Machine vision calibration targets and methods of determining their location and orientation in an image

• US 6,137,893 A
• Filed: 10/07/1996
• Issued: 10/24/2000
• Est. Priority Date: 10/07/1996
• Status: Expired due to Term

• Current Assignee: Cognex Corporation
• Original Assignee: Cognex Corporation
• Inventors: Michael, David, Wallack, Aaron
• Primary Examiner(s): Chang, Jon
• Assistant Examiner(s): Patel, Jayanti K.
• Application Number:
  US08/726,521
• Time in Patent Office: 1,478 Days
• Field of Search: 382/206, 382/124, 382/290, 382/281, 382/108, 382/141, 382/143, 382/144, 382/145, 382/147, 382/148, 382/149, 382/151, 382/152, 382/199, 382/216, 382/266, 382/274, 382/282, 356/363, 395/94, 348/87
• US Class Current: 382/103
• CPC Class Codes: G06T 7/80 Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration

Abstract

A machine vision method analyzes a calibration target of the type having two or more regions, each having a "imageable characteristic," e.g., a different color, contrast, or brightness, from its neighboring region(s). Each region has at least two edges--referred to as "adjoining edges"--that are linear and that are directed toward and, optionally meet at, a reference point (e.g., the center of the target or some other location of interest). The method includes generating an image of the target, identifying in the image features corresponding to the adjoining edges, fitting lines to those edges, and determining the orientation and/or position of the target from those lines.

Claims

1. A machine vision method of determining a location of an object, the method comprising the steps of

A. placing a calibration target on the object, the target comprising

i. two or more regions, each region being defined by at least two linear edges that are directed toward a reference point;

ii. at least one of the regions having a different image able characteristic from an adjacent region;

B. generating an image of at least the calibration target;

C. identifying edges in the image corresponding to lines in the calibration target;

D. determining a location where lines fitted to those edges intersect.

9. A method according to claim 1, comprising the steps of

identifying edges in the image corresponding to lines in the calibration target;

and

determining an orientation of the target based on the angle of those identified edges.

10. A machine vision according to claim 1, wherein

step (C) includes the step of applying a Hough line vision tool to identify edges in the image corresponding to lines in the calibration target;

and

the method further including the step of determining an orientation of the target based on the angle of those identified edges.

11. A machine vision according to claim 1, comprising

A. applying a Sobel edge tool to the image to generate at least a Sobel angle image;

and

B. taking an angle histogram of the Sobel angle image to determine an orientation of the target.

15. A method according to any of claims 1 or 12, wherein step (B) includes the step of generating an image of a target in which each region has any of a different color, contrast, brightness and stippling from regions adjacent thereto.

16. A method according to claim 15, for use with a machine vision system having image capture means for capturing an image of the target, wherein each region has a different characteristic in such an image than the regions adjacent thereto.

17. A method according to any of claims 1 or 12, wherein step (B) includes the step of generating an image of a target for which the reference point is at a center thereof.

18. A method according to claim 17, wherein step (B) includes the step of generating an image of a target for which the linear edges substantially meet at the reference point.

19. A method according to any of claim 1 or 12, wherein step (B) includes the step of generating in image of a target having four image able regions.

20. A method according to claim 19, wherein step (B) includes the step of generating an image of a four-region target in which the at least two linear edges of each of the regions are perpendicular to one another.

2. A method according to step 1, wherein step (C) comprises the step of identifying edges in the image by applying an edge detection vision tool to the image.

3. A method according to step 1, wherein step (C) comprises identifying edges in the image by applying a caliper vision tool to the image.

4. A method according to step 1, wherein step (C) comprises identifying edges in the image by applying a first difference operator vision tool to the image, and by applying a peak detector vision tool the an output of the first difference operator vision tool.

5. A method according to step 1, wherein step (C) comprises identifying edges in the image by applying a caliper tool to the image beginning at an approximate location of the reference point.

6. A method according to claim 5, comprising the step of determining an approximate location of the reference point by applying a Hough line vision tool to the image and finding an intersection of lines identified thereby.

7. A method according to claim 5, comprising the step of determining an approximate location of the reference point by applying a correlation vision tool to the image using a template substantially approximating an expected pattern of edges in the image.

8. A method according to claim 5, comprising the step of determining an approximate location of the reference point by applying a projection vision tool to the image along one or more axes with which the edges are expected to be aligned.

12. A machine vision method of determining an orientation of an object, the method comprising the steps of

A. placing a calibration target on the object, the target comprising

i. two or more regions, each region being defined by at least two linear edges that are directed toward a reference point;

ii. at least one of the regions having a different image able characteristic from an adjacent region;

B. generating an image of at least the target;

C. identifying edges in the image corresponding to lines in the calibration target;

D. determining an orientation of the target based on the angle of those identified edges.

13. A method according to claim 12, wherein

step (C) includes applying a Hough line vision tool to the image to identify therein edges in the image corresponding to lines in the calibration target;

and

step (D) includes determining an orientation of the target based on the angle of those identified edges.

14. A method according to claim 12, wherein

step (C) includes applying a Sobel edge tool to generate at least a Sobel angle image;

and

step (D) includes taking an angle histogram of the Sobel angle image to determine an orientation of the target.

21. A machine vision method of determining a location and orientation of an object, the method comprising the steps of

A. placing a calibration target on the object, the target comprising

i. two or more regions, each region being defined by at least two linear edges that are directed toward a reference point;

ii. at least one of the regions having a different image able characteristic from an adjacent region;

B. generating an image of at least the target;

C. generating, based on analysis of the image, an estimate of an orientation of the target therein;

D. generating, based on analysis of the image, an estimate of a location of the reference point therein;

E. refining, based on analysis of the image, estimates of at least one of the location of the reference point in the image and the orientation of the target in the image.

22. A method according to claim 21, wherein step (C) includes one or more of the following steps:

i. applying a Hough line vision tool to the image to find an angle of edges in the image;

ii. applying a Sobel edge tool to the image to generate Sobel angle image, generating a histogram of the angles represented in that Sobel angle image, and determining a one dimensional correlation thereof; and

iii. inputting an orientation from the user.

23. A method according to claim 22, wherein step (D) includes one or more of the following steps:

i. applying a Hough vision tool to the image to find an approximate location of edges therein, and finding an intersection of lines defined by those edges;

ii. applying a projection tool vision tool to the image to find an approximate location of edges therein, and finding an intersection of lines defined by those edges;

iii. applying a correlation vision tool to the image to find approximate coordinates of the reference point; and

iv. determining a sum of absolute differences to find approximate coordinates of the reference point.

24. A method according to claim 23, wherein step (E) includes the step of invoking the steps in sequence, at least one time:

i. applying a caliper vision tool along each of the expected edges in the image, where those edges are defined by a current estimate of the reference point location and a current estimate of the target orientation; and

ii. fitting lines to edge points determined by the caliper vision tool.

25. A method according to claim 24, wherein step (E)(i) includes the step of applying the caliper vision tool along each of the expected edges until any of the following conditions is met:

no edge is found by the caliper vision tool at a sample point;

more than one edge is found by the caliper vision tool at a sample point;

a distance between a sample edge point and a nominal line is larger than a threshold;

or, the caliper vision tool window for the sample edge point extends outside of the image.