1,720,955 research outputs found
PACMAN: Rapid identification of keypoint patch-based fiducial marker in occluded environments
No full textFiducial marker systems are widely used in image-based localization methods due to their high robustness and low computational latency. However, occlusions caused by dynamic environmental factors, such as shadows and unexpected objects, significantly hinder the detection of fiducial markers, as partially visible patterns and significant image degradation often contradict the fundamental assumptions of marker detection systems. To address this challenge, we propose a keypoint-based fiducial marker and a deep-learning-based detector that jointly handle occlusion and image degradation with minimal computational latency. First, we design four distinct keypoint patches that account for occlusions and maintain essential functionalities. A fiducial marker is then constructed by assembling six identical patches under geometric constraints. Second, a widely-used interest point detector network is optimized for the proposed marker design, resulting in robust keypoint detection under various types of image deformation and degradation. A geometric consistency check is subsequently applied to map imperfect keypoint detections to 6D marker poses in the image, effectively rejecting occlusions and potential network failures. Third, neural network quantization and parallel CPU processing are applied to minimize computational latency. Our experimental results demonstrate higher detection rates then other types of single marker in occluded environments, with improvements ranging from 17% to 40%. The proposed system is also evaluated under motion blur, dimming effects, and variations in scale and rotation. Additionally, the efficient computational design enables end-to-end processing at up to 749 FPS on a desktop PC and 138 FPS on an edge device, for 640 x 480 resolution images containing a single marker. Our code is available at:
End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing
Full text linkReinforcement Learning (RL) has emerged as a transformative approach in the
domains of automation and robotics, offering powerful solutions to complex
problems that conventional methods struggle to address. In scenarios where the
problem definitions are elusive and challenging to quantify, learning-based
solutions such as RL become particularly valuable. One instance of such
complexity can be found in the realm of car racing, a dynamic and unpredictable
environment that demands sophisticated decision-making algorithms. This study
focuses on developing and training an RL agent to navigate a racing environment
solely using feedforward raw lidar and velocity data in a simulated context.
The agent's performance, trained in the simulation environment, is then
experimentally evaluated in a real-world racing scenario. This exploration
underlines the feasibility and potential benefits of RL algorithm enhancing
autonomous racing performance, especially in the environments where prior map
information is not available.Comment: 6 page
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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