scholarly journals Light curve feature extraction from astronomical source

2021 ◽  
Author(s):  
A. D. Lavrukhina ◽  
◽  
K. L. Malanchev ◽  
◽  
Keyword(s):  
Feature Extraction ◽  
Light Curve ◽  
Automatic Extraction ◽  
Space And Time ◽  
Celestial Sphere

Astronomy is entering the era of large surveys of the variable sky such as Zwicky Transient Facility and forthcoming Legacy Survey of Space and Time. These surveys are dedicated to observe the apparent celestial sphere in several days. The usage of an automatic extraction of light curve features is required to work effectively with such amount of data, especially in problems of classification and characterisation of variable sources. In this work, we present the new Python library for extraction of 25 light curve features of variable astronomic sources.

scholarly journals A NEW APPROACH FOR URBAN ROADS DETECTION USING LASER DATA AND AERIAL DIGITAL IMAGES

2016 ◽  
Vol 10 (1) ◽  
pp. 83-97 ◽  
Author(s):  
Francisco Assis Da Silva ◽  
Anderson Akio Gohara ◽  
Mário Augusto Pazoti ◽  
Danillo Roberto Pereira ◽  
Almir Olivette Artero ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Aerial Images ◽  
Automatic Extraction ◽  
New Approach ◽  
Additional Information ◽  
Automatic Feature Extraction ◽  
Laser Data ◽  
Sensitivity Rate ◽  
Road Segments

The automatic feature extraction from digital aerial images is not a trivial task mainly due to occlusion problems, shadows and different viewpoints. To obtain an improved feature extraction we used laser data, which have additional information such as height and material type of the surface. In this paper we performed the combination of digital image and laser data in order to improve the results of automatic extraction of urban roads. Initially, the urban roads were detected from the response of laser information; in the sequence we applied two different approaches to connect the disconnected road segments. The results were very promising, with sensitivity rate of 92%.

scholarly journals Algorithm for extracting product feature from e-commerce comment

2021 ◽  
Vol 22 (2) ◽  
pp. 1199
Author(s):  
Chanida Kaewphet ◽  
Nawaporn Wisitpongpun
Keyword(s):  
Feature Extraction ◽  
Mobile Phone ◽  
Word Frequency ◽  
Hybrid Approach ◽  
Online Reviews ◽  
The Other ◽  
Automatic Extraction ◽  
Purchasing Decisions ◽  
Product Features ◽  
Different Types

<span>Reviews of e-commerce play an important role in online purchasing decisions. Consumers are likely to read reviews and comments on products from other consumers. In addition to those opinions that reflect consumers' trust in products, it also provides each product's distinctive properties. Today, there are many online reviews, resulting in enormous comments and suggestions. However, as fully reading reviews is quite difficult, this article presents 3 algorithms for automatic extraction of product features hidden in e-commerce reviews: a traditional frequency-based product feature extraction (F-PFE), syntax analyzer system (SAS), and the hybrid approach called the frequency and syntax-based product feature extraction (FaS-PFE). The proposed algorithms were tested against 4 different types of products: shampoo, skincare, mobile phone, and tablet, using reviews from amazon.com. Based on the product review used in this study, it was found that the SAS can help improve the performance in terms of precision by 15% when compared with the traditional F-PEE approach. When considering both the word frequency and syntax, FaS-PFE clearly outperforms the other two approaches with 94.00% precision and 95.13% recall.</span>

Research on the Feature Parameter Extraction of Wheat Seeds’ Bad Point Based on Image Processing

2014 ◽  
Vol 644-650 ◽  
pp. 4140-4143
Author(s):  
Zhe Wen ◽  
Qian Dong ◽  
Jie Zhu ◽  
Ya Bin Fan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Automatic Extraction ◽  
Wheat Seed ◽  
Manual Operation ◽  
Feature Parameter ◽  
Wheat Seeds

It is very important that study the feature parameter extraction of bad point of wheat seeds based on image processing for judging the quality of wheat. Using image processing extract and analyze the collected images information, and based on the collected information analyze the bad point information of wheat seed, then extract the feature parameters. Traditional bad point’s feature extraction methods are completed by the manual operation, and the efficient is lower. Currently, by means of image processing technology can extract the bad point’s feature of wheat seed automatically. To this end, the research status of seed feature extraction based on image processing are reviewed and prospected. Experiments show that the method can better complete the bad point’s feature automatic extraction and recognition of wheat seeds.

Feature Extraction for Casting Core Development

1993 ◽  
Vol 115 (4) ◽  
pp. 744-750 ◽  
Author(s):  
M. A. Ganter ◽  
P. A. Skoglund
Keyword(s):  
Feature Extraction ◽  
Boundary Representation ◽  
Automatic Extraction ◽  
Global Feature ◽  
Multiple Objects ◽  
The Core ◽  
Solid Models ◽  
Original Part ◽  
Boundary Perturbations ◽  
Core Features

Feature extraction techniques are presented for the generation of casting core patterns from a boundary representation (B-Rep) solid model. Techniques are presented which would allow for automatic extraction of three classes of core features (internal voids, single and multi-surface holes, and boundary perturbations). The task of extracting casting cores from solid models involves recognizing a collection of entities (i.e., slots, bosses, undercut surfaces, local and global concavities, etc.) from the set of lower level entities (i.e., the B-Rep structure). To this end, a combination of solid modeling B-Rep and graph structures and their associated methods will be used for casting core development. Appropriate local features are identified and extracted from the original object, and are grouped into one or more new object(s) (termed a core-object). If the core-object is multiply connected (i.e., composed of multiple objects), it is graph separated into global feature objects. Each of these global feature objects represents a core in the final pattern. Lastly, the geometry of the original part is augmented to add core prints where core geometries were extracted. The core print, as currently developed, combines the extracted core geometry and its convex hull.

Feature Extraction for Casting Core Development

1991 ◽  
Author(s):  
M. A. Ganter ◽  
P. A. Skoglund
Keyword(s):  
Feature Extraction ◽  
Boundary Representation ◽  
Automatic Extraction ◽  
Global Feature ◽  
Multiple Objects ◽  
The Core ◽  
Solid Models ◽  
Original Part ◽  
Boundary Perturbations ◽  
Core Features

Abstract Feature extraction techniques are presented for the generation of casting core patterns from a boundary representation (B-Rep) solid model. Techniques are presented which would allow for automatic extraction of three classes of core features (internal voids, single and multi-surface holes, and boundary perturbations). The task of extracting casting cores from solid models involves recognizing a collection of entities (i.e. slots, bosses, undercut surfaces, local and global concavities, etc.) from the set of lower level entities (i.e. the B-Rep structure). To this end, a combination of solid modeling B-Rep and graph structures and their associated methods will be used for casting core development. Appropriate local features are identified and extracted from the original object, and are grouped into one or more new object(s) (termed a core-object). If the core-object is multiply connected (i.e. composed of multiple objects), it is graph separated into global feature objects. Each of these global feature objects represents a core in the final pattern. Lastly, the geometry of the original part is augmented to add core prints where core geometries were extracted. The core print, as currently developed, combines the extracted core geometry and its convex hull.

scholarly journals A NEW APPROACH FOR URBAN ROADS DETECTION USING LASER DATA AND AERIAL DIGITAL IMAGES

2016 ◽  
Vol 10 (1) ◽  
pp. 83-97
Author(s):  
Francisco Assis Da Silva ◽  
Anderson Akio Gohara ◽  
Mário Augusto Pazoti ◽  
Danillo Roberto Pereira ◽  
Almir Olivette Artero ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Aerial Images ◽  
Automatic Extraction ◽  
New Approach ◽  
Additional Information ◽  
Automatic Feature Extraction ◽  
Laser Data ◽  
Sensitivity Rate ◽  
Road Segments

The automatic feature extraction from digital aerial images is not a trivial task mainly due to occlusion problems, shadows and different viewpoints. To obtain an improved feature extraction we used laser data, which have additional information such as height and material type of the surface. In this paper we performed the combination of digital image and laser data in order to improve the results of automatic extraction of urban roads. Initially, the urban roads were detected from the response of laser information; in the sequence we applied two different approaches to connect the disconnected road segments. The results were very promising, with sensitivity rate of 92%.

The Eclipsing Binary WX Eridani

1979 ◽  
Vol 46 ◽  
pp. 385
Author(s):  
M.B.K. Sarma ◽  
K.D. Abhankar
Keyword(s):  
Light Curve ◽  
Spectral Type ◽  
Orbital Period ◽  
Primary Component ◽  
Light Curves ◽  
Absorption Feature ◽  
Primary Star ◽  
Eclipsing Binary ◽  
The Times ◽  
Phase Angles

AbstractThe Algol-type eclipsing binary WX Eridani was observed on 21 nights on the 48-inch telescope of the Japal-Rangapur Observatory during 1973-75 in B and V colours. An improved period of P = 0.82327038 days was obtained from the analysis of the times of five primary minima. An absorption feature between phase angles 50-80, 100-130, 230-260 and 280-310 was present in the light curves. The analysis of the light curves indicated the eclipses to be grazing with primary to be transit and secondary, an occultation. Elements derived from the solution of the light curve using Russel-Merrill method are given. From comparison of the fractional radii with Roche lobes, it is concluded that none of the components have filled their respective lobes but the primary star seems to be evolving. The spectral type of the primary component was estimated to be F3 and is found to be pulsating with two periods equal to one-fifth and one-sixth of the orbital period.

RSCVn Stars in the Southern Hemisphere

1979 ◽  
Vol 46 ◽  
pp. 371-384 ◽  
Author(s):  
J.B. Hearnshaw
Keyword(s):  
Light Curve ◽  
Southern Hemisphere ◽  
Orbital Period ◽  
Binary Stars ◽  
Small Amplitude ◽  
Light Variation ◽  
Orbital Phase

RSCVn stars are fully detached binary stars which show intrinsic small amplitude (up to 0.3 amplitude peak-to-peak) light variations, as well as, in most of the known cases, eclipses. The spectra are F to G, IV to V for the hotter component and usually KOIV for the cooler. They are also characterised by abnormally strong H and K emission from the cooler star, or, occasionally, from both components. The orbital and light curve periods are in the range 1 day to 2 weeks. An interesting feature is the migration of the light variations to earlier orbital phase, as the light variation period is shorter than the orbital period by a few parts in 10+4to a few parts in 10+3.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

Languages in Space and Time

2020 ◽  
Author(s):  
Marco Patriarca ◽  
Els Heinsalu ◽  
Jean Leó Leonard
Keyword(s):  
Space And Time
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