BRISKS: Binary Features for Spherical Images on a Geodesic Grid

2017 ◽  
Author(s):  
Hao Guan ◽  
William A. P. Smith
Keyword(s):  
Binary Features ◽  
Spherical Images

Methods for constructing virtual panoramic tours

2020 ◽  
Author(s):  
Elizaveta Koptenok ◽  
Natalia Lagereva ◽  
Arsenii Savenko ◽  
Ilia Fomin
Keyword(s):  
Real Estate ◽  
Web Service ◽  
Virtual Museums ◽  
Implementation Method ◽  
Spherical Images

The article discusses the existing methods for building virtual panoramic tours. A proprietary implementation method based on the Yandex JS API technology is proposed. The designed web service allows to automatically create virtual panoramic tours from a collection of panoramic spherical images "in one click", in contrast to the considered analogues. The developed technology should be used for educational purposes, for example, for the implementation of virtual museums, as well as on real estate trading platforms to increase ad views and improve sales.

Aperture harmony

2018 ◽  
Author(s):  
Harry van der Hulst
Keyword(s):  
Vowel Harmony ◽  
Romance Languages ◽  
Common Element ◽  
Binary Features ◽  
High Vowels

This chapter analyzes a number of vowel harmony systems which have been described or analyzed in terms of aperture (lowering or raising, including complete harmony). This takes us into areas where the literature on vowel harmony discusses cases involving the following binary features: [± high], [± low], [± ATR], and [± RTR]. Raising has been thought of as problematic for unary ‘IUA’ systems as these systems lack a common element for high vowels. This chapter suggests that raising can be attributed to ATR-harmony. The chapter also discusses typological generalizations and analyzes metaphony in Romance languages.

Biomass estimates derived from sector subsampling of 360° spherical images

2021 ◽  
Author(s):  
Xiao Dai ◽  
Mark J Ducey ◽  
Haozhou Wang ◽  
Ting-Ru Yang ◽  
Yung-Han Hsu ◽  
...  
Keyword(s):  
Basal Area ◽  
Sampling Errors ◽  
Eastern Canada ◽  
Individual Tree ◽  
Inventory Costs ◽  
Standard Error Estimation ◽  
The Mean ◽  
Biomass Ratio ◽  
Spherical Images ◽  
Additional Level

Abstract Efficient subsampling designs reduce forest inventory costs by focusing sampling efforts on more variable forest attributes. Sector subsampling is an efficient and accurate alternative to big basal area factor (big BAF) sampling to estimate the mean basal area to biomass ratio. In this study, we apply sector subsampling of spherical images to estimate aboveground biomass and compare our image-based estimates with field data collected from three early spacing trials on western Newfoundland Island in eastern Canada. The results show that sector subsampling of spherical images produced increased sampling errors of 0.3–3.4 per cent with only about 60 trees measured across 30 spherical images compared with about 4000 trees measured in the field. Photo-derived basal area was underestimated because of occluded trees; however, we implemented an additional level of subsampling, collecting field-based basal area counts, to correct for bias due to occluded trees. We applied Bruce’s formula for standard error estimation to our three-level hierarchical subsampling scheme and showed that Bruce’s formula is generalizable to any dimension of hierarchical subsampling. Spherical images are easily and quickly captured in the field using a consumer-grade 360° camera and sector subsampling, including all individual tree measurements, were obtained using a custom-developed python software package. The system is an efficient and accurate photo-based alternative to field-based big BAF subsampling.

Inverse Biometrics: Reconstructing Grayscale Finger Vein Images from Binary Features

2020 ◽  
Author(s):  
Christof Kauba ◽  
Simon Kirchgasser ◽  
Vahid Mirjalili ◽  
Andreas Uhl ◽  
Arun Ross
Keyword(s):  
Finger Vein ◽  
Binary Features

Review Paper on Machine learning-based prediction of Corona Virus

2021 ◽  
Vol 9 (8) ◽  
pp. 2225-2229
Author(s):  
Miss. Aakansha P. Tiwari
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Contact Tracing ◽  
Test Results ◽  
Effective Contact ◽  
Machine Learning Approach ◽  
Binary Features ◽  
Simple Features ◽  
Medical Examiners

Abstract: Effective contact tracing of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and might mitigate the burden on healthcare system. Prediction models that combine several features to approximate the danger of infection are developed. These aim to help medical examiners worldwide in treatment of patients, especially within the context of limited healthcare resources. They established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to own COVID-19 coronavirus). Test set contained data from the upcoming week (47,401 tested individuals of whom 3624 were confirmed to own COVID-19 disease). Their model predicted COVID-19 test results with highest accuracy using only eight binary features: sex, age ≥60 years, known contact with infected patients, and also the appearance of 5 initial clinical symptoms appeared. Generally, supported the nationwide data publicly reported by the Israeli Ministry of Health, they developed a model that detects COVID-19 cases by simple features accessed by asking basic inquiries to the affected patient. Their framework may be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited and important. Keywords: Machine Learning, SARS-COV-2, COVID-19, Coronavirus.

Sign in / Sign up

Export Citation Format

Share Document