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Dive into the research topics where Ricardo Fabbri is active.

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Featured researches published by Ricardo Fabbri.


ACM Computing Surveys | 2008

2D Euclidean distance transform algorithms: A comparative survey

Ricardo Fabbri; Luciano da Fontoura Costa; Julio Cesar Torelli; Odemir Martinez Bruno

The distance transform (DT) is a general operator forming the basis of many methods in computer vision and geometry, with great potential for practical applications. However, all the optimal algorithms for the computation of the exact Euclidean DT (EDT) were proposed only since the 1990s. In this work, state-of-the-art sequential 2D EDT algorithms are reviewed and compared, in an effort to reach more solid conclusions regarding their differences in speed and their exactness. Six of the best algorithms were fully implemented and compared in practice.


computer vision and pattern recognition | 2010

3D curve sketch: Flexible curve-based stereo reconstruction and calibration

Ricardo Fabbri; Benjamin B. Kimia

Interest point-based multiview 3D reconstruction and calibration methods have been very successful in select applications but are not applicable when an abundance of feature points are not available. They also lead to an unorganized point cloud reconstruction where the geometry of the scene is not explicit. The multiview stereo methods on the other hand yield dense surface geometry but require a highly controlled or calibrated setting. We propose and develop a novel framework for 3D reconstruction and calibration based on image curve content, whose output is a 3D curve sketch, an unorganized set of 3D curve fragments. This approach, which is meant to augment the previous approaches, results in a reconstruction of geometric curve structure which can serve as a scaffold on which surface patches can be potentially reconstructed. It is intented for the setting where a number of images are available with coarsely calibrated cameras. The approach operates in two stages. A reliable partial 3D curve sketch is first reconstructed and this is used to refine the cameras to yield a more complete 3D curve sketch in a second stage. A key advantage of this approach is the ability to integrate information across a large number of views. The results have been evaluated on a few datasets.


International Journal of Pattern Recognition and Artificial Intelligence | 2010

A HIGH PERFORMANCE 3D EXACT EUCLIDEAN DISTANCE TRANSFORM ALGORITHM FOR DISTRIBUTED COMPUTING

Julio Cesar Torelli; Ricardo Fabbri; Gonzalo Travieso; Odemir Martinez Bruno

The Euclidean distance transform (EDT) is used in various methods in pattern recognition, computer vision, image analysis, physics, applied mathematics and robotics. Until now, several sequential EDT algorithms have been described in the literature, however they are time- and memory-consuming for images with large resolutions. Therefore, parallel implementations of the EDT are required specially for 3D images. This paper presents a parallel implementation based on domain decomposition of a well-known 3D Euclidean distance transform algorithm, and analyzes its performance on a cluster of workstations. The use of a data compression tool to reduce communication time is investigated and discussed. Among the obtained performance results, this work shows that data compression is an essential tool for clusters with low-bandwidth networks.


energy minimization methods in computer vision and pattern recognition | 2005

High-Order differential geometry of curves for multiview reconstruction and matching

Ricardo Fabbri; Benjamin B. Kimia

The relationship between the orientation and curvature of projected curves and the orientation and curvature of the underlying space curve has been previously established. This has allowed a disambiguation of correspondences in two views and a transfer of these properties to a third view for confirmation. We propose that a higher-order intrinsic differential geometry attribute, namely, curvature derivative, is necessary to account for the range of variation of space curves and their projections. We derive relationships between curvature derivative in a projected view, and curvature derivative and torsion of the underlying space curve. Regardless of the point, tangent, and curvature, any pair of curvature derivatives are possible correspondences, but most would lead to very high torsion and curvature derivatives. We propose that the minimization of third order derivatives of the reconstruction, which combines torsion and curvature derivative of the space curve, regularizes the process of finding the correct correspondences.


european conference on computer vision | 2016

From Multiview Image Curves to 3D Drawings

Anil Usumezbas; Ricardo Fabbri; Benjamin B. Kimia

Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting - without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity - most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.


european conference on computer vision | 2012

Camera pose estimation using first-order curve differential geometry

Ricardo Fabbri; Benjamin B. Kimia; Peter Giblin

This paper considers and solves the problem of estimating camera pose given a pair of point-tangent correspondences between the 3D scene and the projected image. The problem arises when considering curve geometry as the basis of forming correspondences, computation of structure and calibration, which in its simplest form is a point augmented with the curve tangent. We show that while the standard resectioning problem is solved with a minimum of three points given the intrinsic parameters, when points are augmented with tangent information only two points are required, leading to substantial computational savings, e.g., when used as a minimal engine within ransac. In addition, computational algorithms are developed to find a practical and efficient solution shown to effectively recover camera pose using both synthetic and realistic datasets. The resolution of this problem is intended as a basic building block of future curve-based structure from motion systems, allowing new views to be incrementally registered to a core set of views for which relative pose has already been computed.


International Journal of Computer Vision | 2016

Multiview Differential Geometry of Curves

Ricardo Fabbri; Benjamin B. Kimia

The field of multiple view geometry has seen tremendous progress in reconstruction and calibration due to methods for extracting reliable point features and key developments in projective geometry. Point features, however, are not available in certain applications and result in unstructured point cloud reconstructions. General image curves provide a complementary feature when keypoints are scarce, and result in 3D curve geometry, but face challenges not addressed by the usual projective geometry of points and algebraic curves. We address these challenges by laying the theoretical foundations of a framework based on the differential geometry of general curves, including stationary curves, occluding contours, and non-rigid curves, aiming at stereo correspondence, camera estimation (including calibration, pose, and multiview epipolar geometry), and 3D reconstruction given measured image curves. By gathering previous results into a cohesive theory, novel results were made possible, yielding three contributions. First, we derive the differential geometry of an image curve (tangent, curvature, curvature derivative) from that of the underlying space curve (tangent, curvature, curvature derivative, torsion). Second, we derive the differential geometry of a space curve from that of two corresponding image curves. Third, the differential motion of an image curve is derived from camera motion and the differential geometry and motion of the space curve. The availability of such a theory enables novel curve-based multiview reconstruction and camera estimation systems to augment existing point-based approaches. This theory has been used to reconstruct a “3D curve sketch”, to determine camera pose from local curve geometry, and tracking; other developments are underway.


Physica A-statistical Mechanics and Its Applications | 2014

Multi-q pattern classification of polarization curves

Ricardo Fabbri; Ivan Napoleão Bastos; Francisco Duarte Moura Neto; Francisco J. P. Lopes; Wesley Nunes Gonçalves; Odemir Martinez Bruno

Several experimental measurements are expressed in the form of one-dimensional profiles, for which there is a scarcity of methodologies able to classify the pertinence of a given result to a specific group. The polarization curves that evaluate the corrosion kinetics of electrodes in corrosive media are applications where the behavior is chiefly analyzed from profiles. Polarization curves are indeed a classic method to determine the global kinetics of metallic electrodes, but the strong nonlinearity from different metals and alloys can overlap and the discrimination becomes a challenging problem. Moreover, even finding a typical curve from replicated tests requires subjective judgment. In this paper, we used the so-called multi-q approach based on the Tsallis statistics in a classification engine to separate the multiple polarization curve profiles of two stainless steels. We collected 48 experimental polarization curves in an aqueous chloride medium of two stainless steel types, with different resistance against localized corrosion. Multi-q pattern analysis was then carried out on a wide potential range, from cathodic up to anodic regions. An excellent classification rate was obtained, at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and both potential ranges, respectively, using only 2% of the original profile data. These results show the potential of the proposed approach towards efficient, robust, systematic and automatic classification of highly nonlinear profile curves.


computer vision and pattern recognition | 2017

The Surfacing of Multiview 3D Drawings via Lofting and Occlusion Reasoning

Anil Usumezbas; Ricardo Fabbri; Benjamin B. Kimia

The three-dimensional reconstruction of scenes from multiple views has made impressive strides in recent years, chiefly by methods correlating isolated feature points, intensities, or curvilinear structure. In the general setting, i.e., without requiring controlled acquisition, limited number of objects, abundant patterns on objects, or object curves to follow particular models, the majority of these methods produce unorganized point clouds, meshes, or voxel representations of the reconstructed scene, with some exceptions producing 3D drawings as networks of curves. Many applications, e.g., robotics, urban planning, industrial design, and hard surface modeling, however, require structured representations which make explicit 3D curves, surfaces, and their spatial relationships. Reconstructing surface representations can now be constrained by the 3D drawing acting like a scaffold to hang on the computed representations, leading to increased robustness and quality of reconstruction. This paper presents one way of completing such 3D drawings with surface reconstructions, by exploring occlusion reasoning through lofting algorithms.


arXiv: Software Engineering | 2014

THE ALGORITHMIC AUTOREGULATION SOFTWARE DEVELOPMENT METHODOLOGY

Renato Fabbri; Ricardo Fabbri; Vilson Vieira; Daniel Penalva; Danilo Shiga; Marcos Mendonça; Alexandre Negrão; Lucas Zambianchi; Gabriela Salvador Thumé

We present a new self-regulating methodology for coordinating distributed team work called Algorithmic Autoregulation (AA), based on recent social networking concepts and individual merit. Team members take on an egalitarian role, and stay voluntarily logged into so-called AA sessions for part of their time (e.g. 2 hours per day), during which they create periodical logs — short text sentences — they wish to share about their activity with the team. These logs are publicly aggregated in a Website and are peer-validated after the end of a session, as in code review. A short screencast is ideally recorded at the end of each session to make AA logs more understandable. This methodology has shown to be well-suited for increasing the efficiency of distributed teams working on what is called Global Software Development (GSD), as observed in our experience in actual real-world situations. This efficiency boost is mainly achieved through 1) built-in asynchronous on-demand communication in conjunction with documentation of work products and processes, and 2) reduced need for central management, meetings or time-consuming reports. Hence, the AA methodology legitimizes and facilitates the activities of a distributed software team. It thus enables other entities to have a solid means to fund these activities, allowing for new and concrete business models to emerge for very distributed software development. AA has been proposed, at its core, as a way of sustaining self-replicating hacker initiatives. These claims are discussed in a real case-study of running a distributed free software hacker team called Lab Macambira.

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Renato Fabbri

University of São Paulo

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Ivan Napoleão Bastos

Rio de Janeiro State University

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Francisco J. P. Lopes

Federal University of Rio de Janeiro

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Vilson Vieira

University of São Paulo

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