15,180 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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A niching memetic algorithm for simultaneous clustering and feature selection
Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data
Quantitative Perspectives on Fifty Years of the Journal of the History of Biology
Journal of the History of Biology provides a fifty-year long record for
examining the evolution of the history of biology as a scholarly discipline. In
this paper, we present a new dataset and preliminary quantitative analysis of
the thematic content of JHB from the perspectives of geography, organisms, and
thematic fields. The geographic diversity of authors whose work appears in JHB
has increased steadily since 1968, but the geographic coverage of the content
of JHB articles remains strongly lopsided toward the United States, United
Kingdom, and western Europe and has diversified much less dramatically over
time. The taxonomic diversity of organisms discussed in JHB increased steadily
between 1968 and the late 1990s but declined in later years, mirroring broader
patterns of diversification previously reported in the biomedical research
literature. Finally, we used a combination of topic modeling and nonlinear
dimensionality reduction techniques to develop a model of multi-article fields
within JHB. We found evidence for directional changes in the representation of
fields on multiple scales. The diversity of JHB with regard to the
representation of thematic fields has increased overall, with most of that
diversification occurring in recent years. Drawing on the dataset generated in
the course of this analysis, as well as web services in the emerging digital
history and philosophy of science ecosystem, we have developed an interactive
web platform for exploring the content of JHB, and we provide a brief overview
of the platform in this article. As a whole, the data and analyses presented
here provide a starting-place for further critical reflection on the evolution
of the history of biology over the past half-century.Comment: 45 pages, 14 figures, 4 table
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
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