iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.5220/0010346803410348
SciTePress - Publication Details
loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Author: V. E. Antsiperov

Affiliation: Kotelnikov Institute of Radioengineering and Electronics of RAS, Mokhovaya 11-7, Moscow, Russian Federation

Keyword(s): Machine Learning, Image Recognition, Photon Counting Detectors, Poisson Point Process Intensity, Precedent‒based Identification, Naive Bayes Model, Gaussian Mixture Model, K‒means Clustering.

Abstract: The paper discusses a new approach to recognition / identification of the test objects according to their intensity shape in the images registered by photon counting detectors. The main problem analyzed within the framework of the proposed approach is related to the identification decision (inference ) based on a registered set of discrete photocounts (p̃hotons) regarding the similarity of the shape of the object's intensity in the image to the shape of previously observed objects (precedents). It is shown that when the intensity shape is approximated by a mixture of Gaussian components within the framework of this approach, a recurrent identification algorithm can be synthesized, similar to the well-known K-means clustering algorithm in the machine (statistical) learning.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 173.236.136.203

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Antsiperov, V. (2021). New Maximum Similarity Method for Object Identification in Photon Counting Imaging. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 341-348. DOI: 10.5220/0010346803410348

@conference{icpram21,
author={V. E. Antsiperov},
title={New Maximum Similarity Method for Object Identification in Photon Counting Imaging},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={341-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010346803410348},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - New Maximum Similarity Method for Object Identification in Photon Counting Imaging
SN - 978-989-758-486-2
IS - 2184-4313
AU - Antsiperov, V.
PY - 2021
SP - 341
EP - 348
DO - 10.5220/0010346803410348
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic