scholarly journals Rapid sorting of radio galaxy morphology using Haralick features

2021 ◽  
Vol 502 (3) ◽  
pp. 3417-3425
Author(s):  
Kushatha Ntwaetsile ◽  
James E Geach
Keyword(s):  
Clustering Algorithm ◽  
Radio Galaxy ◽  
Morphological Characteristics ◽  
Parametric Representation ◽  
Radio Galaxies ◽  
Image Texture ◽  
Automated Classification ◽  
Haralick Features ◽  
Occurrence Matrix ◽  
Galaxy Morphology

ABSTRACT We demonstrate the use of Haralick features for the automated classification of radio galaxies. The set of thirteen Haralick features represent an extremely compact non-parametric representation of image texture, and are calculated directly from imagery using the Grey Level Co-occurrence Matrix (GLCM). The GLCM is an encoding of the relationship between the intensity of neighbouring pixels in an image. Using 10 000 sources detected in the first data release of the LOFAR Two-metre Sky Survey (LoTSS), we demonstrate that Haralick features are highly efficient, rotationally invariant descriptors of radio galaxy morphology. After calculating Haralick features for LoTSS sources, we employ the fast density-based hierarchical clustering algorithm hdbscan to group radio sources into a sequence of morphological classes, illustrating a simple methodology to classify and label new, unseen galaxies in large samples. By adopting a ‘soft’ clustering approach, we can assign each galaxy a probability of belonging to a given cluster, allowing for more flexibility in the selection of galaxies according to combinations of morphological characteristics and for easily identifying outliers: those objects with a low probability of belonging to any cluster in the Haralick space. Although our demonstration focuses on radio galaxies, Haralick features can be calculated for any image, making this approach also relevant to large optical imaging galaxy surveys.

scholarly journals Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shrouq H. Aleithan ◽  
Doaa Mahmoud-Ghoneim
Keyword(s):  
Neural Networks ◽  
Texture Analysis ◽  
Higher Order ◽  
Image Texture ◽  
Proper Class ◽  
Automated Classification ◽  
Linear Discriminant ◽  
Ann Classifier ◽  
Occurrence Matrix

AbstractThe need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS2 and WS2, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS2 and WS2, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class.

scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
Gabor Filter ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Support Vector ◽  
Texture Image ◽  
Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

scholarly journals Radio Spectra and Particle Ages of the Head-Tail Radio Galaxy NGC1265

1996 ◽  
Vol 175 ◽  
pp. 347-348
Author(s):  
L. Feretti ◽  
G. Giovannini ◽  
U. Klein ◽  
K.-H. Mack ◽  
L.G. Sijbring
Keyword(s):  
Magnetic Field ◽  
Radio Galaxy ◽  
Low Frequency ◽  
Radio Galaxies ◽  
Field Structure ◽  
Magnetic Field Structure ◽  
Linearly Polarized ◽  
Faraday Effects ◽  
Perseus Cluster ◽  
Comprehensive Study

We have performed sensitive observations of three classical head-tail radio galaxies at λ11.1, 6.3, and 2.8 cm using the Effelsberg 100-m telescope (Zech, 1994). Complete maps of the sources 3C129, NGC1265, and 3C465 were obtained, including the distributions of the linearly polarized intensity. Together with the low-frequency interferometric maps these allow a comprehensive study of their radio spectra and, based on models of particle losses, the derivations of particle ages across these sources. The highest frequency involved allows an unambiguous derivation of the projected magnetic field structure, unimpeded by Faraday effects. Here we focus on NGC1265, which is located in the Perseus Cluster.

scholarly journals CBIR Penyakit Kulit dengan Metode Co-Occurrence Matrix dan Color Moments

2017 ◽  
pp. 62
Author(s):  
Ni Putu Chendy Widya Santi ◽  
I Ketut Gede Darma Putra ◽  
I Made Sunia Raharja
Keyword(s):  
Skin Disease ◽  
Color Space ◽  
Texture Feature ◽  
Image Texture ◽  
Research Use ◽  
Color Feature ◽  
Skin Tag ◽  
Moments Method ◽  
Similarity Computation ◽  
Occurrence Matrix

Content Based Image Retrieval (CBIR) is a technique for searching images from database based on information from the image which developed because the technique based on text-based is less effective for represent an image. CBIR skin disease in this research use 12 sample of skin disease images such as Acne, Acropustulosis, Alopecia, Dermatitis, Hemangioma, Herpes, Ichtyosis, Molluscum, Nummular, Skin Tag, Urticaria, and Vitiligo. Method use for this research is for extraction texture feature and color feature from a skin disease image. Texture feature is using co-occurrence Matrix which compute energy, contrast, entropy, homogeneity, and correlation until vector texture result. Extraction color use color moments to compute color space using three moments which result color feature from color distributions such as mean, standard deviation, and skewness. Final result showed the comparison of similarity computation of two methods is the acuration of Color Moments method is more robust than Co-occurrence Matrix Method for skin disease images.

scholarly journals NGC 3894: a young radio galaxy seen by Fermi-LAT

2020 ◽  
Vol 635 ◽  
pp. A185 ◽  
Author(s):  
G. Principe ◽  
G. Migliori ◽  
T. J. Johnson ◽  
F. D’Ammando ◽  
M. Giroletti ◽  
...  
Keyword(s):  
Radio Galaxy ◽  
Radio Galaxies ◽  
High Energy ◽  
Apparent Velocity ◽  
Large Area ◽  
Long Baseline ◽  
Full Period ◽  
Very Long Baseline Array ◽  
High Energy Emission ◽  
First Time

Context. According to radiative models, radio galaxies may produce γ-ray emission from the first stages of their evolution. However, very few such galaxies have been detected by the Fermi Large Area Telescope (LAT) so far. Aims. NGC 3894 is a nearby (z = 0.0108) object that belongs to the class of compact symmetric objects (CSOs, i.e., the most compact and youngest radio galaxies), which is associated with a γ-ray counterpart in the Fourth Fermi-LAT source catalog. Here we present a study of the source in the γ-ray and radio bands aimed at investigating its high-energy emission and assess its young nature. Methods. We analyzed 10.8 years of Fermi-LAT data between 100 MeV and 300 GeV and determined the spectral and variability characteristics of the source. Multi-epoch very long baseline array (VLBA) observations between 5 and 15 GHz over a period of 35years were used to study the radio morphology of NGC 3894 and its evolution. Results. NGC 3894 is detected in γ-rays with a significance >9σ over the full period, and no significant variability has been observed in the γ-ray flux on a yearly time-scale. The spectrum is modeled with a flat power law (Γ = 2.0 ± 0.1) and a flux on the order of 2.2 × 10−9 ph cm−2 s−1. For the first time, the VLBA data allow us to constrain with high precision the apparent velocity of the jet and counter-jet side to be βapp, NW = 0.132 ± 0.004 and βapp, SE = 0.065 ± 0.003, respectively. Conclusions. Fermi-LAT and VLBA results favor the youth scenario for the inner structure of this object, with an estimated dynamical age of 59 ± 5 years. The estimated range of viewing angle (10° < θ <  21°) does not exclude a possible jet-like origin of the γ-ray emission.

scholarly journals The morphological classification of distant radio galaxies explored with three-dimensional simulations

2019 ◽  
Vol 490 (1) ◽  
pp. 1363-1382 ◽  
Author(s):  
Michael D Smith ◽  
Justin Donohoe
Keyword(s):  
Radio Galaxy ◽  
Three Dimensional ◽  
Radio Galaxies ◽  
Morphological Classification ◽  
Helical Structures ◽  
Radio Jets ◽  
Source Type ◽  
Radio Lobe ◽  
Three Dimensional Simulations

ABSTRACT We explore the observational implications of a large systematic study of high-resolution three-dimensional simulations of radio galaxies driven by supersonic jets. For this fiducial study, we employ non-relativistic hydrodynamic adiabatic flows from nozzles into a constant pressure-matched environment. Synchrotron emissivity is approximated via the thermal pressure of injected material. We find that the morphological classification of a simulated radio galaxy depends significantly on several factors with increasing distance (i.e. decreasing observed resolution) and decreasing orientation often causing reclassification from FR II (limb-brightened) to FR I (limb-darkened) type. We introduce the Lobe or Limb Brightening Index (LBI) to measure the radio lobe type more precisely. The jet density also has an influence as expected with lower density leading to broader and bridged lobe morphologies as well as brighter radio jets. Hence, relating observed source type to the intrinsic jet dynamics is not straightforward. Precession of the jet direction may also be responsible for wide relaxed sources with lower LBI and FR class as well as for X-shaped and double–double structures. Helical structures are not generated because the precession is usually too slow. We conclude that distant radio galaxies could appear systematically more limb darkened due to merger-related redirection and precession as well as due to the resolution limitation.

scholarly journals Revisiting the Fanaroff–Riley dichotomy and radio-galaxy morphology with the LOFAR Two-Metre Sky Survey (LoTSS)

2019 ◽  
Vol 488 (2) ◽  
pp. 2701-2721 ◽  
Author(s):  
B Mingo ◽  
J H Croston ◽  
M J Hardcastle ◽  
P N Best ◽  
K J Duncan ◽  
...  
Keyword(s):  
Radio Galaxy ◽  
Large Population ◽  
Automated Classification ◽  
Host Galaxies ◽  
Data Set ◽  
Extended Radio ◽  
Sky Survey ◽  
Low Surface Brightness ◽  
Extended Sources ◽  
Galaxy Population

Abstract The relative positions of the high and low surface brightness regions of radio-loud active galaxies in the 3CR sample were found by Fanaroff and Riley to be correlated with their luminosity. We revisit this canonical relationship with a sample of 5805 extended radio-loud active galactic nuclei (AGN) from the LOFAR Two-Metre Sky Survey (LoTSS), compiling the most complete data set of radio-galaxy morphological information obtained to date. We demonstrate that, for this sample, radio luminosity does not reliably predict whether a source is edge-brightened (FRII) or centre-brightened (FRI). We highlight a large population of low-luminosity FRIIs, extending three orders of magnitude below the traditional FR break, and demonstrate that their host galaxies are on average systematically fainter than those of high-luminosity FRIIs and of FRIs matched in luminosity. This result supports the jet power/environment paradigm for the FR break: low-power jets may remain undisrupted and form hotspots in lower mass hosts. We also find substantial populations that appear physically distinct from the traditional FR classes, including candidate restarting sources and ‘hybrids’. We identify 459 bent-tailed sources, which we find to have a significantly higher SDSS cluster association fraction (at z &lt; 0.4) than the general radio-galaxy population, similar to the results of previous work. The complexity of the LoTSS faint, extended radio sources not only demonstrates the need for caution in the automated classification and interpretation of extended sources in modern radio surveys, but also reveals the wealth of morphological information such surveys will provide and its value for advancing our physical understanding of radio-loud AGN.

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