Rotation and Gray-Scale Invariant Classification of Textures Improved by Spatial Distribution of Features

2005 ◽  
pp. 250-259 ◽  
Author(s):  
Gouchol Pok ◽  
Keun Ho Ryu ◽  
Jyh-charn Lyu
Keyword(s):  
Spatial Distribution ◽  
Gray Scale ◽  
Scale Invariant

scholarly journals Malware Classification with Improved Convolutional Neural Network Model

2020 ◽  
Vol 12 (6) ◽  
pp. 30-43
Author(s):  
Sumit S. Lad ◽  
◽  
Amol C. Adamuthe
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Personal Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Scale ◽  
Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

scholarly journals A weather regime characterisation of winter biomass aerosol transport from southern Africa

2021 ◽  
Author(s):  
Marco Gaetani ◽  
Benjamin Pohl ◽  
Maria del Carmen Alvarez Castro ◽  
Cyrille Flamant ◽  
Paola Formenti
Keyword(s):  
Spatial Distribution ◽  
Southern Africa ◽  
Forest Fires ◽  
South Atlantic ◽  
Austral Winter ◽  
The South ◽  
Predictive Tool ◽  
Weather Regime ◽  
Synoptic Variability

Abstract. During austral winter, a compact low cloud deck over South Atlantic contrasts with clear sky over southern Africa, where forest fires triggered by dry conditions emit large amount of biomass burning aerosols (BBA) in the free troposphere. Most of the BBA burden crosses South Atlantic embedded in the tropical easterly flow. However, midlatitude synoptic disturbances can deflect part of the aerosol from the main transport path towards southern extratropics. In this study, a characterisation of the synoptic variability controlling the spatial distribution of BBA in southern Africa and South Atlantic during austral winter (August to October) is presented. By analysing atmospheric circulation data from reanalysis products, a 6-class weather regime (WR) classification of the region is constructed. The classification reveals that the synoptic variability is composed by four WRs representing disturbances travelling at midlatitudes, and two WRs accounting for pressure anomalies in the South Atlantic. The WR classification is then successfully used to characterise the aerosol spatial distribution in the region in the period 2003–2017, in both reanalysis products and station data. Results show that the BBA transport towards southern extratropics is controlled by weather regimes associated with midlatitude synoptic disturbances. In particular, depending on the relative position of the pressure anomalies along the midlatitude westerly flow, the BBA transport is deflected from the main tropical route towards southern Africa or the South Atlantic. This paper presents the first objective classification of the winter synoptic circulation over South Atlantic and southern Africa. The classification shows skills in characterising the BBA transport, indicating the potential for using it as a diagnostic/predictive tool for aerosol dynamics, which is a key component for the full understanding and modelling of the complex radiation-aerosol-cloud interactions controlling the atmospheric radiative budget in the region.

Logo Matching and Recognition Based on Context

2018 ◽  
pp. 164-176
Author(s):  
Tapan Kumar Das
Keyword(s):  
Real World ◽  
Scale Invariant Feature Transform ◽  
Gray Scale ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Key Points ◽  
Gray Scale Image ◽  
Feature Transform ◽  
Graphic Elements ◽  
Scale Invariant Feature

Logos are graphic productions that recall some real-world objects or emphasize a name, simply display some abstract signs that have strong perceptual appeal. Color may have some relevance to assess the logo identity. Different logos may have a similar layout with slightly different spatial disposition of the graphic elements, localized differences in the orientation, size and shape, or differ by the presence/absence of one or few traits. In this chapter, the author uses ensemble-based framework to choose the best combination of preprocessing methods and candidate extractors. The proposed system has reference logos and test logos which are verified depending on some features like regions, pre-processing, key points. These features are extracted by using gray scale image by scale-invariant feature transform (SIFT) and Affine-SIFT (ASIFT) descriptor method. Pre-processing phase employs four different filters. Key points extraction is carried by SIFT and ASIFT algorithm. Key points are matched to recognize fake logo.

scholarly journals Measuring urban form: Overcoming terminological inconsistencies for a quantitative and comprehensive morphologic analysis of cities

2020 ◽  
pp. 239980832091044 ◽  
Author(s):  
Martin Fleischmann ◽  
Ombretta Romice ◽  
Sergio Porta
Keyword(s):  
Spatial Distribution ◽  
Quantitative Analysis ◽  
Urban Form ◽  
Large Scale ◽  
Global Scale ◽  
Urban Morphology ◽  
Systematic Classification ◽  
Social Patterns ◽  
The Impact

Unprecedented urbanisation processes characterise the Great Acceleration, urging urban researchers to make sense of data analysis in support of evidence-based and large-scale decision-making. Urban morphologists are no exception since the impact of urban form on fundamental natural and social patterns (equity, prosperity and resource consumption’s efficiency) is now fully acknowledged. However, urban morphology is still far from offering a comprehensive and reliable framework for quantitative analysis. Despite remarkable progress since its emergence in the late 1950s, the discipline still exhibits significant terminological inconsistencies with regards to the definition of the fundamental components of urban form, which prevents the establishment of objective models for measuring it. In this article, we present a study of existing methods for measuring urban form, with a focus on terminological inconsistencies, and propose a systematic and comprehensive framework to classify urban form characters, where ‘urban form character’ stands for a characteristic (or feature) of one kind of urban form that distinguishes it from another kind. In particular, we introduce the Index of Elements that allows for a univocal and non-interpretive description of urban form characters. Based on such Index of Elements, we develop a systematic classification of urban form according to six categories (dimension, shape, spatial distribution, intensity, connectivity and diversity) and three conceptual scales (small, medium, large) based on two definitions of scale (extent and grain). This framework is then applied to identify and organise the urban form characters adopted in available literature to date. The resulting classification of urban form characters reveals clear gaps in existing research, in particular, in relation to the spatial distribution and diversity characters. The proposed framework reduces the current inconsistencies of urban morphology research, paving the way to enhanced methods of urban form systematic and quantitative analysis at a global scale.

scholarly journals Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Alejandra Cruz-Bernal ◽  
Martha M. Flores-Barranco ◽  
Dora L. Almanza-Ojeda ◽  
Sergio Ledesma ◽  
Mario A. Ibarra-Manzano
Keyword(s):  
Digital Image ◽  
Visual Inspection ◽  
Experimental Results ◽  
Gray Scale ◽  
High Expectation ◽  
Weak Classifier ◽  
White Region ◽  
Gray Scale Image ◽  
Global Representation

In mammograms, a calcification is represented as small but brilliant white region of the digital image. Earlier detection of malignant calcifications in patients provides high expectation of surviving to this disease. Nevertheless, white regions are difficult to see by visual inspection because a mammogram is a gray-scale image of the breast. To help radiologists in detecting abnormal calcification, computer-inspection methods of mammograms have been proposed; however, it remains an open important issue. In this context, we propose a strategy for detecting calcifications in mammograms based on the analysis of the cluster prominence (cp) feature histogram. The highest frequencies of the cp histogram describe the calcifications on the mammography. Therefore, we obtain a function that models the behaviour of the cp histogram using the Vandermonde interpolation twice. The first interpolation yields a global representation, and the second models the highest frequencies of the histogram. A weak classifier is used for obtaining a final classification of the mammography, that is, with or without calcifications. Experimental results are compared with real DICOM images and their corresponding diagnosis provided by expert radiologists, showing that the cp feature is highly discriminative.

A visual pathway for shape-based invariant classification of gray scale images

2007 ◽  
Vol 14 (4) ◽  
pp. 365-378 ◽  
Author(s):  
Konstantinos A. Raftopoulos ◽  
Nikolaos Papadakis ◽  
Klimis Ntalianis ◽  
Stefanos Kollias
Keyword(s):  
Visual Pathway ◽  
Gray Scale

Chemical stratigraphy of the Paran� lavas (South America): classification of magma types and their spatial distribution

1992 ◽  
Vol 55 (1-2) ◽  
pp. 119-139 ◽  
Author(s):  
David W Peate ◽  
Chris J Hawkesworth ◽  
Marta S M Mantovani
Keyword(s):  
Spatial Distribution ◽  
South America
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