liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evaluation of Random Forests for Detection and Localization of Cattle Eyes
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2015. , 70 p.
Keyword [en]
Random Forests, HOG, LBP, SVM, Descriptor, Classifier
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-121540ISRN: LiTH-ISY-EX--15/4885--SEOAI: oai:DiVA.org:liu-121540DiVA: diva2:856339
External cooperation
Agricam
Subject / course
Computer Vision Laboratory
Presentation
2015-09-08, 16:15 (Swedish)
Supervisors
Examiners
Available from: 2015-09-29 Created: 2015-09-23 Last updated: 2015-09-29Bibliographically approved

Open Access in DiVA

fulltext(13405 kB)139 downloads
File information
File name FULLTEXT01.pdfFile size 13405 kBChecksum SHA-512
55aa49e3999b3d2cfe5061772cf7f180d85b55b9f536f2723a737f1bf67ffc5da73e256a36d353bb9d42a1f379279504654077de7a5f6a681f24c87b16c84505
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Sandsveden, Daniel
By organisation
Computer VisionFaculty of Science & Engineering
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 139 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 998 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf