Systems and methods for identifying roads in images
First Claim
Patent Images
1. A computer-implemented method comprising:
- receiving an image formed from at least one of aerial or satellite imagery data;
segmenting, via a subdivision engine, the image into at least one fragment having a medial axis and medial radiuses, wherein the medial radiuses correspond to about half the width of the at least one fragment, and wherein the segmenting is based at least in part on at least one pixel feature;
for each of the at least one fragments, computing a mean medial radius and a standard deviation for the corresponding fragment based on the corresponding medial radiuses;
determining, using a processor, a road likeness score for each of the at least one fragments based at least in part on the medial radiuses of the corresponding fragment and the computed mean medial radius of the corresponding fragment; and
identifying roads in the image based at least in part on the road likeness score.
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Abstract
Methods and systems described herein enable self-supervised road detection in images. The method includes receiving an image, segmenting the image into at least one fragment based at least in part on at least one pixel feature, determining, using a processor, a road likeness score for the at least one fragment based at least in part on a medial radius, and identifying roads based at least in part on the road likeness score.
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Citations
20 Claims
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1. A computer-implemented method comprising:
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receiving an image formed from at least one of aerial or satellite imagery data; segmenting, via a subdivision engine, the image into at least one fragment having a medial axis and medial radiuses, wherein the medial radiuses correspond to about half the width of the at least one fragment, and wherein the segmenting is based at least in part on at least one pixel feature; for each of the at least one fragments, computing a mean medial radius and a standard deviation for the corresponding fragment based on the corresponding medial radiuses; determining, using a processor, a road likeness score for each of the at least one fragments based at least in part on the medial radiuses of the corresponding fragment and the computed mean medial radius of the corresponding fragment; and identifying roads in the image based at least in part on the road likeness score. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. One or more non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to:
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receive an image formed from at least one of aerial or satellite imagery data; segment the image into at least one fragment having a medial axis and medial radiuses, wherein the medial radiuses correspond to about half the width of the at least one fragment, and wherein the image is segmented based at least in part on at least one pixel feature; for each of the at least one fragments, compute a mean medial radius and a standard deviation for the corresponding fragment based on the corresponding medial radiuses; determine a road likeness score for each of the at least one fragments based at least in part on the medial radiuses of the corresponding fragment and the computed mean medial radius of the corresponding fragment; and identify roads in the image based at least in part on the road likeness score. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A system for analyzing images, said system comprising:
a computing device comprising; a segmentation engine configured to; receive an image formed from at least one of aerial or satellite imagery data; and segment the image into at least one fragment having a medial axis and medial radiuses, wherein the medial radiuses correspond to about half the width of the at least one fragment, and wherein the image is segmented based at least in part on at least one pixel feature; and a classifier configured to; for each of the at least one fragments, computing a mean medial radius and a standard deviation for the corresponding fragment based on the corresponding medial radiuses; determine a road likeness score for each of the at least one fragments based at least in part on the medial radiuses of the corresponding fragment and the computed mean medial radius of the corresponding fragment; and identify roads in the image based at least in part on the road likeness score. - View Dependent Claims (18, 19, 20)
Specification