System or method for classifying images

• US 20050271280A1
• Filed: 07/23/2003
• Published: 12/08/2005
• Est. Priority Date: 07/23/2003
• Status: Abandoned Application

• Current Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Original Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Inventors: Farmer, Michael, Chen, Xunchang
• Application Number:
  US10/625,208
• Publication Number:
  US 20050271280A1
• Time in Patent Office: Days
• US Class Current: 382/224
• CPC Class Codes: G06V 20/59 inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

Abstract

A system or method (collectively “classification system”) is disclosed for classifying sensor images into one of several pre-defined classifications. Mathematical moments relating to various features or attributes in the sensor image are used to populated a vector of attributes, which are then compared to a corresponding template vector of attribute values. The template vector contains values for known classifications which are preferably predefined. By comparing the two vectors, various votes and confidence metrics are used to ultimately select the appropriate classification. In some embodiments, preparation processing is performed before loading the attribute vector with values. Image segmentation is often desirable. The performance of heuristics to adjust for environmental factors such as lighting can also be desirable. One embodiment of the system is to prevent the deployment of an airbag when the occupant in the seat is a child, a rear-facing infant seat, or when the seat is empty.

Claims

1. A classification system comprising:

a vector subsystem, including a sensor image and a feature vector, wherein said vector subsystem provides for generating said feature vector from said sensor image; and

a determination subsystem, including a classification, a first confidence metric, and a historical characteristic, wherein said determination subsystem provides for generating said classification from said feature vector, said first confidence metric, and said historical characteristic.

2. The system of claim 1, said determination subsystem further including a second confidence metric, wherein said determination subsystem provides for generating said classification with said second confidence metric.

3. The system of claim 1, wherein said historical characteristic comprises a prior classification and a prior confidence metric.

4. The system of claim 1, wherein said sensor image is captured by a digital camera.

5. The system of claim 1, wherein said sensor image is in the form of a two-dimensional representation.

6. The system of claim 1, wherein said sensor image is in the form of an edge image.

7. The system of claim 1, further comprising a airbag deployment mechanism, said airbag deployment mechanism including a disablement decision, wherein said airbag deployment mechanism provides for generating said disablement decision from said classification.

8. The system of claim 1, further comprising a image processing subsystem, said image processing subsystem including a raw sensor image, wherein said sensor image processing subsystem generates said sensor image from said raw sensor image.

9. The system of claim 8, wherein said image processing subsystem performs a light evaluation heuristic to set a brightness value.

10. The system of claim 9, wherein said sensor image processing subsystem further includes a plurality of processing heuristics, wherein said sensor image processing subsystem provides for selectively invoking one or more of said processing heuristics using said brightness value.

11. The system of claim 9, wherein said light evaluation heuristic is a day-night determination heuristic and said brightness value is a day-night flag capable of being set to a value of day or a value of night.

12. The system of claim 11, wherein a day-night flag value of day triggers said sensor image processing subsystem to perform a day processing heuristic.

13. The system of claim 12, wherein said day processing heuristic comprises at lease one of a gradient image heuristic, a boundary erosion heuristic, and an adaptive edge thresholding heuristic.

14. The system of claim 11, wherein a day-night flag value of night triggers said sensor image processing subsystem to perform a night processing heuristic.

15. The system of claim 14, wherein said night processing heuristic comprises at least one of a brightness threshold heuristic and a silhouette extraction heuristic.

16. The system of claim 1, wherein said feature vector comprises a plurality of Legendre orthogonal moments.

17. The system of claim 1, wherein said feature vector comprises a plurality of normalized feature values.

18. The system of claim 1, wherein said determination subsystem provides for invoking a k-nearest neighbor heuristic to generate said classification.

19. The system of claim 18, wherein said k-nearest neighbor heuristic comprises a distance heuristic.

20. The system of claim 19, wherein said distance heuristic calculates a Euclidean distance metric.

21. The system of claim 1, wherein said determination subsystem accesses a historical classification and a historical confidence metric to generate said classification.

22. An airbag deployment system, comprising:

a plurality of pre-defined occupant classifications;

a camera for capturing a raw image;

a computer, including an edge image and vector of features, wherein said computer generates said edge image from said raw image, wherein said vector of features is loaded from said edge image, and wherein one classification within said plurality of pre-defined occupant classifications is selectively identified by said computer from said vector of features; and

an airbag deployment mechanism, including a classification and an airbag deployment determination, wherein said airbag deployment mechanism provides for generating said airbag deployment determination from said classification.

23. The system of claim 22, further comprising a day-night flag, wherein said computer further includes a plurality of processing heuristics from generating said edge image from said raw image, and wherein said computer uses said day-night flag to selectively identify one said processing heuristic from said plurality of processing heuristics.

24. The system of claim 22, wherein said vector of features comprise a plurality of Legendre orthogonal moments.

25. The system of claim 22, wherein said computer calculates a Euclidean distance metric from said vector of features by invoking a k-nearest neighbor heuristic.

26. The system of claim 22, wherein a ranking heuristic is performed to calculate a first confidence metric and a median distance heuristic is invoked to compute a second confidence metric, wherein said computer selectively identifies said classification with said first confidence metric and said second confidence metric.

27. The system of claim 22, wherein a said computer accesses a historical characteristic before said computer generates said classification.

28. A method for classifying an image, comprising:

capturing a visual image of a target;

making a day-night determination from the visual image of the target;

selecting a image processing heuristic on the basis of the day-night determination;

converting the visual image into an edge image with the selecting image processing heuristic;

populating a vector of features with feature values extracted from the edge image; and

generating a classification from the vector of features.

29. The method of claim 28, further comprising selectively disabling an airbag deployment mechanism when said classification is one of a plurality of pre-determined classifications requiring the disablement of the airbag deployment mechanism.

30. The method of claim 28, wherein the classification is generated from a historical characteristic of the target.

31. The method of claim 28, wherein the classification is generated from a confidence metric derived from a distance heuristic.