1,720,984 research outputs found
Detection of a small target object in blurry images affected by affine distortions
Get PDFThe paper proposes a novel and practically effective method for detecting, classifying, and estimating the coordinates of the image center of a small-size target object on a noisy scene, which is invariant to linear conformal transformations (rotation, shift, and scale). We consider a binary classifier that decides whether a particular part of the scene contains
the desired image or only the background. The proposed approach implies an interactive procedure for finding an extremum of a function that approximates the likelihood function of the binary classifier. A two-step procedure based on the Nelder-Meade method is used to implement the extremum search. In order to ensure the robustness to noise
and linear conformal transformations, both special training methods and the approach based on using an ensemble of classifiers, each of which corresponds to a certain scale, are applied in training the classifier. The author created a method for detecting a blurred image of a small-sized object in a scene that is distorted by correlated noise and proposes
simultaneous estimation of the coordinates of the center of the target image. The method is robust to linear conformal distortions and has been successfully tested both on the artificial model and real images. The results of numerical study confirmed the robustness of the method to correlated noise of additive type and to linear conformal transformations.
Within the framework of the proposed approach, the problem of constructing a confidence set for the coordinates of the target image center has been formally solved, and the efficiency of the obtained solution has been numerically investigated. The properties of the confidence set are formalized in the form of a theorem. The work also makes a comparison with the classical correlation-extreme method. If necessary, the proposed method can be easily generalized to the multiclass case. The method can be applied to machine vision systems, including online analysis of aerial survey data and to systems for video monitoring of the mechanical condition of complex technical equipment under conditions of strong meteorological disturbance
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
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
METHOD FOR HYPERPARAMETER TUNING IN MACHINE LEARNING TASKS FOR STOCHASTIC OBJECTS CLASSIFICATION
Get PDFSubject of Research. The paper presents a simple and practically effective solution for hyperparameter tuning in
classification problem by machine learning methods. The proposed method is applicable for any hyperparameters of the real type with the values which lie within the known real parametric compact. Method. A random sample (trial
network) of small power is generated within the parametric compact, and the efficiency of hyperparameter tuning is calculated for each element according to a special criterion. The efficiency is estimated by the value of a real scalar, which does not depend on the classification threshold. Thus, a regression sample is formed, the regressors of which are
the random sets of hyperparameters from the parametric compact, and regression values are classification efficiency indicator values corresponding to these sets. The nonparametric approximation of this regression is constructed on the basis of the formed data set. At the next stage the minimum value of the constructed approximation is determined for the regression function on the parametric compact by the Nelder-Mead optimization method. The arguments of the minimum
regression value appear to be an approximate solution to the problem. Main Results. Unlike traditional approaches,
the proposed approach is based on non-parametric approximation of the regression function: a set of hyperparameters – classification efficiency index value. Particular attention is paid to the choice of the classification quality criterion. Due to the use of the mentioned type approximation, it is possible to study the performance indicator behavior out of the trial grid values (“between” its nodes). As it follows from the experiments carried out on various databases, the proposed approach provides a significant increase in the efficiency of hyperparameter tuning in comparison with the basic variants and at the same time maintains almost acceptable performance even for small values of the trial grid power. The novelty of the approach lies in the simultaneous use of non-parametric approximation for the regression function, which links the hyperparameter values with the corresponding values of the quality criterion, selection of the classification quality
criterion, and search method for the global extremum of this function. Practical Relevance. The proposed algorithm for hyperparameters tuning can be used in any systems built on the principles of machine learning, for example, in process control systems, biometric systems and machine vision systems
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