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Detection of Trees Based on Quality Guided Image Segmentation
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Robotics)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Robotics)
2014 (English)In: Second International Conference on Robotics and associated High-technologies and Equipment for Agriculture and forestry (RHEA-2014): New trends in mobile robotics, perception and actuation for agriculture and forestry / [ed] Pablo Gonzalez-de-Santos and Angela Ribeiro, 2014, 531-540 p.Conference paper, Published paper (Refereed)
Abstract [en]

Detection of objects is crucial for any autonomous field robot orvehicle. Typically, object detection is used to avoid collisions whennavigating, but detection capability is essential also for autonomous or semiautonomousobject manipulation such as automatic gripping of logs withharvester cranes used in forestry. In the EU financed project CROPS,special focus is given to detection of trees, bushes, humans, and rocks inforest environments. In this paper we address the specific problem ofidentifying trees using color images. A presented method combinesalgorithms for seed point generation and segmentation similar to regiongrowing. Both algorithms are tailored by heuristics for the specific task oftree detection. Seed points are generated by scanning a verticallycompressed hue matrix for outliers. Each one of these seed points is thenused to segment the entire image into segments with pixels similar to asmall surrounding around the seed point. All generated segments are refinedby a series of morphological operations, taking into account thepredominantly vertical nature of trees. The refined segments are evaluatedby a heuristically designed quality function. For each seed point, thesegment with the highest quality is selected among all segments that coverthe seed point. The set of all selected segments constitute the identified treeobjects in the image. The method was evaluated with images containing intotal 197 trees, collected in forest environments in northern Sweden. In thispreliminary evaluation, precision in detection was 81% and recall rate 87%.

Place, publisher, year, edition, pages
2014. 531-540 p.
Keyword [en]
Seed point, Image segmentation, Region growing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:umu:diva-93290ISBN: 978-84-697-0248-2 (print)OAI: oai:DiVA.org:umu-93290DiVA: diva2:747118
Conference
Second International Conference on Robotics and associated High-technologies and Equipment for Agriculture and forestry (RHEA-2014)
Funder
EU, FP7, Seventh Framework Programme, 246252
Available from: 2014-09-15 Created: 2014-09-15 Last updated: 2014-09-17

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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