Shape detection

• US 10,657,421 B2
• Filed: 12/14/2018
• Issued: 05/19/2020
• Est. Priority Date: 05/24/2016
• Status: Active Grant

• Current Assignee: Idemia Identity & Security USA, LLC (Advent International Corporation)
• Original Assignee: Morphotrust USA Incorporated (Advent International Corporation)
• Inventors: Martin, Brian K.
• Primary Examiner(s): Kassa, Yosef
• Application Number:
  US16/220,135
• Publication Number:
  US 20190220706A1
• Time in Patent Office: 522 Days
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06V 10/247 by affine transforms, e.g. correction due to perspective effects; Quadrilaterals, e.g. trapezoids
• CPC Class Codes: G06V 10/44 Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
• CPC Class Codes: G06V 30/414 Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for shape detection are disclosed. In one aspect, a method includes the actions of generating a shape model that includes a predetermined shape with a predetermined scale and predetermined orientation. The actions further include receiving an image. The actions further include identifying edges that are parallel to the side of the shape model and that are a predetermined distance from the side of the shape model. The actions further include selecting a plurality of edges that likely correspond to edges of a shape that is similar to the shape model. The actions further include determining a fit score between the plurality of edges and each shape of a plurality of shapes that are similar to the shape model. The actions further include identifying a particular shape in the image that most closely fits the shape model.

Claims

1. A computer-implemented method comprising:

receiving, by a computing device, an image;

generating, by the computing device, a shape of an object to detect in the image;

identifying, by the computing device and in the image, edges that are parallel to a side of the shape and that are within a threshold distance of the side of the shape;

determining, by the computing device, a contrast of each edge;

based on the contrast of each edge, selecting, by the computing device and from among the edges, a plurality of edges;

determining, by the computing device, a plurality of candidate objects that are defined by the plurality of edges;

comparing, by the computing device, each candidate object of the plurality of objects to the shape of the object to detect in the image; and

based on comparing each candidate object of the plurality of objects to the shape of the object to detect in the image, selecting, by the computing device, a candidate object as the object to detect in the image.

2. The method of claim 1, wherein the object and the selected object have a same number of sides.

3. The method of claim 1, wherein the threshold distance from the side of the shape is a predetermined number of pixels.

4. The method of claim 1, wherein selecting the candidate object as the object to detect in the image comprises determining that the candidate object most closely fits the shape of the object.

5. The method of claim 1, wherein identifying edges that are parallel to a side of the shape and that are within a threshold distance of the side of the shape comprises identifying the edges at predetermined pixel intervals.

6. The method of claim 1, wherein the shape of the object to detect in the image has straight edges.

7. The method of claim 1, wherein selecting the plurality of edges comprises selecting the plurality of edges that have a largest gradient among the edges.

8. A system comprising:

one or more computers; and

one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising;

receiving, by a computing device, an image;

generating, by the computing device, a shape of an object to detect in the image;

identifying, by the computing device and in the image, edges that are parallel to a side of the shape and that are within a threshold distance of the side of the shape;

determining, by the computing device, a contrast of each edge;

based on the contrast of each edge, selecting, by the computing device and from among the edges, a plurality of edges;

determining, by the computing device, a plurality of candidate objects that are defined by the plurality of edges;

comparing, by the computing device, each candidate object of the plurality of objects to the shape of the object to detect in the image; and

based on comparing each candidate object of the plurality of objects to the shape of the object to detect in the image, selecting, by the computing device, a candidate object as the object to detect in the image.

9. The system of claim 8, wherein the object and the selected object have a same number of sides.

10. The system of claim 8, wherein the threshold distance from the side of the shape is a predetermined number of pixels.

11. The system of claim 8, wherein selecting the candidate object as the object to detect in the image comprises determining that the candidate object most closely fits the shape of the object.

12. The system of claim 8, wherein identifying edges that are parallel to a side of the shape and that are within a threshold distance of the side of the shape comprises identifying the edges at predetermined pixel intervals.

13. The system of claim 8, wherein the shape of the object to detect in the image has straight edges.

14. The system of claim 8, wherein selecting the plurality of edges comprises selecting the plurality of edges that have a largest gradient among the edges.

15. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:

receiving, by a computing device, an image;

generating, by the computing device, a shape of an object to detect in the image;

identifying, by the computing device and in the image, edges that are parallel to a side of the shape and that are within a threshold distance of the side of the shape;

determining, by the computing device, a contrast of each edge;

based on the contrast of each edge, selecting, by the computing device and from among the edges, a plurality of edges;

determining, by the computing device, a plurality of candidate objects that are defined by the plurality of edges;

comparing, by the computing device, each candidate object of the plurality of objects to the shape of the object to detect in the image; and

based on comparing each candidate object of the plurality of objects to the shape of the object to detect in the image, selecting, by the computing device, a candidate object as the object to detect in the image.

16. The medium of claim 15, wherein the object and the selected object have a same number of sides.

17. The medium of claim 15, wherein the threshold distance from the side of the shape is a predetermined number of pixels.

18. The medium of claim 15, wherein selecting the candidate object as the object to detect in the image comprises determining that the candidate object most closely fits the shape of the object.

19. The medium of claim 15, wherein identifying edges that are parallel to a side of the shape and that are within a threshold distance of the side of the shape comprises identifying the edges at predetermined pixel intervals.

20. The medium of claim 15, wherein the shape of the object to detect in the image has straight edges.

21. The system of claim 15, wherein selecting the plurality of edges comprises selecting the plurality of edges that have a largest gradient among the edges.