NDLI: A Statistical Model for Recreational Trails in Aerial Images

Content Provider	IEEE Xplore Digital Library
Author	Predoehl, A. Morris, S. Barnard, K.
Copyright Year	2013
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 text ons describing the images, and use them to divide the image into super-pixels represented by their text on. We then learn, for each text on, 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 ground truth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding method.
Starting Page	337
Ending Page	344
File Size	2284923
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.50
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proposals Computational modeling Image segmentation Vectors Data models Roads Training data shortest path statistical model superpixels GIS
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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