Image quality assessment and outliers filtering in an image-based animal supervision system

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKirjan luku tai artikkeliTieteellinenvertaisarvioitu

Kuvaus

This paper presents a probabilistic framework for the image quality assessment (QA), and filtering of outliers, in an image-based animal supervision system (asup). The proposed framework recognizes asup's imperfect frames in two stages. The first stage deals with the similarity analysis of the same-class distributions. The objective of this stage is to maximize the separability measures by defining a set of similarity indicators (SI) under the condition that the number of permissible values for them is restricted to be relatively low. The second stage, namely faulty frame recognition (FFR), deals with asup's QA training and real-time quality assessment (RTQS). In RTQS, decisions are made based on a real-time quality assessment mechanism such that the majority of the defected frames are removed from the consecutive sub routines that calculate the movements. The underlying approach consists of a set of SI indexes employed in a simple Bayesian inference model. The results confirm that a significant amount of defected frames can be efficiently classified by this approach. The performance of the proposed technique is demonstrated by the classification on a cross-validation set of mixed high and low quality frames. The classification shows a true positive rate of 88.6% while the false negative rate is only about 2.5%. © 2017, IGI Global. All rights reserved.
Alkuperäiskielienglanti
OtsikkoBiometrics : Concepts, Methodologies, Tools, and Applications
Sivumäärä17
JulkaisupaikkaHershey, United States
KustantajaIGI Global
Julkaisupäivä2017
ISBN (painettu)9781522509837
ISBN (elektroninen)9781522509844
DOI - pysyväislinkit
TilaJulkaistu - 2017
OKM-julkaisutyyppiA3 Kirjan tai muun kokoomateoksen osa

Tieteenalat

  • 412 Kotieläintiede, maitotaloustiede
  • 113 Tietojenkäsittely- ja informaatiotieteet

Lainaa tätä

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title = "Image quality assessment and outliers filtering in an image-based animal supervision system",
abstract = "This paper presents a probabilistic framework for the image quality assessment (QA), and filtering of outliers, in an image-based animal supervision system (asup). The proposed framework recognizes asup's imperfect frames in two stages. The first stage deals with the similarity analysis of the same-class distributions. The objective of this stage is to maximize the separability measures by defining a set of similarity indicators (SI) under the condition that the number of permissible values for them is restricted to be relatively low. The second stage, namely faulty frame recognition (FFR), deals with asup's QA training and real-time quality assessment (RTQS). In RTQS, decisions are made based on a real-time quality assessment mechanism such that the majority of the defected frames are removed from the consecutive sub routines that calculate the movements. The underlying approach consists of a set of SI indexes employed in a simple Bayesian inference model. The results confirm that a significant amount of defected frames can be efficiently classified by this approach. The performance of the proposed technique is demonstrated by the classification on a cross-validation set of mixed high and low quality frames. The classification shows a true positive rate of 88.6{\%} while the false negative rate is only about 2.5{\%}. {\circledC} 2017, IGI Global. All rights reserved.",
keywords = "Animals, Barium compounds, Bayesian networks, Inference engines, Statistics, Bayesian inference model, Class distributions, False negative rate, Image quality assessment, Probabilistic framework, Separability measure, Similarity analysis, Supervision systems, Image quality, 412 Animal science, dairy science, 113 Computer and information sciences",
author = "Ehsan Khoramshahi and Juha Hietaoja and Anna Valros and Jinhyeon Yun and Matti Pastell",
year = "2017",
doi = "10.4018/978-1-5225-0983-7.ch049",
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isbn = "9781522509837",
booktitle = "Biometrics",
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Image quality assessment and outliers filtering in an image-based animal supervision system. / Khoramshahi, Ehsan; Hietaoja, Juha; Valros, Anna; Yun, Jinhyeon; Pastell, Matti.

Biometrics: Concepts, Methodologies, Tools, and Applications. Hershey, United States : IGI Global, 2017.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKirjan luku tai artikkeliTieteellinenvertaisarvioitu

TY - CHAP

T1 - Image quality assessment and outliers filtering in an image-based animal supervision system

AU - Khoramshahi, Ehsan

AU - Hietaoja, Juha

AU - Valros, Anna

AU - Yun, Jinhyeon

AU - Pastell, Matti

PY - 2017

Y1 - 2017

N2 - This paper presents a probabilistic framework for the image quality assessment (QA), and filtering of outliers, in an image-based animal supervision system (asup). The proposed framework recognizes asup's imperfect frames in two stages. The first stage deals with the similarity analysis of the same-class distributions. The objective of this stage is to maximize the separability measures by defining a set of similarity indicators (SI) under the condition that the number of permissible values for them is restricted to be relatively low. The second stage, namely faulty frame recognition (FFR), deals with asup's QA training and real-time quality assessment (RTQS). In RTQS, decisions are made based on a real-time quality assessment mechanism such that the majority of the defected frames are removed from the consecutive sub routines that calculate the movements. The underlying approach consists of a set of SI indexes employed in a simple Bayesian inference model. The results confirm that a significant amount of defected frames can be efficiently classified by this approach. The performance of the proposed technique is demonstrated by the classification on a cross-validation set of mixed high and low quality frames. The classification shows a true positive rate of 88.6% while the false negative rate is only about 2.5%. © 2017, IGI Global. All rights reserved.

AB - This paper presents a probabilistic framework for the image quality assessment (QA), and filtering of outliers, in an image-based animal supervision system (asup). The proposed framework recognizes asup's imperfect frames in two stages. The first stage deals with the similarity analysis of the same-class distributions. The objective of this stage is to maximize the separability measures by defining a set of similarity indicators (SI) under the condition that the number of permissible values for them is restricted to be relatively low. The second stage, namely faulty frame recognition (FFR), deals with asup's QA training and real-time quality assessment (RTQS). In RTQS, decisions are made based on a real-time quality assessment mechanism such that the majority of the defected frames are removed from the consecutive sub routines that calculate the movements. The underlying approach consists of a set of SI indexes employed in a simple Bayesian inference model. The results confirm that a significant amount of defected frames can be efficiently classified by this approach. The performance of the proposed technique is demonstrated by the classification on a cross-validation set of mixed high and low quality frames. The classification shows a true positive rate of 88.6% while the false negative rate is only about 2.5%. © 2017, IGI Global. All rights reserved.

KW - Animals

KW - Barium compounds

KW - Bayesian networks

KW - Inference engines

KW - Statistics, Bayesian inference model

KW - Class distributions

KW - False negative rate

KW - Image quality assessment

KW - Probabilistic framework

KW - Separability measure

KW - Similarity analysis

KW - Supervision systems, Image quality

KW - 412 Animal science, dairy science

KW - 113 Computer and information sciences

U2 - 10.4018/978-1-5225-0983-7.ch049

DO - 10.4018/978-1-5225-0983-7.ch049

M3 - Chapter

SN - 9781522509837

BT - Biometrics

PB - IGI Global

CY - Hershey, United States

ER -