Evaluation of Random Forests for Detection and Localization of Cattle Eyes
In a time when cattle herds grow continually larger the need for automatic methods to detect diseases is ever increasing. One possible method to discover diseases is to use thermal images and automatic head and eye detectors. In this thesis an eye detector and a head detector is implemented using th...
Main Author: | |
---|---|
Format: | Others |
Language: | English |
Published: |
Linköpings universitet, Datorseende
2015
|
Subjects: | |
Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540 |
id |
ndltd-UPSALLA1-oai-DiVA.org-liu-121540 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-UPSALLA1-oai-DiVA.org-liu-1215402015-09-30T04:32:47ZEvaluation of Random Forests for Detection and Localization of Cattle EyesengSandsveden, DanielLinköpings universitet, DatorseendeLinköpings universitet, Tekniska fakulteten2015Random ForestsHOGLBPSVMDescriptorClassifierIn a time when cattle herds grow continually larger the need for automatic methods to detect diseases is ever increasing. One possible method to discover diseases is to use thermal images and automatic head and eye detectors. In this thesis an eye detector and a head detector is implemented using the Random Forests classifier. During the implementation the classifier is evaluated using three different descriptors: Histogram of Oriented Gradients, Local Binary Patterns, and a descriptor based on pixel differences. An alternative classifier, the Support Vector Machine, is also evaluated for comparison against Random Forests. The thesis results show that Histogram of Oriented Gradients performs well as a description of cattle heads, while Local Binary Patterns performs well as a description of cattle eyes. The provided descriptor performs almost equally well in both cases. The results also show that Random Forests performs approximately as good as the Support Vector Machine, when the Support Vector Machine is paired with Local Binary Patterns for both heads and eyes. Finally the thesis results indicate that it is easier to detect and locate cattle heads than it is to detect and locate cattle eyes. For eyes, combining a head detector and an eye detector is shown to give a better result than only using an eye detector. In this combination heads are first detected in images, followed by using the eye detector in areas classified as heads. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540application/pdfinfo:eu-repo/semantics/openAccess |
collection |
NDLTD |
language |
English |
format |
Others
|
sources |
NDLTD |
topic |
Random Forests HOG LBP SVM Descriptor Classifier |
spellingShingle |
Random Forests HOG LBP SVM Descriptor Classifier Sandsveden, Daniel Evaluation of Random Forests for Detection and Localization of Cattle Eyes |
description |
In a time when cattle herds grow continually larger the need for automatic methods to detect diseases is ever increasing. One possible method to discover diseases is to use thermal images and automatic head and eye detectors. In this thesis an eye detector and a head detector is implemented using the Random Forests classifier. During the implementation the classifier is evaluated using three different descriptors: Histogram of Oriented Gradients, Local Binary Patterns, and a descriptor based on pixel differences. An alternative classifier, the Support Vector Machine, is also evaluated for comparison against Random Forests. The thesis results show that Histogram of Oriented Gradients performs well as a description of cattle heads, while Local Binary Patterns performs well as a description of cattle eyes. The provided descriptor performs almost equally well in both cases. The results also show that Random Forests performs approximately as good as the Support Vector Machine, when the Support Vector Machine is paired with Local Binary Patterns for both heads and eyes. Finally the thesis results indicate that it is easier to detect and locate cattle heads than it is to detect and locate cattle eyes. For eyes, combining a head detector and an eye detector is shown to give a better result than only using an eye detector. In this combination heads are first detected in images, followed by using the eye detector in areas classified as heads. |
author |
Sandsveden, Daniel |
author_facet |
Sandsveden, Daniel |
author_sort |
Sandsveden, Daniel |
title |
Evaluation of Random Forests for Detection and Localization of Cattle Eyes |
title_short |
Evaluation of Random Forests for Detection and Localization of Cattle Eyes |
title_full |
Evaluation of Random Forests for Detection and Localization of Cattle Eyes |
title_fullStr |
Evaluation of Random Forests for Detection and Localization of Cattle Eyes |
title_full_unstemmed |
Evaluation of Random Forests for Detection and Localization of Cattle Eyes |
title_sort |
evaluation of random forests for detection and localization of cattle eyes |
publisher |
Linköpings universitet, Datorseende |
publishDate |
2015 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540 |
work_keys_str_mv |
AT sandsvedendaniel evaluationofrandomforestsfordetectionandlocalizationofcattleeyes |
_version_ |
1716824897449099264 |