METRIC-BASED RECOGNITION, SYSTEMS AND METHODS

• US 20180039851A1
• Filed: 10/17/2017
• Published: 02/08/2018
• Est. Priority Date: 08/19/2013
• Status: Active Grant

• Current Assignee: Nant Holdings IP, LLC (NantWorks, LLC)
• Original Assignee: Nant Holdings IP, LLC (NantWorks, LLC)
• Inventors: Siddiqui, Matheen
• Granted Patent: US 10,121,092 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/3331 Query processing
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 16/7335 Graphical querying, e.g. query-by-region, query-by-sketch, query-by-trajectory, GUIs for designating a person/face/object as a query predicate end-user interface involving hot spots associated with the video H04N21/4725; end-user interface for selecting a Region of Interest H04N21/4728
• CPC Class Codes: G06F 16/785 using colour or luminescence
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06V 10/56 relating to colour
• CPC Class Codes: G06V 30/422 Technical drawings; Geographical maps
• CPC Class Codes: H04N 1/40012 Conversion of colour to monochrome

Abstract

Apparatus, methods and systems of object recognition are disclosed. Embodiments of the inventive subject matter generates map-altered image data according to an object-specific metric map, derives a metric-based descriptor set by executing an image analysis algorithm on the map-altered image data, and retrieves digital content associated with a target object as a function of the metric-based descriptor set.

Claims

1-33. -33. (canceled)

34. A method of generating a metric-based recognition map comprising:

receiving, by a computing device configured to operate as an image processing engine, image data including a computer generated image or graphic;

compiling, by the image processing engine, an initial object-specific metric map for a specific human person from at least a portion of the image data where the portion represents at least a portion of the object;

generating, by the image processing engine, a metric-based descriptor set by executing an image analysis algorithm on the portion of the image data as a function of the initial object-specific metric map; and

storing the metric-based descriptor set in an object recognition database.

35. The method of claim 34, further comprising adjusting the initial object-specific metric map to generate a new object-specific metric map by tuning metric values in a manner effective to enhance differentiation of descriptors generated by the image analysis algorithm as executed on the portion of the image data.

36. The method of claim 35, wherein adjusting the initial object-specific metric map includes accepting user input that alters at least some metric values within the initial object-specific metric map.

37. The method of claim 35, wherein adjusting the initial object-specific metric map includes the image processing engine recommending at least one a metric value that increases a confidence of a descriptor.

38. The method of claim 35, wherein adjusting the initial object-specific metric map includes the image processing engine automatically adjusting metric values of the initial object-specific metric map.

39. The method of claim 35, wherein the new metric-based map comprises a non-linear mapping from metric values within the initial metric-based map.

40. The method of claim 34, further comprising generating an object-specific color map based the object-specific metric map.

41. The method of claim 40, further comprising storing the object-specific color map as part of the metric-based descriptor set.

42. The method of claim 34, further comprising identifying at least one of a position and an orientation of an imaging device configured to capture the image data.

43. The method of claim 42, further comprising storing the at least one of the position and the orientation with the metric-based descriptor set.

44. The method of claim 34, further comprising removing specularity from the image data.

45. The method of claim 34, wherein the object-specific metric map comprises a pixel-level metric map.

46. The method of claim 34, wherein the metric-based descriptor set comprises lighting invariant descriptors.

47. The method of claim 34, wherein the metric-based descriptor set comprises metric-based invariant descriptors.

48. The method of claim 47, wherein the metric-based descriptors comprise metric-based scale invariant descriptors.

49. The method of claim 34, wherein the object comprises a physical object.

50. The method of claim 34, further comprising storing a key frame bundle that includes the metric-based descriptor set.

51. The method of claim 34, wherein the map is focused on skin tone.

52. The method of claim 34, wherein the map comprises a tissue-specific map.

53. The method of claim 51, further comprising identifying skin tone variations of a specific person'"'"'s face that are indicative of a skin lesion or other abnormality.

54. The method of claim 52, further comprising one or more tissue-specific maps that aid in differentiating structure or features of internal organs during surgery.

55. The method of claim 54, wherein one or more features such as tumors are identified because such features fail to conform to the person'"'"'s tissue-specific maps.