scholarly journals Comparative study on Classification of Digital Images

2017 ◽  
Vol 10 (2) ◽  
pp. 413-416
Author(s):  
H. B Basanth
Keyword(s):  
Comparative Study ◽  
Computer Graphic ◽  
Digital Images ◽  
Natural Images ◽  
Video Classification ◽  
Software Packages ◽  
Graphic Images ◽  
Graphics Software ◽  
Very High

Digital images are widespread today. The use of digital images is classified into natural images and computer graphic images. Discrimination of natural images and computer graphic (CG) images are used in the applications which include flower classification, indexing of images, video classification and many more. With the rapid growth in the image rendering technology, the user can produce very high realistic computer graphic images using sophisticated graphics software packages. Due to high realism in CG images, it is very difficult for the user to distinguish it from natural images by a naked eye. This paper presents comparative study of the existing schemes used to classify digital images.

scholarly journals CNN architecture comparison for radio galaxy classification

2021 ◽  
Vol 503 (2) ◽  
pp. 1828-1846
Author(s):  
Burger Becker ◽  
Mattia Vaccari ◽  
Matthew Prescott ◽  
Trienko Grobler
Keyword(s):  
Comparative Study ◽  
Galaxy Evolution ◽  
Radio Galaxy ◽  
Recognition Performance ◽  
Model Complexity ◽  
Great Promise ◽  
Radio Sources ◽  
Morphological Classification ◽  
Computational Performance

ABSTRACT The morphological classification of radio sources is important to gain a full understanding of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citizen scientists, and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is convolutional neural networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. First, a proper analysis of whether overfitting occurs when training CNNs to perform radio galaxy morphological classification using a small curated training set is needed. Secondly, a good comparative study regarding the practical applicability of the CNN architectures in literature is required. Both of these shortcomings are addressed in this paper. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, computational complexity, and mean per class accuracy. As part of this study, we also investigate the effect that receptive field, stride length, and coverage have on recognition performance. For the sake of completeness, we also investigate the recognition performance gains that we can obtain by employing classification ensembles. A ranking system based upon recognition and computational performance is proposed. MCRGNet, Radio Galaxy Zoo, and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.

Structure and Semantics of Tactile Verbs in a Comparative Aspect (On the Material of the English, German and Russian Languages)

2021 ◽  
pp. 58-63
Author(s):  
Yu. A. Sakhno
Keyword(s):  
Comparative Study ◽  
Semantic Features ◽  
Semantic Classification ◽  
Comparative Aspect ◽  
Similarities And Differences ◽  
The Comparative Study ◽  
Linguistic Units

This article deals with the study of the structural and semantic features of tactile verbs (hereinafter TVs) in English, German and Russian. Particular attention is paid to the comparative study of TVs, which allows us to identify structural and semantic similarities and differences of linguistic units studied. The structural and semantic classification of TVs in the compared languages is also provided.

scholarly journals Analysis of the coefficient of variation in shear and tensile bond strength tests

2005 ◽  
Vol 13 (3) ◽  
pp. 243-246 ◽  
Author(s):  
Fábio Lourenço Romano ◽  
Gláucia Maria Bovi Ambrosano ◽  
Maria Beatriz Borges de Araújo Magnani ◽  
Darcy Flávio Nouer
Keyword(s):  
Coefficient Of Variation ◽  
Mean Value ◽  
Scientific Article ◽  
A Value ◽  
Statistical Analysis System ◽  
Strength Tests ◽  
Analysis System ◽  
Very High

The coefficient of variation is a dispersion measurement that does not depend on the unit scales, thus allowing the comparison of experimental results involving different variables. Its calculation is crucial for the adhesive experiments performed in laboratories because both precision and reliability can be verified. The aim of this study was to evaluate and to suggest a classification of the coefficient variation (CV) for in vitro experiments on shear and tensile strengths. The experiments were performed in laboratory by fifty international and national studies on adhesion materials. Statistical data allowing the estimation of the coefficient of variation was gathered from each scientific article since none of them had such a measurement previously calculated. Excel worksheet was used for organizing the data while the sample normality was tested by using Shapiro Wilk tests (alpha = 0.05) and the Statistical Analysis System software (SAS). A mean value of 6.11 (SD = 1.83) for the coefficient of variation was found by the data analysis and the data had a normal distribution (p>0.05). A range classification was proposed for the coefficient of variation from such data, that is, it should be considered low for a value lesser than 2.44; intermediate for a value between 2.44 and 7.94, high for a value between 7.94 and 9.78, and finally, very high for a value greater than 9.78. Such classification can be used as a guide for experiments on adhesion materials, thus making the planning easier as well as revealing precision and validity concerning the data.

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