Fast Method of Object Detection by Statistical Template Matching

• US 20080166016A1
• Filed: 02/20/2006
• Published: 07/10/2008
• Est. Priority Date: 02/21/2005
• Status: Abandoned Application

• Current Assignee: Mitsubishi Electric Corporation
• Original Assignee: Mitsubishi Electric Corporation
• Inventors: Bober, Miroslaw, Sibiryakov, Alexander
• Application Number:
  US11/884,699
• Publication Number:
  US 20080166016A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/7515 Shifting the patterns to accommodate for positional errors
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation

Abstract

A method of detecting an object in an image comprises comparing a template with a region of an image and determining a similarity measure, wherein the similarity measure is determined using a statistical measure. The template comprises a number of regions corresponding to parts of the object and their spatial relations. The variance of the pixels within the total template is set in relation to the variances of the pixels in all individual regions, to provide a similarity measure.

Claims

1. A method of detecting an object in an image comprising comparing a template with a region of an image and determining a similarity measure, wherein the similarity measure is determined using a statistical measure.

2. The method of claim 1 wherein the statistical measure is determined using statistical values of the region of the image corresponding to the template.

3. The method of claim 2 wherein the statistical values of the region comprise mean and variance of pixel values within the region of the image corresponding to the template.

4. The method of any of claims 1 to 3 wherein the statistical measure involves statistical hypothesis testing.

5. The method of any of claims 1 to 4 using a template comprising M regions, where M is two or more, the M regions of the template corresponding to parts of the object and their spatial relations.

6. The method of claim 5 wherein the template is the union of M regions.

7. The method of claim 5 or claim 6 wherein regions of an object having similar radiometric properties, such as colour, intensity etc are combined in one region of the template.

8. The method of any of claims 5 to 7 wherein one or more regions contains one or more areas which are unused in template matching.

9. The method of any of claims 5 to 8 wherein at least one region comprises unconnected sub-regions.

10. The method of any of claims 5 to 9 wherein the regions correspond to simple shapes.

11. The method of any of claims 5 to 10 wherein the shapes have straight edges.

12. The method of claim 11 wherein the shapes are rectangles.

13. The method of any of claims 5 to 12 wherein the similarity measure involves each of the M regions of the template.

14. The method of claim 13 wherein the similarity measure involves each of the M regions of the template and a region corresponding to the whole template.

15. The method of any of claims 5 to 14 wherein statistical values are used for each of the regions of the image corresponding to the M or M+1 regions of the template.

16. The method of claim 15 wherein the statistical values include mean and variance.

17. The method of claim 16 wherein use of the statistical measure involves applying the statistical t-test to pixel groups.

18. The method of claim 17 wherein the similarity measure is in the form of or similar to equations (1) or (4).

19. The method of claim 16 wherein use of the statistical measure involves applying the analysis of variances, ANOVA, test.

20. The method of claim 19 wherein the similarity measure is in the form of or similar to equations (8) or (9).

21. The method of any of claims 1 to 20 comprising comparing the similarity measure with a threshold.

22. The method of claim 21 comprising using statistical thresholding or a statistical significance level.

23. The method of claim 22 comprising setting a risk level, and using the risk level, the degrees of freedom and a table of significance.

24. The method of any of claims 1 to 23 comprising deriving an integral image from the image and using the integral image in the calculation of the similarity measure.

25. The method of claim 24 comprising using the integral image and relation (10) or (11) in the calculation of the similarity measure.

26. The method of any of claims 1 to 25 comprising deriving a similarity measure for each of a plurality of regions in the image to derive a similarity map, and identifying local maxima or minima according to the similarity measure.

27. The method of claim 26 comprising comparing local maxima or minima with a threshold.

28. The method of any of claims 1 to 27 comprising using additional conditions regarding the object of interest in object detection.

29. The method of claim 28 where the additional conditions involve statistical values derived in the statistical hypothesis testing.

30. The method of any of claims 1 to 29 comprising using a plurality of templates each representing an object and deriving a similarity measure using each of the plurality of templates, and using the plurality of similarity measures, such as by combining, to locate the object.

31. The method of any of claims 1 to 30 comprising generating a plurality of versions of the image at different resolutions and a plurality of versions of the template at different resolutions, performing template matching at a first resolution and template matching at a second higher resolution.

32. The method of claim 31 wherein the matching at a first resolution is to detect a region of interest containing the object, and the matching at a second resolution is carried out within the region of interest.

33. The method of claim 31 or claim 32 including adjusting the template for a resolution, for example, by merging or excluding template regions, or changing the size or shape of the template or template regions, depending on detection results at a different resolution.

34. A method of tracking an object in a sequence of images comprising detecting an object using the method of any of claims 1 to 33, predicting an approximate location of the object in a subsequent image and using the prediction to determine a region of interest in the subsequent image, and using the method of any of claims 1 to 33 in the region of interest to detect the object.

35. The method of claim 34 including adjusting the template for an image in the sequence of images, for example, by merging or excluding template regions, or changing the size or shape of the template or template regions, depending on detection results in a different image in the sequence of images.

36. The method of any preceding claim for detecting facial features and/or faces.

37. The method of any preceding claim for detecting features in satellite images, geographical images or the like.

38. The method of any preceding claim for detecting fiduciary marks, road markings, watermarks or the like.

39. Apparatus for executing the method of any of claims 1 to 38.

40. A control device programmed to execute the method of any of claims 1 to 38.

41. Apparatus comprising the control device of claim 40, and storage means for storing images.

42. A computer program, system or computer-readable storage medium for executing a method of any of claims 1 to 38.