SYSTEMS AND METHODS FOR OBJECT RECOGNITION USING A LARGE DATABASE

• US 20110286628A1
• Filed: 05/13/2011
• Published: 11/24/2011
• Est. Priority Date: 05/14/2010
• Status: Abandoned Application

• Current Assignee: Datalogic Adc, Inc. (Datalogic S.p.A.)
• Original Assignee: Datalogic Adc, Inc. (Datalogic S.p.A.)
• Inventors: Boman, Robert, Ostrowski, Jim, Goncalves, Luis F.
• Application Number:
  US13/107,824
• Publication Number:
  US 20110286628A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 16/5854 using shape and object relationship

Abstract

A method of organizing a set of recognition models of known objects stored in a database of an object recognition system includes determining a classification model for each known object and grouping the classification models into multiple classification model groups. Each classification model group identifies a portion of the database that contains the recognition models of the known objects having classification models that are members of the classification model group. The method also includes computing a representative classification model for each classification model group. Each representative classification model is derived from the classification models that are members of the classification model group. When a target object is to be recognized, the representative classification models are compared to a classification model of the target object to enable selection of a subset of the recognition models of the known objects for comparison to a recognition model of the target object.

Claims

1. A method of organizing a set of recognition models of known objects stored in a database of an object recognition system, the method comprising:

determining for each of the known objects a classification model;

grouping the classification models of the known objects into multiple classification model groups, each of the classification model groups identifying a corresponding portion of the database that contains the recognition models of the known objects having classification models that are members of the classification model group; and

computing representative classification models for the classification model groups, wherein a representative classification model of a classification model group is derived from the classification models that are members of the classification model group, and wherein the representative classification models are compared to a classification model of a target object when recognizing the target object to enable selection of a subset of the recognition models of the known objects for comparison to a recognition model of the target object.

2. The method of claim 1, wherein determining the classification model of a known object comprises measuring an appearance characteristic from an image of the known object.

3. The method of claim 2, wherein the appearance characteristic corresponds to one or more of color, texture, spatial frequency, shape, illumination invariant image properties and illumination invariant image gradient properties.

4. The method of claim 2, wherein the classification model of the known object is determined by:

segmenting an image of a scene captured by an image capturing device to produce an isolated image of the known object;

computing local feature descriptor vectors from the image of the known object, wherein the local feature descriptor vectors are within a feature descriptor vector space;

dividing the feature descriptor vector space into multiple regions;

determining which regions the local feature descriptor vectors belong to; and

creating a histogram that quantifies how many local feature descriptor vectors belong to each of the regions, the histogram corresponding to the classification model.

5. The method of claim 4, further comprising:

assigning to each of the regions a representative descriptor vector; and

comparing the local feature descriptor vectors to the representative descriptor vectors to determine which region the local feature descriptor vectors belong to.

6. The method of claim 2, wherein the classification model of the known object is determined by:

segmenting an image of a scene captured by an image capturing device to produce an isolated image of the known object;

applying a geometric transformation to the segmented image of the known object to obtain a normalized image of the known object; and

generating a single feature descriptor for the normalized image of the known object, the classification model including a representation of the single feature descriptor.

7. The method of claim 6, wherein the single feature descriptor is generated using the entire extent of the normalized image of the known object.

8. The method of claim 2, wherein the classification model of the known object is determined by:

segmenting an image of a scene captured by an image capturing device to produce an isolated image of the known object;

applying a geometric transformation to the segmented image of the known object to obtain a normalized image of the known object;

dividing the normalized image of the known object into multiple predetermined grid portions; and

generating for each grid portion of the divided image a feature descriptor vector, the classification model including a representation of the feature descriptor vectors of the grid portions.

9. The method of claim 2, wherein the classification model of the known object is determined by:

segmenting an image of a scene captured by an image capturing device to produce an isolated image of the known object;

applying a geometric transformation to the segmented image of the known object to obtain a normalized image of the known object, wherein a vector represents the normalized image; and

computing a principal component analysis representation of the vector representing the normalized image, the classification model including a representation of the principal component analysis representation of the vector.

10. The method of claim 1, wherein determining the classification model of a known object comprises measuring a physical property of the known object.

11. The method of claim 10, wherein the physical property is one or more of height, width, length, shape, mass, a geometric moment, volume, curvature, an electromagnetic characteristic and temperature.

12. The method of claim 10, further comprising measuring an appearance characteristic from an image of the known object, wherein the classification model of the known object includes a representation of the physical property of the known object and a representation of the appearance characteristic of the known object.

13. The method of claim 1, wherein the classification model groups are formed by using a clustering algorithm on the classification models.

14. The method of claim 13, wherein the classification models of the known objects are clustered using a k-means clustering algorithm.

15. The method of claim 13, wherein a number of the classification model groups into which the classification models are clustered is determined prior to the clustering.

16. The method of claim 13, wherein a number of the classification model groups in which the classification models are clustered is determined during clustering.

17. The method of claim 1, wherein the clustering includes soft clustering in which a classification model of a known object is clustered into one or more of the classification model groups and the recognition model of the known object is included in one or more of the portions of the database.

18. The method of claim 1, wherein a representative classification model of a classification model group corresponds to a mean of the classification models that are members of the classification model group.

19. The method of claim 1, wherein the classification model includes a classification signature that represents a n-dimensional vector.

20. A method of recognizing a target object from a database containing recognition models of a set of known objects, the database being divided into multiple portions, and each portion containing recognition models of a subset of the known objects, comprising:

receiving image data representing an image of the target object;

determining for the target object a classification model;

generating for the target object a recognition model derived from the image of the target object;

comparing the classification model of the target object to representative classification models associated with the portions of the database, the representative classification model of a portion of the database derived from classification models of a subset of the known objects having recognition models contained in the portion;

selecting a portion of the database to search based on the comparing; and

searching the selected portion of the database to identify a recognition model of a known object that matches the recognition model of the target object.

21. The method of claim 20, wherein determining the classification model of the target object comprises measuring an appearance characteristic from the image of the target object.

22. The method of claim 21, wherein the appearance characteristic corresponds to one or more of color, texture, spatial frequency, shape, illumination invariant image properties and illumination invariant image gradient properties.

23. The method of claim 21, wherein the classification model of the target object is determined by:

segmenting an image of a scene captured by an image capturing device to produce an isolated image of the target object;

computing local feature descriptor vectors from the image of the target object, wherein the local feature descriptor vectors are within a feature descriptor vector space;

dividing the feature descriptor vector space into multiple regions;

determining which regions the local feature descriptor vectors belong to; and

creating a histogram that quantifies how many local feature descriptor vectors belong to each of the regions of the feature descriptor vector space, the histogram corresponding to the classification model of the target object.

24. The method of claim 23, further comprising:

assigning to each of the regions a representative descriptor vector; and

comparing the local feature descriptor vectors to the representative descriptor vectors to determine which region the local feature descriptor vectors belong to.

25. The method of claim 21, wherein the classification model of the target object is determined by:

segmenting an image of a scene captured by an image capturing device to produce an isolated image of the target object;

applying a geometric transformation to the segmented image of the target object to obtain a normalized image of the target object; and

generating a single feature descriptor for the normalized image of the target object, the classification model including a representation of the single feature descriptor.

26. The method of claim 21, wherein the classification model of the target object is determined by:

segmenting an image of a scene captured by an image capturing device to produce an isolated image of the target object;

applying a geometric transformation to the segmented image of the target object to obtain a normalized image of the target object;

dividing the normalized image of the target object into multiple predetermined grid portions; and

generating for each grid portion of the divided image a feature descriptor vector, the classification model including a representation of the feature descriptor vectors of the grid portions.

27. The method of claim 21, wherein the classification model of the target object is determined by:

segmenting an image of a scene captured by an image capturing device to produce an isolated image of the target object;

applying a geometric transformation to the segmented image of the target object to obtain a normalized image of the target object, wherein a vector represents the normalized image; and

computing a principal component analysis representation of the vector representing the normalized image, the classification model including a representation of the principal component analysis representation of the vector.

28. The method of claim 20, wherein determining the classification model of the target object comprises measuring a physical property of the target object.

29. The method of claim 28, wherein the physical property is one or more of height, width, length, shape, mass, a geometric moment, volume, curvature, an electromagnetic characteristic and temperature.

30. The method of claim 28, further comprising measuring an appearance characteristic from the image of the target object, wherein the classification model of the target object includes a representation of the physical property of the target object and a representation of the appearance characteristic of the target object.

31. The method of claim 20, wherein the classification model of the target object and the representative classification models of the portions of the database are vectors and the comparing includes determining Euclidean distances between the classification model of the target object and the representative classification models, wherein the shortest Euclidean distance identifies the portion of the database to select for the searching.

32. The method of claim 20, wherein the recognition model of the target object and the recognition models of the known objects include feature descriptors.

33. The method of claim 32, wherein the feature descriptors are scale invariant feature transformation feature descriptors.

34. The method of claim 20, wherein multiple ones of the portions of the database are selected based on comparing the classification model of the target object to the representative classification models of the portions.

35. An object recognition system for recognizing a target object, comprising:

a database containing recognition models of a set of known objects, the database divided into multiple portions each containing recognition models of a subset of the known objects, wherein the portions have representative classification models, and wherein the representative classification model of a portion is derived from classification models of a subset of the known objects having recognition models contained in the portion; and

a processor comprising;

a classification module configured to generate for the target object a classification model, the classification module configured to compare the classification model of the target object to the representative classification models of the portions of the database to select a portion, and

a recognition module configured to receive image data representing an image of the target object and produce from the image data a recognition model of the target object, the recognition module configured to search a portion of the database selected by the classification module to identify a recognition model contained in the portion that matches the recognition model of the target object.

36. The system of claim 35, wherein the classification module is configured to receive the image data representing the image of the target object and generate the classification model of the target object from an appearance characteristic represented in the image data.

37. The system of claim 36, wherein the appearance characteristic is one or more of color, texture, spatial frequency, shape, illumination invariant image properties, illumination invariant image gradient properties, a histogram derived from quantized local feature descriptor vectors, a single feature descriptor representation derived from a normalized image of the target object, feature descriptor vectors corresponding to predetermined grid portions of a normalized image of the target object and a principal component analysis representation.

38. The system of claim 35, wherein the classification model of the target object includes a representation of a physical property of the target object.

39. The system of claim 38, wherein the physical property is one or more of height, width, length, shape, mass, a geometric moment, volume, curvature, an electromagnetic characteristic and temperature.

40. The system of claim 35, wherein:

the classification model of the target object and the representative classification models of the portions of the database are vectors;

the classification module is configured to determine Euclidean distances between the classification model of the target object and the representative classification models; and

the shortest Euclidean distance identifies the portion of the database to select.

41. The system of claim 35, wherein the recognition model of the target object and the recognition models of the known objects include feature descriptors.

42. The system of claim 41, wherein the feature descriptors are scale invariant feature transformation feature descriptors.

43. The system of claim 35, further comprising an image capturing device to produce the image data representing the image of the target object.