Systematic generation of moment invariant bases for 2D and 3D tensor fields

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00553375" target="_blank" >RIV/67985556:_____/22:00553375 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0031320321004933" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0031320321004933</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.patcog.2021.108313" target="_blank" >10.1016/j.patcog.2021.108313</a>

Result language

angličtina

Original language name

Systematic generation of moment invariant bases for 2D and 3D tensor fields

Original language description

Moment invariants have been successfully applied to pattern detection tasks in 2D and 3D scalar, vector, and matrix valued data. However so far no flexible basis of invariants exists, i.e., no set that is optimal in the sense that it is complete and independent for every input pattern. In this paper, we prove that a basis of moment invariants can be generated that consists of tensor contractions of not more than two different moment tensors each under the conjecture of the set of all possible tensor contractions to be complete. This result allows us to derive the first generator algorithm that produces flexible bases of moment invariants with respect to orthogonal transformations by selecting a single non-zero moment to pair with all others in these two-factor products. Since at least one non-zero moment can be found in every non-zero pattern, this approach always generates a complete set of descriptors.

Czech name

—

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

—

OECD FORD branch

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

Project

<a href="/en/project/GA18-07247S" target="_blank" >GA18-07247S: Methods and Algorithms for Vector and Tensor Field Image Analysis</a><br>

Continuities

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

Publication year

2022

Confidentiality

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

Name of the periodical

Pattern Recognition

ISSN

0031-3203

e-ISSN

1873-5142

Volume of the periodical

123

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

108313

UT code for WoS article

000711834400009

EID of the result in the Scopus database

2-s2.0-85117588635