2022
DOI: 10.3390/s22166052
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Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

Abstract: As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horse… Show more

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Cited by 4 publications
(10 citation statements)
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“…In the current study, the ROIs annotated on the incisor teeth pass the criterion of being a small size image, thus, one may suspect DispEn2D and DistEn2D rather than SampEn2D, FuzzEn2D, and PermEn2D to be effective in this type of application, especially considering that both DispEn2D and DistEn2D have been shown to be the most suitable for the entropy–based texture analysis of small ROIs which were extracted from thermographs in the equine applications of pregnancy [ 27 ] and back load [ 28 ] detection. Interestingly, in the current study, only DistEn2D, not DispEn2D, demonstrated the most favourable EOTRH grade–related differences.…”
Section: Discussionmentioning
confidence: 99%
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“…In the current study, the ROIs annotated on the incisor teeth pass the criterion of being a small size image, thus, one may suspect DispEn2D and DistEn2D rather than SampEn2D, FuzzEn2D, and PermEn2D to be effective in this type of application, especially considering that both DispEn2D and DistEn2D have been shown to be the most suitable for the entropy–based texture analysis of small ROIs which were extracted from thermographs in the equine applications of pregnancy [ 27 ] and back load [ 28 ] detection. Interestingly, in the current study, only DistEn2D, not DispEn2D, demonstrated the most favourable EOTRH grade–related differences.…”
Section: Discussionmentioning
confidence: 99%
“…For the image filtering, three filtering algorithms were chosen: Normalize, Median, and Laplacian Sharpening based on the previously described findings of equine incisor teeth radiographs evaluation [ 17 ]. For the image texture analysis, five entropy–based texture measures were considered: two–dimensional sample entropy (SampEn2D), two–dimensional fuzzy entropy (FuzzEn2D), two–dimensional permutation entropy (PermEn2D), two–dimensional dispersion entropy (DispEn2D), and two–dimensional distribution entropy (DistEn2D), based on the previously described applicability in the equine image evaluation [ 27 , 28 ]. Both processing steps were conducted one after the other for the annotated ROIs, so that for each ROI, fifteen filtering–entropy combinations were returned.…”
Section: Methodsmentioning
confidence: 99%
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