Persistent Link:
http://hdl.handle.net/10150/612599
Title:
A Statistical Model of Recreational Trails
Author:
Predoehl, Andrew
Issue Date:
2016
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
We present a statistical model of recreational trails, and a method to infer trail routes from geophysical data, namely aerial imagery and terrain elevation. We learn a set of textures (textons) that characterize the imagery, and use the textons to segment each image into super-pixels. We also model each texton's probability of generating trail pixels, and the direction of such trails. From terrain elevation, we model the magnitude and direction of terrain gradient on-trail and off-trail. These models lead to a likelihood function for image and elevation. Consistent with Bayesian reasoning, we combine the likelihood with a prior model of trail length and smoothness, yielding a posterior distribution for trails, given an image. We search for good values of this posterior using both a novel stochastic variation of Dijkstra's algorithm, and an MCMC-inspired sampler. Our experiments, on trail images and groundtruth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding methods.
Type:
text; Electronic Dissertation
Keywords:
Bayesian models; Computer vision; Digital elevation models; Generative models; Image processing; Computer Science; Aerial imagery
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Computer Science
Degree Grantor:
University of Arizona
Advisor:
Barnard, Kobus

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleA Statistical Model of Recreational Trailsen_US
dc.creatorPredoehl, Andrewen
dc.contributor.authorPredoehl, Andrewen
dc.date.issued2016-
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractWe present a statistical model of recreational trails, and a method to infer trail routes from geophysical data, namely aerial imagery and terrain elevation. We learn a set of textures (textons) that characterize the imagery, and use the textons to segment each image into super-pixels. We also model each texton's probability of generating trail pixels, and the direction of such trails. From terrain elevation, we model the magnitude and direction of terrain gradient on-trail and off-trail. These models lead to a likelihood function for image and elevation. Consistent with Bayesian reasoning, we combine the likelihood with a prior model of trail length and smoothness, yielding a posterior distribution for trails, given an image. We search for good values of this posterior using both a novel stochastic variation of Dijkstra's algorithm, and an MCMC-inspired sampler. Our experiments, on trail images and groundtruth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding methods.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectBayesian modelsen
dc.subjectComputer visionen
dc.subjectDigital elevation modelsen
dc.subjectGenerative modelsen
dc.subjectImage processingen
dc.subjectComputer Scienceen
dc.subjectAerial imageryen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorBarnard, Kobusen
dc.contributor.committeememberEfrat, Alonen
dc.contributor.committeememberKececioglu, Johnen
dc.contributor.committeememberMorrison, Claytonen
dc.contributor.committeememberBarnard, Kobusen
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