Geo-relevance for images
First Claim
Patent Images
1. A method performed at least in part by a computer processor, said method comprising:
- determining a plurality of image frustums having an approximate point of origin and an approximate direction, each of said image frustums being determined from data derived from each of an individual image and representing a geographic area covered by said individual image;
arranging said plurality of image frustums based on said approximate point of origin and said approximate direction relative to one another in a geographic representation to form a relevance map showing one or more densities of coverage by said plurality of image frustums;
determining a first area of relevance from said relevance map based on said one or more densities of coverage of said relevance map; and
identifying a first group of images that include said first area of relevance.
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Accused Products
Abstract
Images may be sorted and categorized by defining a frustum for each image and overlaying the frustums in two, three, or four dimensions to create a density map and identify points of interest. Images that contain a point of interest may be grouped, sorted, and categorized to determine representative images of the point. By including many images from different sources, common points of interest may be defined. Points of interest may be defined in two or three Euclidian dimensions, or may include a dimension of time.
26 Citations
20 Claims
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1. A method performed at least in part by a computer processor, said method comprising:
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determining a plurality of image frustums having an approximate point of origin and an approximate direction, each of said image frustums being determined from data derived from each of an individual image and representing a geographic area covered by said individual image; arranging said plurality of image frustums based on said approximate point of origin and said approximate direction relative to one another in a geographic representation to form a relevance map showing one or more densities of coverage by said plurality of image frustums; determining a first area of relevance from said relevance map based on said one or more densities of coverage of said relevance map; and identifying a first group of images that include said first area of relevance. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system comprising:
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a processor; a frustum determination engine operating on said processor and adapted to determine a frustum of an image, using data derived from said image, said frustum comprising an approximate point of origin and an approximate direction and representing a geographic area covered by said image; a mapping engine adapted to map a plurality of frustums relative to one another in a geographic representation onto a relevance map showing one or more densities of coverage by said plurality of mapped frustums; and a grouping engine adapted to determine a first area of relevance from said relevance map and create a first group of images that include said first area of relevance based on said one or more densities of coverage of the relevance map. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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17. A method performed at least in part by a computer processor, said method comprising:
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receiving a plurality of images, said images being created by a plurality of sources; approximating a frustum for each of said plurality of images based on data derived from each respective one of said plurality of images, said frustum representing a geographic area covered by said individual images; determining an area of relevance based on said frustums by overlapping said plurality of approximated frustums relative to one another in a geographic representation to form a relevance map; determining a density of coverage over said area of relevance on the relevance map based on a number of said plurality of frustums overlapping over said area of relevance; and selecting a first group of said plurality of images based on said area of relevance. - View Dependent Claims (18, 19, 20)
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Specification