METHOD AND SYSTEM FOR OBJECT IDENTIFICATION

• US 20160098620A1
• Filed: 03/11/2013
• Published: 04/07/2016
• Est. Priority Date: 03/11/2013
• Status: Abandoned Application

• Current Assignee: 1626628 Ontario Limited
• Original Assignee: 1626628 Ontario Limited
• Inventors: GEILE, Wolfhard
• Application Number:
  US13/793,053
• Publication Number:
  US 20160098620A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/29 Graphical models, e.g. Bayesian networks
• CPC Class Codes: G06V 10/426 Graphical representations
• CPC Class Codes: G06V 10/84 using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks

Abstract

A system and method for object classification is provided. The system includes a computing device that typically comprises a processor configured to receive data and detect an object within the data. Once an object is detected, it can be decomposed into sub-objects and connectivities. Based on the sub-objects and connectivities parameters can be generated. Moreover, based on at least one of sub-objects, connectivities and parameters objective measures can be generated. The object can then be classified based on the objective measures. The parameters can be linked into into linked parameters. Linked classification measures can be generated based on linked parameters. The system can also detect environment objects that form the environment of the detected object. Similar to an object, an environment object can be decomposed into environment sub-objects, and subsequently to environment parameters. Objective measure generation can then be further based on the environment

Claims

1. A method of object classification of a computing device comprising:

receiving data;

detecting an object based on said data;

decomposing said object into sub-objects and;

generating parameters based on said sub-objects and connectivities; and

generating objective measures based on at least one of said sub-objects, connectivities and parameters.

2. The method of claim 1 further comprising:

classifying said object based on said objective measures.

3. The method of claim 2 further comprising:

maintaining said parameters, connectivities and sub-objects as a primary multi-dimensional data structure; and

maintaining said objective measures as a secondary multi-dimensional data structure.

4. The method of claim 1 further comprising:

decomposing said sub-objects until each sub-object is a primitive object.

5. The method of claim 1, wherein decomposing is repeated on the sub-objects for n times where n is an integer >

1.

6. The method of claim 1 wherein said parameters comprise on one or more of sensory data measures and derived physical measures.

7. The method of claim 6 wherein said sensory data measures comprise one or more of tone, texture and gray value gradient.

8. The method of claim 1 wherein said data is received from a sensing device.

9. The method of claim 1 wherein said data is received from a non-imaging source.

10. The method of claim 1, wherein generating said objective measures include determining an occurrence or co-occurrence of sub-objects, parameters and connectivities.

11. The method of claim 1, generating at least one objective measure further comprising:

linking said parameters into linked parameters; and

generating linked classification measures based on said linked parameters.

12. The method of claim 11, wherein said linking is performed based on connectivities.

13. The method of claim 1 wherein said connectivities include one or more of a spatial, temporal or functional relationship between a plurality of sub-objects.

14. The method of claim 2 wherein said classification is based on a rule based association of said objective measures.

15. The method of claim 1, wherein said generating of said objective measures includes pattern analysis of said parameters.

16. The method of claim 1 further comprising:

detecting an environment object based on said data;

decomposing said environment object into environment sub-objects; and

generating environment parameters based on said environment sub-objects;

wherein generating at least one objective measure is further based on said environment parameters.

17. The method of claim 16 wherein said environment sub-objects and said sub-objects are linked and at least one of said at least one objective measure is based on said linkage between said sub-objects and said environment sub-objects.

18. A computing device for object classification, comprising:

a processor configured to;

receive data;

detect an object within said data;

decompose said object into sub-objects and connectivities;

generate parameter based on said sub-objects and connectivities; and

generate objective measures based on at least one of said sub-objects, connectivities and parameters.

19. The device of claim 18 wherein said processor is further configured to classify said object based on said objective measures.

20. The device of claim 18 wherein said processor is further configured to decompose said sub-objects until each sub-object is a primitive object.

21. The device of claim 18 wherein said processor is further configured to:

link said parameters into linked parameters; and

generate linked classification measures based on said linked parameters.

22. The device of claim 18 wherein said processor is further configured to:

detect an environment object based on said data;

decompose said environment object into environment sub-objects; and

generate environment parameters based on said environment sub-objects;

wherein said processor is configured to generate said objective measures further based on said environment parameters.

23. The method of claim 18 wherein said processor is further configured to:

maintain said parameters, connectivities and sub-objects as a primary multi-dimensional data structure; and

maintain said objective measures as secondary multi-dimensional data structure.

24. The method of claim 23 wherein said processor is further configured to classify said object based on said secondary multi-dimensional data structure.