1,720,982 research outputs found
Local signature quantization by sparse coding
No full textIn 3D object retrieval it is very important to define reliable shape descriptors, which compactly characterizegeometric properties of the underlying surface. To this aim two main approaches are considered: global, andlocal ones. Global approaches are effective in describing the whole object, while local ones are more suitableto characterize small parts of the shape. Some strategies to combine these two approaches have been proposedrecently but still no consolidate work is available in this field. With this paper we address this problem and proposea new method based on sparse coding techniques. A set of local shape descriptors are collected from the shape.Then a dictionary is trained as generative model. In this fashion the dictionary is used as global shape descriptorfor shape retrieval purposes. Preliminary experiments are performed on a standard dataset by showing a drasticimprovement of the proposed method in comparison with well known local-to-global and global approaches
A sparse coding approach for local-to-global 3D shape description
No full textThe definition of reliable shape descriptors is an essential topic for 3D object retrieval. In general, two main approaches are considered: global, and local. Global approaches are effective in describing the whole object, while local ones are more suitable to characterize small parts of the shape. Recently some strategies to combine these two approaches have been proposed which are mainly concentrated to the so-called bag of words paradigm. With this paper we address this problem and propose an alternative strategy that goes beyond the bag of word approach. In particular, a sparse coding technique is exploited for the 3Ddomain: a set of local shape descriptors are collected fromthe shape, and then a dictionary is trained as generativemodel. In this fashion the dictionary is used as global shapedescriptor for shape retrieval purposes. Several experimentsare performed on standard databases in order to evaluate theproposed method in challenging situations like the case of‘SHREC 2011: robustness benchmark’ where strong shapetransformations are included, and the case of ‘SHREC 2007:partial matching track’ where composite models are consideredin the query phase. A drastic improvement of theproposed method is observed by showing that sparse codingapproach is particularly suitable for local-to-global descriptionand outperforms other approaches such as the bag ofwords
Deep learning for shape analysis
No full textThe past decade in computer vision research has witnessed the re-emergence of deep learning, and in particular convolutional neural network (CNN) techniques, allowing to learn powerful image feature representations from large collections of examples. Nevertheless, when attempting to apply standard deep learning methods to geometric data which by its nature is non-Euclidean (e.g. 3D shapes, graphs), one has to face fundamental differences between images and geometric objects. The purpose of this tutorial is to overview the foundations and the state of the art on learning techniques for 3D shape analysis. Special focus will be put on deep learning (CNN) applied to Euclidean and non-Euclidean manifolds for tasks of shape classification, retrieval and correspondence. The tutorial will present in a new light the problems of shape analysis, emphasizing the analogies and differences with the classical 2D setting and showing how to adapt popular learning schemes to deal with deformable shapes
SHIELD: Safeguard Heritage In Endangered Looted Districts
No full textUnmanned aerial surveillance is key for the protection of archaeological sites against looting activities. Automated surveillance is a valuable decision-making means that can become of great support to public authorities. The European project Safeguard Heritage In Endangered Looted Districts (SHIELD) aims to developing a fully autonomous surveillance system that involves a drone capable of taking off and surveying a scene of interest (i.e., detecting/tracking objects, monitoring suspicious actions), as well as landing and charging at its smart helipad. In this extended abstract we focus on SHIELD perception capabilities, in particular on the the object detection module that is based on the state-of-the-art CenterNet algorithm. Preliminary results on thermal infrared data, from the BIRDSAI dataset, show promising results that could serve as a build-up of future improvement
Self-Supervision for 3D Real-World Challenges
Full text linkWe consider several possible scenarios involving synthetic and real-world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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