Body Condition Score Determination for an Animal

• US 20140029808A1
• Filed: 07/23/2013
• Published: 01/30/2014
• Est. Priority Date: 07/23/2012
• Status: Abandoned Application

• Current Assignee: clicRweight LLC
• Original Assignee: clicRweight LLC
• Inventors: Lee, Ken
• Application Number:
  US13/948,826
• Publication Number:
  US 20140029808A1
• Time in Patent Office: Days
• US Class Current: 382/110
• CPC Class Codes: A01K 29/00 Other apparatus for animal husbandry
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/4561 Evaluating static posture, e.g. undesirable back curvature A61B5/1116 takes precedence
• CPC Class Codes: A61B 5/4872 Body fat
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 7/0014 using an image reference approach
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

Abstract

Described are methods and systems for determining a body condition score (BCS) for an animal. An imaging device captures at least one image of an animal, where each image includes a body region of the animal, and transmits the image to a computing device. The computing device identifies the body region contained in the image and crops a portion of the image associated with the identified body region from the image. The computing device compares the cropped portion of the image with one or more fitting models corresponding to the body region and determines a body condition score for the body region based upon the comparing step.

Claims

1. A method for determining a body condition score (BCS) for an animal, the method comprising:

capturing, by an imaging device, at least one image of an animal, wherein each image includes a body region of the animal, and transmitting the image to a computing device;

identifying, by a computing device, the body region contained in the image;

cropping, by the computing device, a portion of the image associated with the identified body region from the image;

comparing, by the computing device, the cropped portion of the image with one or more fitting models corresponding to the body region; and

determining, by the computing device, a body condition score for the body region based upon the comparing step.

2. The method of claim 1, further comprising validating, by the computing device, a posture of the animal in the image.

3. The method of claim 2, the validating step further comprising:

generating a 3D point cloud based upon the image;

performing one or more edge analysis tests on the 3D point cloud; and

determining whether the posture is valid based upon the one or more edge analysis tests.

4. The method of claim 3, wherein performing one or more edge analysis tests includes:

generating a cubic polynomial curve based upon the topmost points of the 3D point cloud; and

analyzing the inflection point and concavity of the cubic polynomial curve.

5. The method of claim 4, further comprising analyzing the local minimum and maximum of the cubic polynomial curve.

6. The method of claim 3, wherein performing one or more edge analysis tests includes:

generating a linear model based upon the topmost points of the 3D point cloud; and

analyzing the slope of the linear model.

7. The method of claim 6, wherein the computing device determines that the posture is not valid if the slope of the linear model exceeds 0.12.

8. The method of claim 1, wherein each image includes a plurality of body regions.

9. The method of claim 1, wherein identifying the body region contained in the image includes:

determining a type of the animal in the image; and

selecting a portion of the image corresponding to the body region based upon the animal type.

10. The method of claim 9, wherein the animal type includes a sex of the animal, a breed of the animal, and a species name of the animal.

11. The method of claim 1, wherein comparing the cropped portion of the image with one or more fitting models corresponding to the body region includes:

retrieving the one or more fitting models from a database based upon a type of the animal and the body region;

identifying one or more anatomical features in the cropped portion of the image;

identifying one or more anatomical features in the fitting models; and

comparing cloud points in the cropped portion of the image that are associated with the one or more anatomical features with cloud points in the one or more fitting models that are associated with the one or more anatomical features.

12. The method of claim 11, wherein each of the one or more fitting models is associated with a known BCS.

13. The method of claim 11, wherein the comparing cloud points step includes:

determining an error value between the cloud points in the cropped portion of the image and the cloud points in the one or fitting models; and

selecting the fitting model that has the minimum error value.

14. The method of claim 13, wherein the determining an error value step is based upon an iterative closest point algorithm.

15. The method of claim 13, further comprising assigning the BCS associated with the fitting model that has the minimum error value to the cropped portion of the image.

16. The method of claim 11, further comprising adjusting at least one of height, length or depth of at least one cloud point of (i) the image relative to one of the fitting models or (ii) one of the fitting models relative to the image.

17. The method of claim 11, further comprising determining whether the density of cloud points in the image meets a predefined threshold.

18. The method of claim 1, wherein the image is a 3D scan.

19. The method of claim 1, wherein the body region is a rump, hip, backbone, thigh, short-rib, long-rib, tail-head, or pin bone of the animal.

20. The method of claim 1, wherein the BCS is a score on a scale that indicates fatness or thinness of the animal.

21. The method of claim 1, wherein each image includes a plurality of body regions, the method further comprising determining an overall body condition score for the animal based upon the body condition score for each of the plurality of body regions.

22. A system for determining a body condition score (BCS) for an animal, the system comprising:

an imaging device configured to capture at least one image of an animal, wherein each image includes a body region of the animal, and transmit the image to a computing device coupled to the imaging device;

the computing device configured to;

identify the body region contained in the image;

crop a portion of the image associated with the identified body region from the image;

compare the cropped portion of the image with one or more fitting models corresponding to the body region; and

determine a body condition score for the body region based upon the comparing step.

23. The system of claim 22, the computing device further configured to validate a posture of the animal in the image.

24. The system of claim 23, the validating step further comprising:

generating a 3D point cloud based upon the image;

performing one or more edge analysis tests on the 3D point cloud; and

determining whether the posture is valid based upon the one or more edge analysis tests.

25. The system of claim 24, wherein performing one or more edge analysis tests includes:

generating a cubic polynomial curve based upon the topmost points of the 3D point cloud; and

analyzing the inflection point and concavity of the cubic polynomial curve.

26. The system of claim 25, further comprising analyzing the local minimum and maximum of the cubic polynomial curve.

27. The system of claim 24, wherein performing one or more edge analysis tests includes:

generating a linear model based upon the topmost points of the 3D point cloud; and

analyzing the slope of the linear model.

28. The system of claim 27, wherein the computing device determines that the posture is not valid if the slope of the linear model exceeds 0.12.

29. The system of claim 22, wherein each image includes a plurality of body regions.

30. The system of claim 22, wherein identifying the body region contained in the image includes:

determining a type of the animal in the image; and

selecting a portion of the image corresponding to the body region based upon the animal type.

31. The system of claim 30, wherein the animal type includes a sex of the animal, a breed of the animal, and a species name of the animal.

32. The system of claim 22, wherein comparing the cropped portion of the image with one or more fitting models corresponding to the body region includes:

retrieving the one or more fitting models from a database based upon a type of the animal and the body region;

identifying one or more anatomical features in the cropped portion of the image;

identifying one or more anatomical features in the fitting models; and

comparing cloud points in the cropped portion of the image that are associated with the one or more anatomical features with cloud points in the one or more fitting models that are associated with the one or more anatomical features.

33. The system of claim 32, wherein each of the one or more fitting models is associated with a known BCS.

34. The system of claim 32, wherein the comparing cloud points step includes:

determining an error value between the cloud points in the cropped portion of the image and the cloud points in the one or fitting models; and

selecting the fitting model that has the minimum error value.

35. The system of claim 34, wherein the determining an error value step is based upon an iterative closest point algorithm.

36. The system of claim 34, the computing device further configured to assign the BCS associated with the fitting model that has the minimum error value to the cropped portion of the image.

37. The system of claim 32, the computing device further configured to adjust at least one of height, length or depth of at least one cloud point of (i) the image relative to one of the fitting models or (ii) one of the fitting models relative to the image.

38. The system of claim 32, the computing device further configure to determine whether the density of cloud points in the image meets a predefined threshold.

39. The system of claim 22, wherein the image is a 3D scan.

40. The system of claim 22, wherein the body region is a rump, hip, backbone, thigh, short-rib, long-rib, tail-head, or pin bone of the animal.

41. The system of claim 22, wherein the BCS is a score on a scale that indicates fatness or thinness of the animal.

42. The system of claim 22, wherein each image includes a plurality of body regions, the computing device further configured to determine an overall body condition score for the animal based upon the body condition score for each of the plurality of body regions.