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1/05/2013SoHuman 2013 atACM Web Science 2013 1Exploiting User Generated Content forMountain Peak DetectionRoman Fedrov, Da...
2Passive Human ComputationUser EffortSoHuman 2013 atACM Web Science 20131/05/2013
3Passive Human ComputationCollective IntelligenceSoHuman 2013 atACM Web Science 20131/05/2013Our GoalRetrieve the collecti...
4Key Problem: Object IdentificationInputSoHuman 2013 atACM Web Science 20131/05/201346° 0’ 48.51” N7° 48’ 6.62” Ehttp://ww...
5Key Problem: Object IdentificationOutputSoHuman 2013 atACM Web Science 20131/05/2013
6Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatch
7Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatch
8Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatch
9Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatch
10Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatchAdapted from ...
11ResultsSoHuman 2013 atACM Web Science 20131/05/2013 62% of correct match as maximum result 81% of correct match in top...
12Potential ApplicationsSoHuman 2013 atACM Web Science 20131/05/2013http://www.nohrsc.noaa.gov/Augmented RealityEnvironmen...
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Exploiting User Generated Content for Mountain Peak Detection

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CUbRIK research used for the classification of mountain panoramas from user-generated photographs followed by identification and extraction of mountain peaks from those panoramas.

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Transcript of "Exploiting User Generated Content for Mountain Peak Detection"

  1. 1. 1/05/2013SoHuman 2013 atACM Web Science 2013 1Exploiting User Generated Content forMountain Peak DetectionRoman Fedrov, Davide Martinenghi,Marco Tagliasacchi, Andrea Castelletti
  2. 2. 2Passive Human ComputationUser EffortSoHuman 2013 atACM Web Science 20131/05/2013
  3. 3. 3Passive Human ComputationCollective IntelligenceSoHuman 2013 atACM Web Science 20131/05/2013Our GoalRetrieve the collections of the mountainappearances in different time instants and buildenvironmental models.201320122011201020092008Key ProblemIdentify the mountains
  4. 4. 4Key Problem: Object IdentificationInputSoHuman 2013 atACM Web Science 20131/05/201346° 0’ 48.51” N7° 48’ 6.62” Ehttp://www.udeuschle.de/Panoramen.html
  5. 5. 5Key Problem: Object IdentificationOutputSoHuman 2013 atACM Web Science 20131/05/2013
  6. 6. 6Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatch
  7. 7. 7Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatch
  8. 8. 8Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatch
  9. 9. 9Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatch
  10. 10. 10Matching AlgorithmSoHuman 2013 atACM Web Science 20131/05/2013StepsScaleRenderExtract EdgesFilterMatchAdapted from matching technique by Baboud et al. “Automatic photo-to-terrain alignment forthe annotation of mountain pictures”, CVPR 2011
  11. 11. 11ResultsSoHuman 2013 atACM Web Science 20131/05/2013 62% of correct match as maximum result 81% of correct match in top-10 positions
  12. 12. 12Potential ApplicationsSoHuman 2013 atACM Web Science 20131/05/2013http://www.nohrsc.noaa.gov/Augmented RealityEnvironmental ModelsColle dei BreuilFurgggrat
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