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Image Quality Assessment and Outliers Filtering in an Image-Based Animal Supervision System

Image Quality Assessment and Outliers Filtering in an Image-Based Animal Supervision System

Ehsan Khoramshahi, Juha Hietaoja, Anna Valros, Jinhyeon Yun, Matti Pastell
Copyright: © 2015 |Volume: 6 |Issue: 2 |Pages: 16
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781466678705|DOI: 10.4018/ijaeis.2015040102
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MLA

Khoramshahi, Ehsan, et al. "Image Quality Assessment and Outliers Filtering in an Image-Based Animal Supervision System." IJAEIS vol.6, no.2 2015: pp.15-30. http://doi.org/10.4018/ijaeis.2015040102

APA

Khoramshahi, E., Hietaoja, J., Valros, A., Yun, J., & Pastell, M. (2015). Image Quality Assessment and Outliers Filtering in an Image-Based Animal Supervision System. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 6(2), 15-30. http://doi.org/10.4018/ijaeis.2015040102

Chicago

Khoramshahi, Ehsan, et al. "Image Quality Assessment and Outliers Filtering in an Image-Based Animal Supervision System," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 6, no.2: 15-30. http://doi.org/10.4018/ijaeis.2015040102

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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%.

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