1,720,961 research outputs found
On analysing deformable (moving) objects
No full textPerforming a high level vision is usually based on features extracted at low and intermediate levels of the process of perception of a visual scene.Segmentation and matching are instrumental tasks in producing comparable features in applications such as medical imaging, mining and oil extraction, gaming consoles, face, ear and gait biometrics, and etc.The ultimate goal of this study is to develop a fully functional prior aided segmentation framework to extract deformable shapes over a sequence of frames. This thesis acknowledges the demand by these applications for a robust and flexible approach which is particularly designed to extract deformable timely shape sequences. It is also recognised that existing methods are either too general, and thus inaccurate, or too specific, thereby limited in usability.This thesis suggests a learning model for gait synthesis with the ability to extrapolate to novel data. It involves computing comparable features from multiple sources. We show that these features which we formulate as continuous functions can be modelled by linear PCA.This thesis also proposes a new fast and robust shape registration algorithm to match shapes from different sources in the proposed framework. This algorithm is based on linear orthogonal transformations and shape moments. The registration parameters are computed directly by analysing the signed distance functions of the shapes. This is in-line with the level sets based prior shape segmentation framework adopted here. The segmentation is performed in a balanced framework between the data in the given images on one hand and the prior induced by the shape model and the registration algorithm proposed here on the other hand. This configuration ensures more control for the shape force over the overall shape geometry. Thus, favouring shapes familiar to the learned knowledge
Breast cancer cad system by using transfer learning and enhanced ROI
Get PDFComputer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%
Implementation of national cryptocurrency using ethereum development platform
Get PDFWhile Ethereum run in public networks which make the blockchain size large and transaction run time longer than time for private or national network, that led to continuous worries over the expanding size of Ethereum Blockchain, which certainly reduce Cryptocurrency\u27s effectiveness. The estimations were on increase and believed it would cross the node limit of 1 TB terribly shortly. If new consumer a full node enters to that blockchain and cryptocurrency world, a node is a computer software cum database of the blockchain, which a full node client must download on their personal computers to become a full node in the blockchain. in this way, the client can be verifying transaction on the network with the help of other nodes on the system. We proposed to implement national cryptocurrency which developed using Ethereum as a development platform that could serve national or regional people that has limited or slow internet connections like Iraq, in addition payments in countries with unstable fiat currencies, although cryptocurrencies are suffering from unstable exchange rates against fiat currencies, the use of national cryptocurrency instead of the native fiat cash could even be a far better alternative for individuals in certain countries like Iraq, Iran and Syria, with high rate of inflation. The reminder of this paper is arranging as following: an introduction, the advantages and drawbacks of cryptocurrencies, Background on Blockchain and Ethereum, implementation, results and conclusion
Transfer learning with GoogLeNet for detection of lung cancer
Get PDFThe use of computer algorithms has gained momentum in filling/assisting roles of specialists especially in early diagnosis scenarios. This paper proposes the employment of deep neural networks (DNN) to detect images with malignant nodules of lung computed tomography (CT). The method includes subjecting input images to a simple and fast pre-processing which isolates regions of interest (ROI), that’s the lungs dominated area, ridding the images of other surrounding tissues and artifacts. Centered and size normalized images are then fed to a deep neural network for training and validation. In this work transfer learning is used to readjust GoogLeNet DNN to learn this medical data. This includes allowing final layers of the DNN to evolve while restricting deep layers. In this setting, a rough, unprocessed dataset, the IQ-OTH/NCCD lung cancer dataset was used to train/validate the proposed algorithm. Experimental results show that this algorithm scores 94.38% accuracy, which outperforms benchmark method previously used with this dataset
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
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
- …
