1,720,961 research outputs found
The Mirror Transform
Full text linkThis paper explains how to describe any finite-energy signal through a unique representation consisting of an ordered set of positions and a sparse set of signals. This is obtained by designing an iterative decomposition through a series of mirror operations around those positions. The purpose is to find at any step of the decomposition the location that provides for the maximum decoupling between the even and odd components of the signal with respect to it. By limiting such even and odd components on three separate information bearing support, the algorithm can be iterated at infinity determining a sequence of positions. The per location information determines the optimal energy decoupling strategy at each stage providing remarkable sparsity in the representation. The resulting transformation leads to a 1-to-1 mapping. The approach is easily extended to finite-energy sequences, and in particular for sequences of finite length N, at most N iterations of the decomposition are required. Thanks to the sparsity of the resulting representation, experimental simulations demonstrate superior approximation capabilities of this proposed non-linear Mirror Transform with potential application in many domains such as approximation and coding. Its implementation has been made publicly available
Combining Appearance and Gradient Information for Image Symmetry Detection
No full textThis work addresses the challenging problem of reflection symmetry detection in unconstrained environments. Starting from the understanding on how the visual cortex manages planar symmetry detection, it is proposed to treat the problem in two stages: i) the design of a stable metric that extracts subsets of consistently oriented candidate segments, whenever the underlying 2D signal appearance exhibits definite near symmetric correspondences; ii) the ranking of such segments on the basis of the surrounding gradient orientation specularity, in order to reflect real symmetric object boundaries. Since these operations are related to the way the human brain performs planar symmetry detection, a better correspondence can be established between the outcomes of the proposed algorithm and a human-constructed ground truth. When compared to the testing sets used in recent symmetry detection competitions, a remarkable performance gain can be observed. In additional, further validation has been achieved by conducting perceptual validation experiments with users on a newly built dataset
Machine learning techniques for MRI feature-based detection of frontotemporal lobar degeneration
Full text linkMaking a diagnosis of neurodegenerative diseases at an early stage is one of the most significant challenges of modern neuroscience. Although this family of diseases remains without a cure, the effectiveness of their medical treatment largely relies on the timing of their detection. For certain groups of diseases, such as Fronto-Temporal Dementia (FTD), trained professionals can effectively reach a correct diagnosis through the visual analysis of Magnetic Resonance Imaging, in its functional (fMRI) or raw (MRI) version. However, this operation is time-consuming and may be subject to personal interpretation. In this paper, we explore the performance of a group of machine learning algorithms to formulate a correct FTD diagnosis, in order to provide medical professionals with a supporting tool. The dataset consists of MRI data acquired on 30 subjects, and the experiments are carried out by investigating different fMRI techniques based on a Multi-Voxel Pattern Analysis (MVPA) approach. The results obtained show high accuracy in identifying FTD in elderly patients when Support Vector Machine and Random Forest techniques are used, with outcomes varying based on the fMRI methods
Innerspec: Technical Report
Full text linkIn this report we describe “InnerSpec”, an approach for symmetric object detection that is based both on the com- putation of a symmetry measure for each pixel and on gra- dient information analysis. The symmetry value is obtained as the energy balance of the even-odd decomposition of an oriented square patch with respect to its central axis. Such an operation is akin to the computation of a row-wise con- volution in the midpoint. The candidate symmetry axes are then identified through the localization of peaks along the direction perpendicular to each considered angle. These axes are finally evaluated by computing the image gradient in their neighborhood, in particular checking whether the gradient information displays specular characteristics
Iterative Mirror Decomposition for Signal Representation
Full text linkIn this paper it is shown how to describe any finite-energy continuous or discrete signal through an ordered set of positions to uniquely represent it. This is obtained by designing an iterative decomposition through a series of mirror operations around those positions. The purpose is to find at any step of the decomposition the location that provides for the maximum decoupling between the even and odd components of the signal with respect to it. The algorithm can then be iterated at infinity determining a sequence of positions. The per location information determines the optimal energy decoupling strategy at each stage providing remarkable sparsity in the representation. Thanks to the sparsity of the resulting representation, experimental simulations demonstrate superior approximation capabilities of this proposed non-linear mirror transform
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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