras embedding, finding a good compromise between the model complexity and its responsiveness. Real experiments demonstrate that this BG model approach shows great performances and robustness during the real time processing of athletic video frames, up to 100 fps. Copyright 2014 ACM.

In this paper, we propose an embedded vision system based on laser profilometry able to get the pose of a vehicle and its relative displacements with reference to the constitutive media of a structured environment. Fundamental equations for laser triangulation are developed and encoded for their actual implementation on an embedded system. It is made of a laser source that projects a line-shaped beam onto the environment and an on-chip camera able to frame the laser light. Images are then sent to the inexpensive Raspberry Pi onboard computer, which is responsible for processing tasks. For the first time, laser profilometry is coupled with the correlation of laser signatures on a low-cost and low-resource processing board for vehicle localization purposes. Several validation tests of the proposed sensor have proven the effectiveness of the system with respect to commercially available sensors such as inductive sensors and standard odometers, which fail when the vehicle crosses path interceptions or its wheels undergo unavoidable slippages. Moreover, further comparisons with other vision-based techniques have also proven the good performances of this embedded system for real-time localization of vehicles.

Wide field of view 3-D scanners are strategic in manycontexts, above all, infrastructure inspection and vehicle/robotautomatic motion. In this paper we present a patented sensorable to recover three-dimensional data and perform anomnidirectional highly accurate environmental reconstruction,mainly focusing on a design strategy for setting up the geometricparameters. Performance of a prototypal implementation arereported, and 3-D reconstructions are shown.

In this paper, an approach based on the analysis of variance (ANOVA) for the extraction of crop marks from aerial images is improved by means of preliminary analyses and semantic processing of the extracted objects. The paper falls in the field of digitalization of images for archaeology, assisting expert users in the detection of un-excavated sites. The methodology is improved by a preliminary analysis of local curvatures, able to determine the most suitable direction for the ANOVA formulation. Then, a semantic processing, based on the knowledge of the shape of the target wide line, is performed to delete false positive detections. Sample analyses are always performed on actual images and prove the capability of the method to discriminate the most significant marks, aiding archaeologists in the analysis of huge amount of data.

In this paper we present a reliable method to derive the differences between indoor environments using the comparison of high-resolution range images. Samples belonging to different acquisitions are firstly reduced preserving the topology of the scenes and then registered in the same system of reference through an iterative least-squares algorithm, aided by a deletion mask, whose assignment is the removal of implicit errors due to the different points of view of each orthographic acquisition. Finally the analysis of the exact range measures returns an intuitive difference map that allows the fast detection of the positions of the altered regions within the scenes. Numerical experiments are presented to prove the capability of the method for the comparison of scenes regardless the resolution of the sensor and the input noise level of such measurements. © 2013 IEEE.

This paper presents a complete framework aimed to nondestructive inspection of composite materials. Starting from the acquisition, performed with lock-in thermography, the method flows through a set of consecutive blocks of data processing: input enhancement, feature extraction, classification and defect detection. Experimental results prove the capability of the presented methodology to detect the presence of defects underneath the surface of a calibrated specimen made of Glass Fiber Reinforced Polymer (GFRP). Results are also compared with those obtained by other techniques, based on different features and unsupervised learning methods. The comparison further proves that the proposed methodology is able to reduce the number of false positives, while ensuring the exact detection of subsurface defects.

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 high-resolution vision system for the inspection of drilling tools is presented. A triangulation-based laser scanner is used to extract a three-dimensional model of the target aimed to the fast detection and characterization of surface defects. The use of two orthogonal calibrated handlings allows the achievement of precisions of the order of few microns in the whole testing volume and the prevention of self-occlusions induced on the undercut surfaces of the tool. Point cloud registration is also derived analytically to increase to strength of the measurement scheme, whereas proper filters are used to delete samples whose quality is below a reference threshold. Experimental tests are performed on calibrated spheres and different-sized tools, proving the capability of the presented setup to entirely reconstruct complex targets with maximum absolute errors between the estimated distances and the corresponding nominal values below 12 mu m.

The environmental 3-D reconstruction is a strategic task in many contexts, above all, in infrastructure inspection and vehicle/robot automatic motion. In this paper, we present a patented sensor capable to recover 3-D data with a very high profile acquisition rate and perform an omnidirectional highly accurate environmental reconstruction: these skills are allowed by a profilometric laser approach coupled to a catadioptric system. After a presentation of the sensor's functionality, its sensitivity is theoretically analyzed both in terms of maximum and medium error. This, not only for proving the sensor's accuracy (which, anyway, has been also experimentally tested), but also for defining a design strategy which optimally sets up its geometrical parameters. Performance of a prototypal implementation of the sensor, as well as a calibration technique, are presented, and several indoor and outdoor 3-D reconstructions are shown. © 2011 IEEE.

High resolution in distance (range) measurements can be achieved by means of accurate instrumentations and precise analytical models. This paper reports an improvement in the estimation of distance measurements performed by an omnidirectional range sensor already presented in literature. This sensor exploits the principle of laser triangulation, together with the advantages brought by catadioptric systems, which allow the reduction of the sensor size without decreasing the resolution. Starting from a known analytical model in two dimensions (2D), the paper shows the development of a fully 3D formulation where all initial constrains are removed to gain in measurement accuracy. Specifically, the ray projection problem is solved by considering that both the emitter and the receiver have general poses in a global system of coordinates. Calibration is thus made to estimate their poses and compensate for any misalignment with respect to the 2D approximation. Results prove an increase in the measurement accuracy due to the more general formulation of the problem, with a remarkable decrease of the uncertainty.

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.

This paper describes a complete method for monitoring indoor environments. Three-dimensional (3D) point clouds are first acquired from the environment under investigation by means of a laser range scanner in order to obtain several 3D models to be compared. Input datasets are thus registered each other exploiting a reliable variant of the iterative closest point algorithm (ICP) assisted by the use of deletion masks. These terms work in cooperation with the resampling of the model surfaces to reduce significantly the errors in the estimation of the registration parameters. Once datasets are registered, deformation maps are displayed to help the user to detect changes within the environment. Deletion masks are again used to filter measurement artifacts from the comparison, thus highlighting only the actual alterations of the environment. Several experiments are performed for the analysis of an indoor environment, proving the capability of the proposed method to reliably estimate the presence of alterations.

In the last decades the development of very high speed trains in railway transportation requires new maintenance strategies. New trolleys equipped with innovative measuring systems have been employed for monitoring overhead lines (catenaries). Using this system gives two great advantages: i) the diagnose can be performed with a low level of breaking in railway traffic; ii) the monitoring can be executed at the same speed of ordinary locomotives in order to point out the stress suffered by mechanical components of the train and the railroad structure. In this paper we present a vision system for monitoring of the catenary staggering. We propose a new method which is able to measure the position of the overhead line by a stereovision system. All these sensors are installed on a innovative maintenance trolley. Experimental results in real context are presented.

In this paper, a real case study on a Goal Line Monitoringsystem is presented. The core of the paper is a re-fined ball detection algorithm that analyzes candidate ballregions to detect the ball. A decision making approach, bymeans of camera calibration, decides about the goal eventoccurrence. Differently from other similar approaches, theproposed one provides, as unquestionable proof, the imagesequence that records the goal event under consideration.Moreover, it is non-invasive: it does not require any changein the typical football devices (ball, goal posts, and so on).Extensive experiments were performed on both real matchesacquired during the Italian Serie A championship, and specificevaluation tests by means of an artificial impact walland a shooting machine for shot simulation. The encouragingexperimental results confirmed that the system couldhelp humans in ambiguous goal line event detection.

Computer vision is steadily gaining importance in many research fields, as its applications expand from traditional fields situation analysis and scene understanding in video surveillance to other scenarios. The sportive context can represent a perfect test-bed for many machine vision algorithms because of the large availability of visual data brought by wide spread cameras on a relatively high number of courts. In this paper we introduce a tennis ball detection and tracking method that exploits domain knowledge to effectively recognize ball positions and trajectories. A peculiarity of this approach is that it starts from a sparse but cluttered point cloud that evolves over time, basically working on 3D samples only. Experiments on real data demonstrate the effectiveness of the algorithm in terms of tracking accuracy and path following capability.

We live in the era of the fourth industrial revolution, where everything - from small objects to entire factories - is smart and connected, and we are also strongly accustomed to comforts and services, but emergent questions are arising. What are the consequences of human activities on terrestrial and aquatic/marine systems? And how does the loss of biodiversity alter the integrity and functioning of ecosystems? It is reasonable to assert that there are correlations between the anthropic pressure and degradation of natural habitats and loss in biodiversity. In fact, the alteration of ecosystem structure affects ecosystem services and resilience, the level of perturbation that an ecosystem can withstand without shifting to an alternative status providing fewer benefits to humans [1]. To that regards, the research studies on cetacean species distribution and conservation status along with their habitats can give an idea of the current impact of human pressure on marine biodiversity and its ecosystem services, being both dolphins and whales key species in the marine food webs. However, although the inherent complexity of food-web dynamics often makes difficult to investigate and quantify the role of marine mammals in the ecosystem [2], the challenge to investigate their ecological significance is leading and highly informative when facing human induced environmental changes from local to global scales. For this reason, dedicated research activities have been performed in the last years to standardize the best practices for sampling and collecting scientific relevant information on the cetaceans in the Gulf of Taranto (Northern Ionian Sea in the Central-Eastern Mediterranean Sea) [3, 4, 5, 6]. Standardized scientific protocols and technological innovations have been brought by integrating interdisciplinary approaches: a genetic study on dolphin's social structure, an automated photo-identification, assisted by intelligent unsupervised algorithms and the study of acoustic signals. Finally, education and citizen science were applied as fundamental to raise awareness on the need of marine environmental protection among the active population, from children to adults.

Tennis player silhouette extraction is a preliminary step fundamental for any behavior analysis processing. Automatic systems for the evaluation of player tactics, in terms of position in the court, postures during the game and types of strokes, are highly desired for coaches and training purposes. These systems require accurate segmentation of players in order to apply posture analysis and high level semantic analysis. Background subtraction algorithms have been largely used in sportive context when fixed cameras are used. In this paper an innovative background subtraction algorithm is presented, which has been adapted to the tennis context and allows high precision in player segmentation both for the completeness of the extracted silhouettes. The algorithm is able to achieve interactive frame rates with up to 30 frames per second, and is suitable for smart cameras embedding. Real experiments demonstrate that the proposed approach is suitable in tennis contexts.

The present invention relates to a visual inspection system and method for the maintenance of infrastructures, in particular railway infrastructures. It is a system able to operate in real time, wholly automatically, for the automatic detection of the presence/absence of characterizing members of the infrastructure itself, for example the coupling locks fastening the rails to the sleepers.

The present invention refers to the problem of the automatic detection of events in sport field, in particular Goal/NoGoal events by signalling to the mach management, which can autonomously take the final decision upon the event. The system is not invasive for the field structures, neither it requires to interrupt the game or to modify the rules thereof, but it only aims at detecting objectively the event occurrence and at providing support in the referees' decisions by means of specific signalling of the detected events.

The present invention relates to a system for detecting and classifying events during motion actions, in particular "offside" event in the football game. The system allows determining such event in a real-time and semi-automatic context, by taking into account the variability of the environmental conditions and of the dynamics of the events which can be traced back to the offside. The present invention proposes itself with a not-invasive technique, compatible with the usual course of the match.