Access Restriction

Author Predoehl, Andrew ♦ Morris, Scott ♦ Barnard, Kobus
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Aerial Image ♦ Statistical Model ♦ Recreational Trail ♦ Trail Image ♦ Off-trail Pixel ♦ Trail Length ♦ On-trail Pixel ♦ Image Likelihood Function ♦ Western Continental Usa ♦ Substantial Improvement ♦ Prior Model ♦ Novel Stochastic Variation ♦ Good Value ♦ Trail-finding Method ♦ Trail Route ♦ Posterior Distribution ♦ Dijkstra Algorithm
Abstract We present a statistical model of aerial images of recreational trails, and a method to infer trail routes in such images. We learn a set of textons describing the images, and use them to divide the image into super-pixels represented by their texton. We then learn, for each texton, the frequency of generating on-trail and off-trail pixels, and the direction of trail through on-trail pixels. From these, we derive an image likelihood function. We combine that 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 a novel stochastic variation of Dijkstra’s algorithm. Our experiments, on trail images and groundtruth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding method. (a) (b)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study