Geo-Relevance for Images

• US 20090092277A1
• Filed: 10/04/2007
• Published: 04/09/2009
• Est. Priority Date: 10/04/2007
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Ofek, Eyal, Epshtein, Boris
• Granted Patent: US 8,326,048 B2
• Time in Patent Office: Days
• US Class Current: 382/100
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59

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.

Claims

1. A 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 an individual image;

arranging said plurality of image frustums based on said approximate point of origin and said approximate direction to form a relevance map;

determining a first area of relevance from said relevance map; and

identifying a first group of said plurality of images that include said first area of relevance.

2. The method of claim 1, said frustums further having at least one of a group composed of:

a view angle;

a depth;

a timestamp; and

a relevance function.

3. The method of claim 2, said depth being determined by at least one of a group composed of:

computing an approximate geographical distance from said point of origin to an object;

mapping said frustum to a known geographical model; and

mapping a first portion of a first image to a second portion of a second image.

4. The method of claim 1, said relevance map having a dimension of time.

5. The method of claim 1, said relevance map having three dimensions.

6. The method of claim 1, further comprising:

selecting a representative image from said group.

7. The method of claim 6, said selecting being based on at least one of a group composed of:

overlap of said first area of relevance with said frustum;

sharpness; and

resolution.

8. The method of claim 1 further comprising:

selecting a second area of relevance from said first group of said plurality of images; and

identifying a second group of said plurality of images that include said second area of relevance.

9. A computer readable medium comprising computer executable instructions adapted to perform the method of claim 1.

10. A system comprising:

a frustum determination engine adapted to determine a frustum of an image comprising an approximate point of origin and an approximate direction;

a mapping engine adapted to map a plurality of frustums onto a relevance map; 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.

11. The system of claim 10, said frustum determination engine adapted to determine said frustum by geopositional metadata for an image.

12. The system of claim 11, said geopositional metadata being determined by at least one of a group composed of:

user input; and

a geopositional detection system.

13. The system of claim 10, said frustum further having at least one of a group of parameters composed of:

a focal point;

a view angle;

a depth;

a timestamp; and

a relevance function.

14. The system of claim 13, at least one of said parameters being determined by an image capture device.

15. The system of claim 10, said grouping engine further adapted to:

rank a plurality of images in said first group of images.

16. The system of claim 10, said grouping engine further adapted to:

select a representative image from said first group of images.

17. A method comprising:

receiving a plurality of images, said images being created by a plurality of sources;

approximating a frustum for each of said plurality of images;

determining an area of relevance based on said frustums;

selecting a first group of said plurality of images based on said area of relevance.

18. The method of claim 17, said images being individual frames of a video sequence.

19. The method of claim 17 further comprising:

applying a relevance function to each of said frustums.

20. A computer readable medium comprising computer executable instructions adapted to perform the method of claim 17.