Saddle: Fast and repeatable features with good coverage

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00334662" target="_blank" >RIV/68407700:21230/20:00334662 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.imavis.2019.08.011" target="_blank" >https://doi.org/10.1016/j.imavis.2019.08.011</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.imavis.2019.08.011" target="_blank" >10.1016/j.imavis.2019.08.011</a>

Result language

angličtina

Original language name

Saddle: Fast and repeatable features with good coverage

Original language description

A novel similarity-covariant feature detector that extracts points whose neighborhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile is presented. The saddle condition is verified efficiently by intensity comparisons on two concentric rings that must have exactly two dark-to-bright and two bright-to-dark transitions satisfying certain geometric constraints. Saddle is a fast approximation of Hessian detector as ORB, that implements the FAST detector, is for Harris detector. We propose to use the matching strategy called the first geometric inconsistent with binary descriptors that is suitable for our feature detector, including experiments with fix point descriptors hand-crafted and learned. Experiments show that the Saddle features are general, evenly spread and appearing in high density in a range of images. The Saddle detector is among the fastest proposed. In comparison with detector with similar speed, the Saddle features show superior matching performance on number of challenging datasets. Compared to recently proposed deep-learning based interest point detectors and popular hand-crafted keypoint detectors, evaluated for repeatability in the ApolloScape dataset [1], the Saddle detectors shows the best performance in most of the street-level view sequences a.k.a. traversals.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Image and Vision Computing

ISSN

0262-8856

e-ISSN

1872-8138

Volume of the periodical

97

Issue of the periodical within the volume

May

Country of publishing house

GB - UNITED KINGDOM

Number of pages

17

Pages from-to

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UT code for WoS article

000535899900001

EID of the result in the Scopus database

2-s2.0-85079534086