1,720,964 research outputs found
3D Scanner, state of the art
No full textThe digital models of real objects are used today in many fields: medicine, archeology and entertainment are some examples of areas in which these models are applied.
Generally, the first step of the creation of a real object’s 3D model consists in capturing the geometrical information of the physical object.
Real objects can be small as coins or big as buildings: the different requirements have brought to the development of a very variegated set of techniques for the acquisition of geometrical information of the object.
The aim of this chapter is to present and explain the techniques the 3D scanners are based on and compare them in terms of accuracy, speed, and applicability, in order to understand advantages and disadvantages of the different approaches
Kernel Regression in HRBF Networks for Surface Reconstruction
No full textThe Hierarchical Radial Basis Function (HRBF) Network is a neural model that proved its suitability in the surface reconstruction
problem. Its non-iterative configuration algorithm requires an estimate of the surface in the centers of the units of the network. In this paper, we analyze the effect of different estimators in training
HRBF networks, in terms of accuracy, required units, and computational
time
3D surface reconstruction : multi-scale hierarchical approaches
No full text3D Surface Reconstruction: Multi-Scale Hierarchical Approaches presents methods to model 3D objects in an incremental way so as to capture more finer details at each step. The configuration of the model parameters, the rationale and solutions are described and discussed in detail so the reader has a strong understanding of the methodology. Modeling starts from data captured by 3D digitizers and makes the process even more clear and engaging. Innovative approaches, based on two popular machine learning paradigms, namely Radial Basis Functions and the Support Vector Machines, are also introduced. These paradigms are innovatively extended to a multi-scale incremental structure, based on a hierarchical scheme. The resulting approaches allow readers to achieve high accuracy with limited computational complexity, and makes the approaches appropriate for online, real-time operation. Applications can be found in any domain in which regression is required. 3D Surface Reconstruction: Multi-Scale Hierarchical Approaches is designed as a secondary text book or reference for advanced-level students and researchers in computer science. This book also targets practitioners working in computer vision or machine learning related field
Refining Hierarchical Radial Basis Function Networks
No full textThe hierarchical radial basis function (HRBF) Network is a neural model that proved its ability in surface reconstruction problem. The algebraic error is used to drive the HRBF configuration procedure and for evaluating the reconstruction ability of the network. While for function approximation the algebraic distance is the appropriate error metric, for computer graphics applications, such as model reconstruction by 3D scanning, the geometric distance is a more suitable error metric. In this paper, we propose a modified HRBF model which makes use of the geometric error as a measure of the reconstruction accuracy
Hierarchical approach for multiscale support vector regression
No full textSupport vector regression (SVR) is based on a linear combination of displaced replicas of the same function, called a kernel. When the function to be approximated is nonstationary, the single kernel approach may be ineffective, as it is not able to follow the variations in the frequency content in the different regions of the input space. The hierarchical support vector regression (HSVR) model presented here aims to provide a good solution also in these cases. HSVR consists of a set of hierarchical layers, each containing a standard SVR with Gaussian kernel at a given scale. Decreasing the scale layer by layer, details are incorporated inside the regression function. HSVR has been widely applied to noisy synthetic and real datasets and it has shown the ability in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by standard SVR. Results also compare favorably with multikernel approaches. Furthermore, tuning the SVR configuration parameters is strongly simplified in the HSVR model
Computational intelligence for surface modeling
No full textThe surface reconstruction problem, which consists in the search of the surface that best describes a given set of points, is of interest in many application fields (e.g., design, archeology, medicine, and entertainment).
This can be viewed as a supervised learning problem, where the vector coordinates (or other features) of each point is an input instance, while a further coordinate is an output label.
The approximation function provides a law to obtain labels from instances.
Several effective computational intelligence paradigms have been developed for solving the surface reconstruction problem, e.g., Multi-layer Perceptron Networks, Radial Basis Function (RBF) Networks, Self-Organizing Maps (SOM), and Support Vector Machines (SVM).
However, other paradigms such as Genetic Algorithms has been used to improve the performances of traditional approaches of surface reconstruction.
In general, the performance of a single paradigm depends on the application context.
Since the real objects has generally a complex structure, that can be described at different levels of detail, a hierarchical multi-scale representation allows for a more accurate tuning of the reconstruction, with a lower complexity of the final model.
In this paper, the basic concepts of surface reconstruction will be introduced and the approaches based on computational intelligence paradigms will be presented.
In particular, the approaches based on some hierarchical techniques (namely, HRBF and HSVR) will be analyzed and discussed in detail
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Multi-scale support vector regression
No full textA multi-kernel Support Vector Machine model, called Hierarchical Support Vector Regression (HSVR), is proposed here. This is a self-organizing (by growing) multiscale version of a Support Vector Regression (SVR) model. It is constituted of hierarchical layers, each containing a standard SVR with Gaussian kernel, at decreasing scales. HSVR have been applied to a noisy synthetic dataset. The results illustrate their power in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by standard SVR. Furthermore with this approach the well known problem of tuning the SVR parameters is strongly simplified
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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