1,720,959 research outputs found
Image data understanding and preparation
No full textData understanding and preparation involves a process of analyzing, cleaning, transforming, and organizing the data in preparation for data mining. To improve the performance of applications that use image processing, verifying the quality of the images and image cleaning are crucial steps. However, diverse and complex environments have a great effect on images, directly affecting the decisions derived from image analysis and thereby limiting the acceleration of industrial automation. Environmental and camera impacts play a vital role in the quality of the photos captured in outdoor environments. Because of these impacts, the use of images captured in outdoor environments limits the effectiveness of an application in which image processing is involved. There are many techniques available in the current literature for analyzing the impact of the environment on Internet of Things (IoT) images. However, objectively assessing the effect of dynamic and complex environments on IoT images is challenging. To advance this research area, we present an innovative technique for evaluating the impact of environmental parameters on image quality compared with the quality affected by the Joint Photographic Experts Group (JPEG) image compression technique and the different levels of Gaussian noise. The quality values produced by the structural similarity index measure (SSIM) are consistent with the different levels of environmental impacts, JPEG image quality, and Gaussian noise, and can be used for image understanding and preparation. For camera impacts, there exist many approaches that assess the influence on the quality of the images. Analysis shows that none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, lens dirtiness, or barrel distortion. To address the loopholes in the existing metrics and to ensure that the quality assessment metrics are more reliable, we introduce a new image quality assessment metric that uses the Dempster–Shafer theory to fuse quality values from different metrics. Our proposed metric produces quality values that are more consistent and better aligned with perceptually defined camera parameter impact levels. Various noise reduction techniques are proposed in the literature for image data preparation, including the use of median, Gaussian, and bilateral filters. Convolutional neural networks (CNNs) have gained popularity in image denoising owing to their ability to extract complex patterns and features from data. CNNs are highly adaptable, making them effective tools for various image-denoising tasks. The drawback of CNN-based techniques is that they require an appropriate training dataset and all images to be resized. Another notable disadvantage of these filtering techniques is that they work for certain types of environmental and camera impacts. To bridge this research gap, we analyze the impact of denoising on CNN performance. First, we filter noise from images using traditional denoising methods before using them in the CNN model. Second, we embed a denoising layer within the CNN. We conduct extensive experiments on traffic sign and object recognition datasets to validate the performance of image denoising. We also present an approach using peak signal-to-noise ratio (PSNR) distribution to determine whether denoising should be adopted and which filter to use. Both CNN accuracy and PSNR distribution are used to determine the type of filter that needs to be used. The results vary by filter type, impact, and dataset, with traditional denoising showing better accuracy and embedded denoising offering shorter computational time in most cases. This comparative study provides insights into adopting denoising in various CNN-based image analyses. From this analysis, it is shown that traditional and embedding denoising techniques are efficient in reducing many impacts; however, these are not competent enough to reduce impact types such as salt and pepper, lens blur, and shadow. To address this problem, finally, for the first time, we introduce an approach to directly filter out poor-quality images for different environmental and camera impacts. Our approach assesses quality using an image quality metric and employs an optimal threshold to remove low-quality images while ensuring that an adequate number of images remain for deep learning model development. Results from real and simulated traffic and object recognition data showcase the superior performance of our approach compared with state-of-the-art approaches. The merit of our technique is that it works well for all environmental and camera impacts with comparable computational time.Doctor of Philosoph
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Image quality assessment metric fusing traditional and dempster-shafer theory
No full textImage analysis is being applied in many applications including industrial automation with the Industrial Internet of Things and machine vision. The images captured by cameras, from the outdoor environment are impacted by various parameters such as lens blur, dirty lens and lens distortion (barrel distortion). There exist many approaches that assess the impact of camera parameters on the quality of the images. However, most of these techniques do not use important quality assessment metrics such as Oriented FAST and Rotated BRIEF and Structural Content. None of these techniques objectively evaluate the impact of barrel distortion on the image quality using quality assessment metrics such as Mean Square Error, Peak signal-to-noise ratio, Structural Content, Oriented FAST and Rotated BRIEF and Structural Similarity Index. In this paper, besides lens dirtiness and blurring, we also examine the impact of barrel distortion using various types of dataset having different levels of barrel distortion. Analysis shows none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, dirtiness and barrel distortion. To address the loopholes of existing metrics and make the quality assessment metric more reliable, we present two new image quality assessment metrics. For our combined metric, results show that the maximum values of impact level created by barrel distortion, blurriness and dirtiness are 66.6%, 87.9% and 94.4%, respectively. These results demonstrate the effectiveness of our metric to assess the impact level more accurately. The second approach fuses the quality values obtained from different metrics using a decision fusion technique known as the Dempster-Shafer (DS) theory. Our metric produces quality values that are more consistent and conform with the perceptually defined camera parameter impact levels. For all above-mentioned camera impacts, our metric DS exhibits 100 % assessment reliability, which includes an enormous improvement over other metrics. © 2023, Springer Nature Switzerland AG
Variations on the Author
Get PDF“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
Get PDFWe 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
Get PDFWe 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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