System and method for automatically measuring the dimensions of and identifying the type of exterior siding

• US 9,830,696 B1
• Filed: 11/15/2016
• Issued: 11/28/2017
• Est. Priority Date: 03/28/2014
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Maurer, Jonathan D., Christopulos, Nicholas U.
• Primary Examiner(s): Hung, Yubin
• Application Number:
  US15/351,549
• Time in Patent Office: 378 Days
• Field of Search: 348137
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 2207/20048 Transform domain processing
• CPC Class Codes: G06T 2207/20061 Hough transform
• CPC Class Codes: G06T 2207/30108 Industrial image inspection
• CPC Class Codes: G06T 7/001 using an image reference approach
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06T 7/60 Analysis of geometric attributes
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06V 10/245 by locating a pattern; Special marks for positioning

Abstract

Methods, systems, and computer readable media are disclosed for determining a pixel-to-length ratio between a number of pixels disposed over a predetermined length of a reference object within an image of a siding sample and the predetermined length of the reference object. A first and second distance between respective first and second pairs of points within the image corresponding to respective first and second length measurements of the siding sample are determined, as well as a first and second number of pixels disposed between the first and second pair of points, respectively. Furthermore, the method, system, and computer readable medium disclose determining the first length measurement based on the pixel-to-length ratio and the first number of pixels, determining the second length measurement based on the pixel-to-length ratio and the second number of pixels, and identifying a siding product associated with the first and second length measurements.

Claims

1. A computing device, comprising:

a memory configured to store computer-readable instructions; and

a processor configured to execute the computer-readable instructions stored in the memory to;

selectively align edges of a reference object included within an image of a two-dimensional object with edges of the two-dimensional object to provide an aligned reference object;

calculate a first dimension of the two-dimensional object based upon a pixel dimension of the aligned reference object and a first number of pixels spanned by the first dimension of the two-dimensional object within the image; and

identify a product associated with the first dimension of the two-dimensional object.

2. The computing device of claim 1, wherein the processor is further configured to determine that the reference object needs to be aligned when the reference object is not aligned parallel with a portion of the two-dimensional object within the image.

3. The computing device of claim 1, wherein the processor is further configured to rectify the image of the two-dimensional object so the perspective of the two-dimensional object faces forward if the reference object includes perspective distortion.

4. The computing device of claim 1, wherein the processor is further configured to measure a second dimension of the two-dimensional object based upon the pixel dimension of the aligned reference object and a second number of pixels spanned by the second dimension of the two-dimensional object within the image, and to identify the product based upon the first and second dimensions of the two-dimensional object.

5. The computing device of claim 4, wherein:

the two-dimensional object is a siding sample,

the first dimension of the two-dimensional object corresponds to a face dimension of the siding sample,

the second dimension of the two-dimensional object corresponds to a profile dimension of the siding sample, and

the processor is further configured to identify the product as a siding product stored in a siding product reference database having a face dimension and a profile dimension matching the first dimension and the second dimension of the siding sample, respectively.

6. The computing device of claim 1, wherein the processor is further configured to detect the reference object within the image using at least one of:

a Scale-Invariant Feature Transform (SIFT);

a Speeded Up Robust Features (SURF);

a Radon transform;

or

a Hough transform.

7. A product identification engine, comprising:

a memory configured to store computer-readable instructions; and

a processor configured to execute the computer-readable instructions stored in the memory and to identify a siding product by;

selectively aligning edges of a reference object included within an image of a two-dimensional object received from a mobile computing device with edges of the two-dimensional object to provide an aligned reference object;

calculating a first dimension of the two-dimensional object based upon a pixel dimension of the aligned reference object and a first number of pixels spanned by the first dimension of the two-dimensional object within the image; and

identifying a product associated with the first dimension of the two-dimensional object.

8. The product identification engine of claim 7, wherein the processor is further configured to cause the product identification engine to wirelessly communicate the identified product associated with the first dimension to the mobile computing device.

9. The product identification engine of claim 7, wherein the processor is further configured to determine that the reference object needs to be aligned when the reference object is not aligned parallel with a portion of the two-dimensional object within the image.

10. The product identification engine of claim 7, wherein the processor is further configured to rectify the image of the two-dimensional object so the perspective of the two-dimensional object faces forward if the reference object includes perspective distortion.

11. The product identification engine of claim 7, wherein the processor is further configured to measure a second dimension of the two-dimensional object based upon the pixel dimension of the aligned reference object and a second number of pixels spanned by the second dimension of the two-dimensional object within the image, and to identify the product based upon the first and second dimensions of the two-dimensional object.

12. The product identification engine of claim 11, wherein:

the two-dimensional object is a siding sample,

the first dimension of the two-dimensional object corresponds to a face dimension of the siding sample,

the second dimension of the two-dimensional object corresponds to a profile dimension of the siding sample, and

the processor is further configured to identify the product as a siding product stored in a siding product reference database having a face dimension and a profile dimension matching the first dimension and the second dimension of the siding sample, respectively.

13. The product identification engine of claim 7, wherein the processor is further configured to detect the reference object within the image using at least one of:

a Scale-Invariant Feature Transform (SIFT);

a Speeded Up Robust Features (SURF);

a Radon transform;

or

a Hough transform.

14. A system, comprising:

a mobile computing device configured to transmit an image of a two-dimensional object that includes a reference object within the image; and

an external computing device configured to;

selectively align edges of a reference object included within the image with edges of the two-dimensional object to provide an aligned reference object;

calculate a first dimension of the two-dimensional object based upon a pixel dimension of the aligned reference object and a first number of pixels spanned by the first dimension of the two-dimensional object within the image; and

identify a product associated with the first dimension of the two-dimensional object.

15. The system of claim 14, wherein the external computing device is further configured to wirelessly transmit the identified product to the mobile computing device.

16. The system of claim 14, wherein the external computing device is further configured to determine that the reference object needs to be aligned when the reference object is not aligned parallel with a portion of the two-dimensional object within the image.

17. The system of claim 14, wherein the external computing device is further configured to rectify the image of the two-dimensional object so the perspective of the two-dimensional object faces forward if the reference object includes perspective distortion.

18. The system of claim 14, wherein the external computing device is further configured to measure a second dimension of the two-dimensional object based upon the pixel dimension of the aligned reference object and a second number of pixels spanned by the second dimension of the two-dimensional object within the image, and to identify the product based upon the first and second dimensions of the two-dimensional object.

19. The system of claim 18, wherein:

the two-dimensional object is a siding sample,

the first dimension of the two-dimensional object corresponds to a face dimension of the siding sample,

the second dimension of the two-dimensional object corresponds to a profile dimension of the siding sample, and

the external computing device is further configured to identify the product as a siding product stored in a siding product reference database having a face dimension and a profile dimension matching the first dimension and the second dimension of the siding sample, respectively.

20. The system of claim 14, wherein the external computing device is further configured to detect the reference object within the image using at least one of:

a Scale-Invariant Feature Transform (SIFT);

a Speeded Up Robust Features (SURF);

a Radon transform;

or

a Hough transform.