1,720,961 research outputs found
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Motion-aware temporal median filtering for robust background estimation
Full text linkGiven an input video sequence, whose frames depict the same scene at different times, the background estimation problem consists in generating a model of the scene background, free of the foreground elements occluding it. In this thesis, we are interested in a unimodal variant of this problem, whose resulting background model is a single image. To date, background estimation, that is often confused with background subtraction, has been marginally explored. The simplest method, called the temporal median filter, consists in computing the median pixel value in each pixel position. While it produces excellent results for basic scenes, it relies on the strong assumption that the background is observed more than half of the time in each pixel position. As this assumption is rarely met in complex video sequences, such as the ones containing a large amount of foreground elements, or subject to background motion and/or intermittent motion, the temporal median filter usually fails to generate a clean background image for realistic scenes.
In this thesis, we propose LaBGen, a new background estimation method built upon the temporal median filter while improving its robustness. It is mainly based on the idea that, if we had an information indicating for a given frame which pixels are in motion, we could filter out foreground pixel values considered during median computations, and relax the need to observe the background more than half of the time. After describing and justifying the design of LaBGen, we test the relevance of the motion detection performed by different popular background subtraction algorithms for our task. It turns out that the simple frame difference algorithm enables LaBGen to achieve its best performance. For this reason, we integrate this algorithm in LaBGen-P, another of our methods that improves LaBGen by avoiding some artifacts that it sometimes introduces into the generated background images. In addition to achieving a better performance than many sophisticated state-of-the-art methods, while having a much lower run time, LaBGen and LaBGen-P were ranked first in the international IEEE Scene Background Modeling Contest organized in 2016.
Thereafter, we study the relationship between the performance of motion detection, and the performance of our methods. Although we do not find an obvious correlation between both, we make the assumption upon previous experimental evidence that a temporally memoryless motion detection is the most relevant for LaBGen. Unlike a temporally aware motion detection that does not ignore the temporal information history, a temporally memoryless approach detects motion between two frames without relying on additional past frames. Based on this hypothesis, we design LaBGen-OF, a variant of LaBGen that leverages temporally memoryless optical flow algorithms (i.e. that determine the displacement of each pixel from one frame to another). A consecutive performance study highlights that LaBGen-OF always performs better than LaBGen embedded with different temporally aware motion detection algorithms. Even better, LaBGen-OF is ranked number 2 over 30 on the popular SBMnet background estimation dataset, and takes the lead in 2 categories over 8.
These promising results lead us to push the temporally memoryless even further. For this purpose, we propose two homemade intra-frame motion detection algorithms that leverage semantic segmentation (i.e. a segmentation indicating which object is currently depicted in a given pixel) to determine the possibility of observing motion from spatial information only. Afterwards, we integrate those algorithms into a new variant of LaBGen-P, called LaBGen-P-Semantic, and determine their relevance to our task. In addition to validate the use of intra-frame motion detection algorithms, a performance evaluation shows that LaBGen-P-Semantic performs better than LaBGen and LaBGen-P, and takes the lead in 3 other SBMnet categories.
Finally, an additional contribution of this thesis lies in the performance evaluation subfield. Indeed, as most evaluation methodologies and datasets are used blindly without ever being questioned, we describe and analyze them in depth in order to determine whether such trust is justified. It turns out that some evaluation tools are mathematically inaccurate, and/or redundant, and/or not well correlated with what the visual perception of a human considers as an acceptable background image. In addition, the public implementations of some of those tools return erroneous results. We thus revisit the performance evaluation paradigms used in background estimation, review the problems, and provide possible solutions. Furthermore, as no methodology to assess online background estimation methods (i.e. generating a background image after each frame of the input video sequence) has been proposed to date, we provide insights into how to evaluate such methods. Our proposal is based on paradigms borrowed from the video quality assessment field, and a proof of concept shows that it is able to discriminate the performance of two different online methods that traditional evaluation tools consider to be identical
An Overview of Background Initialization and LaBGen
Full text linkGiven a video sequence captured from a static viewpoint, the stationary background initialization problem consists in generating a unique image estimating the stationary background of the sequence (i.e. the set of elements which are motionless throughout the sequence). Generating an estimation of the background is helpful, and sometimes crucial for many applications including video surveillance, segmentation, compression, inpainting, privacy protection, and computational photography.
The aim of this talk is to first introduce the background initialization field by presenting the main challenges, some important methods, and the evaluation framework. Second, LaBGen, which emerged as the best method during the Scene Background Modeling and Initialization (SBMI 2015) workshop and IEEE Scene Background Modeling Contest (SBMC 2016), will be presented in depth
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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