Liwen W Lin, 56Geneva, IL

2019031, Oct 17, 2019

Jun 24, 2019

16/450876

- Culver City CA, US
Liwen Lin - Los Angeles CA, US
Mustafa Jaber - Los Angeles CA, US

Nant Holdings IP, LLC - Culver City CA

G06K 9/46
G06K 9/52
G06K 9/62
G06T 3/00
G06T 3/40

Techniques are provided that include identifying robust features within a training image. Training features are generated by applying a feature detection algorithm to the training image, each training feature having a training feature location within the training image. At least a portion of the training image is transformed into a transformed image in accordance with a predefined image transformation. Transform features are generated by applying the feature detection algorithm to the transformed image, each transform feature having a transform feature location within the transformed image. The training feature locations of the training features are mapped to corresponding training feature transformed locations within the transformed image in accordance with the predefined image transformation, and a robust feature set is compiled by selecting robust features, wherein each robust feature represents a training feature having a training feature transformed location proximal to a transform feature location of one of the transform features.

2017026, Sep 14, 2017

Mar 7, 2017

15/452644

- Culver City CA, US
Liwen Lin - Los Angeles CA, US

Nant Holdings IP, LLC - Culver City CA

G06T 11/00
G06T 3/00
G06K 9/46
G06T 7/33
G06K 9/62

Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.

2017009, Mar 30, 2017

Dec 14, 2016

15/379111

- Culver City CA, US
Liwen Lin - Los Angeles CA, US
Mustafa Jaber - Los Angeles CA, US

Nant Holdings IP, LLC - Culver City CA

G06K 9/46
G06K 9/62

Techniques are provided that include identifying robust features within a training image. Training features are generated by applying a feature detection algorithm to the training image, each training feature having a training feature location within the training image. At least a portion of the training image is transformed into a transformed image in accordance with a predefined image transformation. Transform features are generated by applying the feature detection algorithm to the transformed image, each transform feature having a transform feature location within the transformed image. The training feature locations of the training features are mapped to corresponding training feature transformed locations within the transformed image in accordance with the predefined image transformation, and a robust feature set is compiled by selecting robust features, wherein each robust feature represents a training feature having a training feature transformed location proximal to a transform feature location of one of the transform features.

2016025, Sep 8, 2016

Mar 7, 2016

15/063209

- Culver City CA, US
Liwen Lin - Los Angeles CA, US

Nant Holdings IP, LLC - Culver City CA

G06F 17/30
G06K 9/62

Techniques are provided that include receiving one or more global signatures for a query image in response to an image recognition query, wherein some of the plurality of global signatures are generated using local descriptors corresponding to different cropped versions of the image. A ranking order is determined for a plurality of document images based on nearest neighbor relations between document signatures corresponding to the plurality of document images and each one of the one or more global signatures for the query image. A subset of the plurality of document images is selected based on the determined ranking order. Additional document data corresponding to the selected subset of the plurality of document images is obtained, and a search result is generated based on a geometric verification between the additional document data corresponding to the selected subset of the plurality of document images and the query image.