IP Library Granted Patent US 10,127,647
Granted Patent B2
US 10,127,647 · App. 15/130,851 · Granted Nov 13, 2018

Methods and systems for detecting cracks in electronic devices

Inventors: Babak Forutanpour (San Diego, CA); Jeffrey Ploetner (San Diego, CA)
Assignee: ecoATM, LLC
G06T7/0004G01N21/88G06T7/0085G06T7/13G06T7/60G06T2207/20061G06T2207/30108
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Quick Facts
Patent No.
US 10,127,647
App. No.
15/130,851
Granted
Nov 13, 2018
Kind
B2
Abstract

Systems and methods for detecting cracks in an electronic device are disclosed. In one embodiment, the method includes receiving an image of a front side of a mobile device and automatically identifying edges in the image. For given edges among the identified edges, the method includes determining whether another edge among the identified edges is present within a predetermined distance of the given edge. Next, straight line segments corresponding to the edges for which another edge is within the predetermined distance are identified, and then a crack evaluation assessment is assigned to the mobile device based at least in part on the identified straight line segments.

Claims (56)

1. A method performed by one or more computing devices for detecting cracks in an electronic device, the method comprising:

receiving an image of an electronic device;

automatically identifying a plurality of edges in the image;

for individual edges among the identified edges, determining whether another edge among the identified edges is present within a predetermined distance of each of the individual edges;

automatically identifying straight line segments corresponding to the individual edges for which another edge is within the predetermined distance; and

evaluating a condition of the electronic device based at least in part on the number of identified straight line segments.

2. The method of claim 1 wherein:

automatically identifying the straight line segments includes applying a Hough line transform to the edges for which another edge is within the predetermined distance, and

evaluating a condition of the electronic device includes assigning the crack evaluation assessment based at least in part on a result of applying the Hough line transform.

3. The method of claim 1 wherein determining, respectively, whether another edge among the identified edges is present within the predetermined distance comprises:

for a given point along an individual edge among the identified edges:

determining whether another edge among the identified edges is present within a first predetermined distance along a first axis; and

determining whether another edge among the identified edges is present within a second predetermined distance along a second axis orthogonal to the first axis.

4. The method of claim 1 wherein automatically identifying the edges comprises applying a Canny edge detector to the image.

5. The method of claim 1 wherein the image is of a screen and a non-screen portion of the electronic device, and wherein the method further comprises applying a first filter to the screen portion and applying a second filter to the non-screen portion before automatically identifying the edges.

6. The method of claim 1 wherein the predetermined distance is not more than about 1.5 mm.

7. The method of claim 1 wherein evaluating a condition of the electronic device comprises calculating a number of pixels in the image that are included in the identified straight line segments.

8. The method of claim 1 , further comprising determining a value for the electronic device based on the evaluated condition of the electronic device.

9. A method performed by one or more computing devices for detecting cracks in an electronic device, the method comprising:

receiving an image of an electronic device;

automatically identifying a plurality of edges in the image;

automatically determining whether individual edges among the identified edges are within a pre-selected proximity to other individual edges among the identified edges; and

assessing the condition of the electronic device based at least in part on the number of the edges within the pre-selected proximity to other edges.

10. The method of claim 9 wherein, in assessing the condition of the electronic device, the edges not within the pre-selected proximity to other edges have no weight.

11. The method of claim 9 wherein automatically determining whether individual edges among the identified edges are within a pre-selected proximity to other individual edges among the identified edges comprises:

for an individual edge among the identified edges:

determining whether another edge among the identified edges is present within a first predetermined distance along a first axis;

determining whether another edge among the identified edges is present within a second predetermined distance along a second axis orthogonal to the first axis; and

if another edge among the identified edges is present within the first predetermined distance along the first axis or within the second predetermined distance along the second axis, then indicating that the individual edge is within the pre-selected proximity to the other edge.

12. The method of claim 11 wherein the first predetermined distance is not more than about 1.5 mm.

13. The method of claim 9 wherein assessing the electronic device comprises calculating a number of pixels in the image that are included in the individual edges that are within the pre-selected proximity to other individual edges.

14. A method performed by one or more computing devices for detecting cracks in an electronic device, the method comprising:

receiving an image of an electronic device;

automatically identifying one or more edges in the image;

identifying edge-to-non-edge-to-edge groupings among the identified edges; and

assessing the condition of the electronic device based at least in part on the identified edge-to-non-edge-to-edge groupings, wherein, in the assessment, the edges in the identified edge-to-non-edge-to-edge groupings have greater weight than other edges among the identified edges.

15. The method of claim 14 wherein identifying edge-to-non-edge-to-edge groupings comprises:

for an individual edge among the identified edges:

determining whether another edge among the identified edges is present within a first predetermined distance along a first axis; and

determining whether another edge among the identified edges is present within a second predetermined distance along a second axis orthogonal to the first axis.

16. The method of claim 14 , wherein assessing the condition of the electronic device comprises calculating a number of pixels in the image that are included in the identified edge-to-non-edge-to-edge groupings.

17. A computer-readable memory carrying computer-executable instructions for causing one or more processors to facilitate detecting cracks in an electronic device, the computer-executable instructions comprising instructions that, when executed by the one or more processors:

receive an image of an electronic device;

automatically identify edges in the image;

for individual edges among the identified edges, determine whether another edge among the identified edges is present within a predetermined distance of the individual edge;

automatically identify straight line segments corresponding to the edges for which another edge is within the predetermined distance; and

evaluate a condition of the electronic device based at least in part on the identified straight line segments.

18. The computer-readable memory of claim 17 wherein the computer-executable instructions, when executed by the one or more processors:

automatically identify the straight line segments at least in part by applying a Hough line transform to the edges for which another edge is within the predetermined distance; and

evaluate the condition of the electronic device at least in part by assigning a crack evaluation assessment based at least in part on a result of applying the Hough line transform.

19. The computer-readable memory of claim 17 wherein the computer-executable instructions, when executed by the one or more processors, determine, respectively, whether another edge among the identified edges is present within the predetermined distance of an individual edge among the identified edges at least in part by:

for a given point along the individual edge:

determining whether another edge among the identified edges is present within a first predetermined distance along a first axis; and

determining whether another edge among the identified edges is present within a second predetermined distance along a second axis orthogonal to the first axis.

20. The computer-readable memory of claim 17 wherein the computer-executable instructions, when executed by the one or more processors, automatically identify the straight line segments at least in part by applying a Canny edge detector to the image.

21. The computer-readable memory of claim 17 wherein the computer-executable instructions, when executed by the one or more processors, assign the crack evaluation assessment based at least in part on a number of pixels in the image that are included in the identified straight line segments.

Assignments (8)
SECURITY AGREEMENT Recorded Jan 8, 2020
From: ECOATM, LLC
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 051504/0383 →
RELEASE OF SECURITY INTEREST Recorded Jan 7, 2020
From: SILICON VALLEY BANK
To: ECOATM, LLC
Reel/Frame 051431/0022 →
RELEASE OF SECURITY INTEREST Recorded Jan 7, 2020
From: SILICON VALLEY BANK
To: ECOATM, LLC
Reel/Frame 051431/0522 →
SECURITY INTEREST Recorded Jan 4, 2019
From: ECOATM, LLC
To: SILICON VALLEY BANK
Reel/Frame 047901/0518 →
CHANGE OF NAME Recorded Sep 19, 2018
From: ECOATM, INC.
To: ECOATM, LLC
Reel/Frame 047109/0432 →
SECURITY INTEREST Recorded Mar 8, 2017
From: ECOATM, LLC
To: SILICON VALLEY BANK
Reel/Frame 041505/0967 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 22, 2016
From: FORUTANPOUR, BABAK; PLOETNER, JEFFREY
To: ECOATM, INC.
Reel/Frame 038672/0320 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 22, 2016
From: FORUTANPOUR, BABAK; PLOETNER, JEFFREY
To: ECOATM, INC.
Reel/Frame 038672/0303 →
Continuity (1)
Related Publication 20170301078A1 · Oct 19, 2017
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