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Ca' Foscari University of Venice, Italy
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10 Papers

A Light Source Calibration Technique for Multi-camera Inspection Devices

Topics: Industry Related Applications; Image and Video Analysis and Understanding

Authors: Mara Pistellato , Mauro Noris , Andrea Albarelli and Filippo Bergamasco

Affiliation: DAIS, Università Ca’Foscari Venezia, 155, via Torino, Venezia, Italy

Digital Object Identifier: 10.5220/0010995900003122

Proceedings of ICPRAM 2022, ISBN: 978-989-758-549-4

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Industrial manufacturing processes often involve a visual control system to detect possible product defects during production. Such inspection devices usually include one or more cameras and several light sources designed to highlight surface imperfections under different illumination conditions (e.g. bumps, scratches, holes). In such scenarios, a preliminary calibration procedure of each component is a mandatory step to recover the system’s geometrical configuration and thus ensure a good process accuracy. In this paper we propose a procedure to estimate the position of each light source with respect to a camera network using an inexpensive Lambertian spherical target. For each light source, the target is acquired at different positions from different cameras, and an initial guess of the corresponding light vector is recovered from the analysis of the collected intensity isocurves. Then, an energy minimization process based on the Lambertian shading model refines the result for a precise 3D localization. We tested our approach in an industrial setup, performing extensive experiments on synthetic and real-world data to demonstrate the accuracy of the proposed approach.

Calibration of a Telecentric Structured-light Device for Micrometric 3D Reconstruction

Topics: Signal Processing; Industry Related Applications

Authors: Mara Pistellato , Andrea Albarelli and Filippo Bergamasco

Affiliation: DAIS, Università Ca’Foscari Venezia, 155, via Torino, Venezia Italy

Digital Object Identifier: 10.5220/0010841700003122

Proceedings of ICPRAM 2022, ISBN: 978-989-758-549-4

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Structured-light 3D reconstruction techniques are employed in a wide range of applications for industrial inspection. In particular, some tasks require micrometric precision for the identification of microscopic surface irregularities. We propose a novel calibration technique for structured-light systems adopting telecentric lenses for both camera and projector. The device exploits a fixed light pattern (striped-based) to perform accurate microscopic surface reconstruction and measurements. Our method employs a sphere with a known radius as calibration target and takes advantage of the orthographic projection model of the telecentric lenses to recover the bundle of planes originated by the projector. Once the sheaf of parallel planes is properly described in the camera reference frame, the triangulation of the surface’s object hit by the light stripes is immediate. Moreover, we tested our technique in a real-world scenario for industrial surface inspection by implementing a complete pipeline to recover the intersections between the projected planes and the surface. Experimental analysis shows the robustness of the proposed approach against synthetic and real-world test data.

Robust Cylinder Estimation in Point Clouds from Pairwise Axes Similarities

Topics: Industry Related Applications; Model Representation and Selection

Authors: Mara Pistellato , Filippo Bergamasco , Andrea Albarelli and Andrea Torsello

Affiliation: DAIS, Ca’Foscari University of Venice, Via Torino 155, Venice and Italy

Digital Object Identifier: 10.5220/0007401706400647

Proceedings of ICPRAM 2019, ISBN: 978-989-758-351-3

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The ubiquitous presence of cylindrical shapes in both natural and man-made environments makes their automated extraction a pivotal task for a broad range of applications such as robot manipulation, reverse engineering and automated industrial inspection. Albeit conceptually simple, the task of fitting cylinders from 3D data can quickly become challenging if performed "in-the-wild", when no prior is given to the number of primitives to find or when the point cloud is noisy and not oriented. In this paper we introduce a new robust approach to iteratively extract cylindrical primitives from a 3D point cloud by exploiting mutual consensus of different cylinder candidates. First, a set of possible axes is generated by slicing the point cloud with multiple random planes. Then, a game-theoretic inlier selection process is performed to extract a subset of axes maximizing the fitness against a payoff function based on the shortest geodesic path in SE(3) between pairs of corresponding 3D lines. Finally, the probability distribution resulting from the previous selection step is used to weight the input candidates and robustly obtain the final cylinder coefficients. Compared to other methods, our approach does not require point normals, offers superior resilience to noise and does not depend on delicate tuning of multiple parameters.

Stochastic Phase Estimation and Unwrapping

Topics: Signal Processing; Industry Related Applications; Image and Video Analysis and Understanding

Authors: Mara Pistellato , Filippo Bergamasco , Andrea Albarelli , Luca Cosmo , Andrea Gasparetto and Andrea Torsello

Affiliation: DAIS, Ca’Foscari University of Venice, Via Torino 155, Venice and Italy

Digital Object Identifier: 10.5220/0007389402000209

Proceedings of ICPRAM 2019, ISBN: 978-989-758-351-3

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Phase-shift is one of the most effective techniques in 3D structured-light scanning for its accuracy and noise resilience. However, the periodic nature of the signal causes a spatial ambiguity when the fringe periods are shorter than the projector resolution. To solve this, many techniques exploit multiple combined signals to unwrap the phases and thus recovering a unique consistent code. In this paper, we study the phase estimation and unwrapping problem in a stochastic context. Assuming the acquired fringe signal to be affected by additive white Gaussian noise, we start by modelling each estimated phase as a zero-mean Wrapped Normal distribution with variance σ̄2. Then, our contributions are twofolds. First, we show how to recover the best projector code given multiple phase observations by means of a ML estimation over the combined fringe distributions. Second, we exploit the Cramér-Rao bounds to relate the phase variance σ̄2 to the variance of the observed signal, that can be easily estimated online during the fringe acquisition. An extensive set of experiments demonstrate that our approach outperforms other methods in terms of code recovery accuracy and ratio of faulty unwrappings.
Collapse Button Related Ontology Subjects/Areas/Topics (7)
Applications

Health Engineering and Technology Applications

Signal Processing

Cardiovascular Imaging and Cardiography

Pattern Recognition

Software Engineering
Cardiovascular Technologies

Document Clustering Games

Topics: Information Retrieval; Natural Language Processing; Graphical and Graph-Based Models; Clustering

Authors: Rocco Tripodi and Marcello Pelillo

Affiliation: Ca' Focsari University, Italy

Digital Object Identifier: 10.5220/0005798601090118

Proceedings of ICPRAM 2016, ISBN: 978-989-758-173-1

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In this article we propose a new model for document clustering, based on game theoretic principles. Each document to be clustered is represented as a player, in the game theoretic sense, and each cluster as a strategy that the players have to choose in order to maximize their payoff. The geometry of the data is modeled as a graph, which encodes the pairwise similarity among each document and the games are played among similar players. In each game the players update their strategies, according to what strategy has been effective in previous games. The Dominant Set clustering algorithm is used to find the prototypical elements of each cluster. This information is used in order to divide the players in two disjoint sets, one collecting labeled players, which always play a definite strategy and the other one collecting unlabeled players, which update their strategy at each iteration of the games. The evaluation of the system was conducted on 13 document datasets and shows that the proposed method performs well compared to different document clustering algorithms.
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Applications

Information Retrieval

Pattern Recognition

Artificial Intelligence

Knowledge Engineering and Ontology Development

Software Engineering

Clustering

Knowledge-Based Systems

Symbolic Systems

Data Engineering

Natural Language Processing

Theory and Methods
Graphical and Graph-Based Models

Ontologies and the Semantic Web

A Non-parametric Spectral Model for Graph Classification

Topics: Graphical and Graph-Based Models; Spectral Methods

Authors: Andrea Gasparetto , Giorgia Minello and Andrea Torsello

Affiliation: Università Ca' Foscari Venezia, Italy

Digital Object Identifier: 10.5220/0005220303120319

Proceedings of ICPRAM 2015, ISBN: 978-989-758-076-5

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Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective spectral approach to graph learning. In particular, we define a novel model of structural representation based on the spectral decomposition of graph Laplacian of a set of graphs, but which make away with the need of one-to-one node-correspondences at the base of several previous approaches, and handles directly a set of other invariants of the representation which are often neglected. An experimental evaluation shows that the approach significantly improves over the state of the art.
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Graphical and Graph-Based Models

Spectral Methods

Theory and Methods
Pattern Recognition

Objective and Subjective Metrics for 3D Display Perception Evaluation

Topics: Perception; Virtual Environments

Authors: Andrea Albarelli , Luca Cosmo , Filippo Bergamasco and Andrea Gasparetto

Affiliation: Università Ca' Foscari, Italy

Digital Object Identifier: 10.5220/0005280603090317

Proceedings of ICPRAM 2015, ISBN: 978-989-758-077-2

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Many modern professional 3D display systems adopt stereo vision and viewer-dependent rendering in order to offer an immersive experience and to enable complex interaction models. Within these scenarios, the ability of the user to effectively perform a task depends both on the correct rendering of the scene and on his ability to perceive it. These factors, in turn, are affected by several error sources, such as accuracy of the user position estimation or lags between tracking and rendering. With this paper, we introduce a practical and sound method to quantitatively assess the accuracy of any view-dependent display approach and the effects of the different error sources. This is obtained by defining a number of metrics that can be used to analyze the results of a set of experiments specially crafted to tickle different aspects of the system. This fills a clear shortcoming of the evaluation methods for 3D displays found in literature, that are, for the most part, qualitative.
Collapse Button Related Ontology Subjects/Areas/Topics (5)
Applications

Perception

Virtual Environments
Pattern Recognition

Software Engineering

LINGUISTICALLY BASED QA BY DYNAMIC LOD ACCESS FROM LOGICAL FORMS

Topics: Semantic Web; Natural Language Processing; Ontology Sharing and Reuse; Ontology Matching and Alignment

Authors: Rocco Tripodi and Rodolfo Delmonte

Affiliation: Ca’ Foscari University, Italy

Digital Object Identifier: 10.5220/0003629100050014

Proceedings of KEOD 2011/IC3K, ISBN: 978-989-8425-80-5

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We present a system for Question Answering which computes a prospective answer from Logical Forms produced by a full-fledged NLP for text understanding, and then maps the result onto schemata in SPARQL to be used for accessing the Semantic Web. It is just by the internal structure of the Logical Form that we are able to produce a suitable and meaningful context for concept disambiguation. Logical Forms are the final output of a complex system for text understanding – VENSES - which can deal with different levels of syntactic and semantic ambiguity in the generation of a final structure, by accessing computational lexical equipped with sub-categorization frames and appropriate selectional restrictions applied to the attachment of complements and adjuncts. The system also produces pronominal binding and instantiates the implicit arguments, if needed, in order to complete the required Predicate Argument structure which is licensed by the semantic component.
Collapse Button Related Ontology Subjects/Areas/Topics (14)
Applications

Knowledge Engineering and Ontology Development

Pattern Recognition

Artificial Intelligence

Knowledge-Based Systems

Semantic Web

Collaboration and e-Services

Natural Language Processing

Soft Computing

e-Business

Ontology Matching and Alignment

Symbolic Systems
Enterprise Information Systems

Ontology Sharing and Reuse

Computing Implicit Entities and Events with Getaruns

Author: Rodolfo Delmonte

Affiliation: Università Ca’ Foscari, Italy

Digital Object Identifier: 10.5220/0002171600230035

Proceedings of NLPCS 2009/ICEIS, ISBN: 978-989-8111-92-0

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In this paper we will focus on the notion of “implicit” or lexically unexpressed linguistic elements that are nonetheless necessary for a complete semantic interpretation of a text. We referred to “entities” and “events” because the recovery of the implicit material may affect all the modules of a system for semantic processing, from the grammatically guided components to the inferential and reasoning ones. Reference to the system GETARUNS offers one possible implementation of the algorithms and procedures needed to cope with the problem and allows to deal with all the spectrum of phenomena. The paper will address at first the following three types of “implicit” entities and events: - the grammatical ones, as suggested by a linguistic theories like LFG or similar generative theories; - the semantic ones suggested in the FrameNet project, i.e. CNI, DNI, INI; - the pragmatic ones: here we will present a theory and an implementation for the recovery of implicit entities and events of (non-) standard implicatures. In particular we will show how the use of commonsense knowledge may fruitfully contribute in finding relevant implied meanings. Last Implicit Entity only touched on, though for lack of space, by the paper is the Subject of Point of View which is computed by Semantic Informational Structure and contributes the intended entity from whose point of view is expressed a given subjective statement.

Building Domain Ontologies from Text Analysis: An Application for Question Answering

Topics: Intelligent Tutoring Systems; Intelligent User Interfaces; Verification and Validation of Knowledge-Based Systems; Natural Language Interfaces to Intelligent Systems

Author: Rodolfo Delmonte

Affiliation: Università Ca’ Foscari, Italy

Digital Object Identifier: 10.5220/0002483200030016

Proceedings of NLUCS 2006/ICEIS, ISBN: 978-972-8865-50-4

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In the field of information extraction and automatic question answering access to a domain ontology may be of great help. But the main problem is building such an ontology, a difficult and time consuming task. We propose an approach in which the domain ontology is learned from the linguistic analysis of a number of texts which represent the domain itself. NLP analysis is done with GETARUNS system. GETARUNS can build a Discourse Model and is able to assign a relevance score to each entity. From Discourse Model we extract best candidates to become concepts in the domain ontology. To arrange concepts in the correct hierarchy we use WordNet taxonomy. Once the domain ontology is built we reconsider the texts to extract information. In this phase the entities recognized at discourse level are used to create instances of the concepts. The predicate-argument structure of the verb is used to construct instance slots for concepts. Eventually, the question answering task is performed by translating the natural language question in a suitable form and use that to query the Discourse Model enriched by the ontology.
Collapse Button Related Ontology Subjects/Areas/Topics (11)
Artificial Intelligence

Enterprise Information Systems

Intelligent User Interfaces

Artificial Intelligence and Decision Support Systems

Human-Computer Interaction

Natural Language Interfaces to Intelligent Systems

Computer-Supported Education

Information Technologies Supporting Learning

Verification and Validation of Knowledge-Based Systems
e-Learning

Intelligent Tutoring Systems

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