1,721,032 research outputs found
Metodi e applicazioni di sensor fusion per i sistemi meccatronici
Full text linkIn this paper we present a system for 3D shapes digitization. During the research we developed algorithms for 3D shapes reconstruction from multiple stereo images; this algorithms provide the use of colored markers lying over the object surface. The procedure developed provides steps of image processing for markers detection, 3D reconstruction based on epipolar geometry and data fusion from different stereo-pairs. Particular attention was paid to the latter point, developing an algorithm based on uncertainty analysis of each 3D point and compatibility analysis by the Mahalanobis distance. Were then developed algorithms for modeling 3D objects in the scene based on a images sequence of moving bodies. A physical prototype was created at the MeccaLab at University of Trento and it has been used for an experimental verification of the proposed algorithms.In questo lavoro viene presentato un sistema di digitalizzazione di forme 3D. Nel corso della ricerca sono stati sviluppati degli algoritmi per la ricostruzione di forme 3D a partire da immagini acquisite da più stereocamere; questi algoritmi prevedono l'utilizzo di marker colorati posizionati sull'oggetto da digitalizzare. La procedura sviluppata prevede fasi di elaborazione delle immagini per la localizzazione dei marker, ricostruzione 3D basata sulla geometria epipolare e fusione dei dati provenienti dalle diverse stereocamere. Particolare attenzione è stata posta a quest'ultimo punto, sviluppando un algoritmo basato su analisi dell'incertezza di ogni punto e analisi di compatibilità dei punti mediante distanza di Mahalanobis. Vengono poi proesentati degli algoritmi per la modellazione degli oggetti 3D presenti nella scena sulla base di una sequenza di immagini dei corpi in movimento. Un prototipo fisico è stato realizzato presso il MeccaLab dell'Università di Trento ed è stato utilizzato per una fase sperimentale di verifica degli algoritmi proposti
Evaluating the Group Detection Performance: The GRODE Metrics
No full textThe detection of groups of individuals is attracting the attention of many researchers in diverse fields, from automated surveillance to human-computer interaction, with a growing number of approaches published every year. Unexpectedly, the evaluation metrics for this problem are not consolidated, with some measures inherited from different fields, other designed specifically for a particular approach, thus lacking in generalization and making the comparisons between different approaches difficult. Moreover, most of the existent metrics are scarcely expressive, addressing groups as atomic entities, ignoring that they may have different cardinalities, and that group detection approaches may fail in capturing the exact number of individuals that compose it. This paper fills this gap presenting the GROup DEtection (GRODE) metrics, which formally define precision and recall on the groups, including the group cardinality as a variable. This gives the possibility to investigate aspects never considered so far, such as the tendency of a method of over- or under-segmenting, or of better dealing with specific group cardinalities. The GRODE metrics have been evaluated first on controlled scenarios, where the differences with alternative metrics are evident, as well as on public datasets, providing a fresh-new panorama of the state-of-the-art
The GRODE Metrics
No full textThe detection of groups of individuals is attracting the attention of many researchers in diverse fields, from surveillance to social robotics, with a growing number of approaches published every year. Unexpectedly, the evaluation metrics for this problem are not consolidated, with some measures being inherited from the people detection field, others come from clustering or were designed specifically for a particular approach, thus lacking in generalization and making the comparisons hard to be carried out. Moreover, the existent metrics are scarcely expressive, ignoring, for example, the fact that groups have different cardinalities, and that, obviously, larger groups are harder to find. This work fills this gap by presenting the GROup DEtection (GRODE) metrics, which formally define precision and recall on the groups, including the group cardinality as a variable. This gives the possibility to investigate aspects never considered so far, such as the tendency of a method of over- or undersegmenting groups, or of better dealing with specific group cardinalities. The GRODE metrics have been applied to all the publicly available approaches of group detection, on several datasets, discovering interesting strengths and pitfalls so far neglected from the state-of-the-art metrics
The GRODE metrics: Exploring the performance of group detection approaches
No full textThe detection of groups of people is attracting the attention of many researchers in diverse fields, with a growing number of approaches published each year; despite this, the evaluation metrics are not consolidated, with some measures inherited from the people detection fields, other ones designed specifically for a particular approach, generating a set of not comparable figure of merits. Moreover, existent methods of analysis are scarcely expressive, for example ignoring the fact that groups have different cardinalities, and that obviously larger groups are harder to find. This paper fills this gap presenting GRODE, a suite of measures of accuracy which defines precision and recall on the groups, including the group cardinality as variable. This gives the possibility to investigate aspects never considered so far, such as the tendency of a method of over- or under-segmenting groups, or of better dealing with specific group cardinalities. The metrics have been applied to all the publicly available approaches of group detection, discovering interesting strength and pitfalls of the current literature, and promoting further research in the fiel
Markerless human pose estimation for biomedical applications: a survey
No full textMarkerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-based Motion Capture systems due to the ease of setup, portability, and affordable cost of the technology. However, the exploitation of HPE in biomedical applications is still under investigation. This review aims to provide an overview of current biomedical applications of HPE. In this paper, we examine the main features of HPE approaches and discuss whether or not those features are of interest to biomedical applications. We also identify those areas where HPE is already in use and present peculiarities and trends followed by researchers and practitioners. We include here 25 approaches to HPE and more than 40 studies of HPE applied to motor development assessment, neuromuscolar rehabilitation, and gait & posture analysis. We conclude that markerless HPE offers great potential for extending diagnosis and rehabilitation outside hospitals and clinics, toward the paradigm of remote medical care
Count on me: learning to count on a single image
No full textIndividuating and locating repetitive patterns in still images is a fundamental task in image processing, typically achieved by means of correlation strategies. In this paper we provide a solid solution to this task using a differential geometry approach, operating on Lie algebra, and exploiting a mixture of templates. The proposed method asks the user to locate few instances of the target patterns (seeds), that become visual templates used to explore the image. We propose an iterative algorithm to locate patches similar to the seeds working in three steps: first clustering the detected patches to generate templates of different classes, then looking for the affine transformations, living on a Lie algebra, that best link the templates and the detected patches, and finally detecting new patches with a convolutional strategy. The process ends when no new patches are found. We will show how our method is able to process heterogeneous unstructured images with multiple visual motifs and extremely crowded scenarios with high precision and recall, outperforming all the state of the art methods
Grain Segmentation in Atomic Force Microscopy for Thin-Film Deposition Quality Control
No full textIn this paper we propose an image segmentation method specifically designed to detect crystalline grains in microscopic images. We build on the watershed segmentation approach; we propose a preprocessing pipeline to generate a topographic map exploiting the physical nature of the incoming data (i.e. Atomic Force Microscopy) to emphasize grain boundaries and generate seeds for basins. Experimental results show the effectiveness of the proposed method against grain segmentation implementations available in commercial software on a new labelled dataset with an average improvement of over 20% in precision and recall over the standard implementation of watershed segmentation
Gaze-Based Human-Robot Interaction by the Brunswick Model
No full textWe present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered
Efficient Second Order Multi-Target Tracking with Exclusion Constraints
Full text linkCurrent state of the art multi-target tracking (MTT) exists in an "either/or" situation. Either a greedy approach can be used, that can make use of second-order information which captures object dynamics, such as "objects tend to move in the same direction over adjacent frames", or one can use global approaches that make use of the information contained in the entire sequence to resolve ambiguous sub-sequences, but are unable to use such second order information. However, the accurate resolution of ambiguous sequences requires both a good model of object dynamics, and global inference. In this work we present a novel approach to MTT that combines the best of both worlds. By formulating the problem of tracking as one of global MAP estimation over a directed acyclic hyper-graph, we are able to both capture long range interactions, and informative second order priors. In practice, our algorithm is extremely effective, with a run time linear in the number of objects to be tracked, possible locations of an object, and the number of frames. We demonstrate the effectiveness of our approach, both on standard MTT data-sets that contain few objects to be tracked, and on point tracking for non-rigid structure from motion, which, with hundreds of points to be tracked simultaneously, strongly benefits from the efficiency of our approach
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