Jeff L Graham

US Patent:

20230005238, Jan 5, 2023

Filed:

Sep 13, 2022

Appl. No.:

17/943922

Inventors:

- Dallas TX, US
Rajkiran K. GOTTUMUKKAL - Houston TX, US
Kishor Adinath SAITWAL - Houston TX, US
Ming-Jung SEOW - The Woodlands TX, US
Gang XU - Katy TX, US
Lon W. RISINGER - Katy TX, US
Jeff GRAHAM - League City TX, US

Assignee:

Intellective Ai, Inc. - Dallas TX

International Classification:

G06V 10/40
G06V 10/25
G06V 10/46
H04N 7/18
G06V 10/50

Abstract:

Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.

US Patent:

20210042556, Feb 11, 2021

Filed:

Jul 17, 2020

Appl. No.:

16/931921

Inventors:

- Dallas TX, US
Rajkiran K. GOTTUMUKKAL - Houston TX, US
Kishor Adinath SAITWAL - Houston TX, US
Ming-Jung SEOW - The Woodlands TX, US
Gang XU - Katy TX, US
Lon W. RISINGER - Katy TX, US
Jeff GRAHAM - League City TX, US

Assignee:

Intellective Ai, Inc. - Dallas TX

International Classification:

G06K 9/46
G06K 9/32
G06K 9/48
H04N 7/18

Abstract:

Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particularly objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specify object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups object into object type clusters based on the micro-feature vectors.

US Patent:

20190180135, Jun 13, 2019

Filed:

Jul 12, 2018

Appl. No.:

16/033264

Inventors:

- Houston TX, US
Rajkiran K. GOTTUMUKKAL - Houston TX, US
Kishor Adinath SAITWAL - Houston TX, US
Ming-Jung SEOW - Houston TX, US
Gang XU - Katy TX, US
Lon W. RISINGER - Katy TX, US
Jeff GRAHAM - League City TX, US

Assignee:

Omni AI, Inc. - Houston TX

International Classification:

G06K 9/46
H04N 7/18
G06K 9/48
G06K 9/32

Abstract:

Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.

US Patent:

20180032834, Feb 1, 2018

Filed:

Mar 16, 2017

Appl. No.:

15/461139

Inventors:

- Dallas TX, US
Rajkiran K. GOTTUMUKKAL - Houston TX, US
Kishor Adinath SAITWAL - Houston TX, US
Ming-Jung SEOW - Houston TX, US
Gang XU - Katy TX, US
Lon W. RISINGER - Katy TX, US
Jeff GRAHAM - League City TX, US

International Classification:

G06K 9/46

Abstract:

Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.