Analyzing or resolving ambiguities in an image for object or pattern recognition

• US 9,171,261 B1
• Filed: 06/22/2015
• Issued: 10/27/2015
• Est. Priority Date: 09/24/2011
• Status: Active Grant

• Current Assignee: Z Advanced Computing, Inc.
• Original Assignee: Z Advanced Computing, Inc.
• Inventors: Zadeh, Lotfi A.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Seck, Ababacar
• Application Number:
  US14/746,815
• Publication Number:
  US 20150310301A1
• Time in Patent Office: 127 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G05B 13/0275 using fuzzy logic only
• CPC Class Codes: G06F 16/90344 by using string matching techniques
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06N 7/02 using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00
• CPC Class Codes: G06N 7/023 Learning or tuning the parameters of a fuzzy system
• CPC Class Codes: G06T 7/12 Edge-based segmentation

Abstract

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

Claims

1. A method for analyzing or resolving ambiguities in an image for object or pattern recognition, said method comprising:

an input device receiving a first image;

wherein said first image comprises a first portion;

an analyzer module segmenting said first portion out of said first image;

said analyzer module determining Z-valuation for a parameter for said first portion with respect to said first image;

wherein said Z-valuation for said parameter for said first portion with respect to said first image is based on unsharp or soft class boundary or fuzzy membership function;

said analyzer module processing and resolving ambiguities in said first image to a candidate image, from a candidate image database, based on said Z-valuation for said parameter for said first portion with respect to said first image.

2. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is a still image.

3. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is from a video or multimedia sequence or frame.

4. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is a medical image.

5. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is a satellite image.

6. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is a biometrics image.

7. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of an object.

8. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of an airplane.

9. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of a fingerprint.

10. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of an iris.

11. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of an eye.

12. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of a person.

13. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of a face.

14. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of a text.

15. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of a signature.

16. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said first image is an image of a car.

17. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said Z-valuation is reliability of data.

18. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said Z-valuation is certainty of data.

19. The method for analyzing or resolving ambiguities in an image for object or pattern recognition as recited in claim 1, wherein said Z-valuation is confidence of data.

20. A method for analyzing or resolving ambiguities in an image for object or pattern recognition, said method comprising:

an input device receiving a first image;

wherein said first image comprises a first portion;

an analyzer module selecting said first portion out of said first image;

said analyzer module determining Z-valuation for a parameter for said first portion with respect to said first image;

wherein said Z-valuation for said parameter for said first portion with respect to said first image is based on unsharp or soft class boundary or fuzzy membership function;

said analyzer module selecting a candidate image, from a candidate image database, for said first image, based on said Z-valuation for said parameter for said first portion with respect to said first image.