David Mckinnon is a true Florida Native. He has lived in the Tampa Bay area for the last 43 years. David is happily married to his beautiful wife of 20 years and the proud father of 2 teenagers. David enjoys the beach and sunsets with His wife and surfing and coaching soccer with His Kids. David has mentored teens for 20 years and volunteers at both of his children's schools. Real Estate is David's passion and He loves his work. Buying or selling or looking for an investment property, David is there to guide you through the process hassle free. call or email David Mckinnon today!
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David McKinnon
Baymac Electrical Systems Ltd Electricians
Po Box 508 Stn Postal Box Ctr, Red Deer, AB T4N 5G1 (403)3461299, (403)7842577
Systems and methods for generating pixel based depth estimates are disclosed. An image processing system operating as depth analysis engine generates an estimated depth associated with a pixel based on a reference image and other related images. A current depth estimate is refined based on neighboring pixels and calculated consistency scores. Further, depth estimates can be levered in object or scene recognition to trigger or initiate an action taken by a computing device.
Duane M. McKinnon - Chino CA David A. McKinnon - Upland CA
International Classification:
A47H 2305
US Classification:
160332
Abstract:
A flexible multi-strip closure is supported in a vertically hanging position by means on horizontally oriented structural beads attached to each strip, which beads are retained in a horizontally oriented, inverted U-shape channel which includes an entrapment portion for supporting the beads against vertical forces and at least one lip to eliminate stress concentrations in the flexible strips when they are flexed. Releasable means are provided adjacent at least one end of the channel to restrict horizontal movement of the beads and to allow removal of the beads with their attached strips for cleaning and replacement.
Multiparty object recognition systems and methods are disclosed. A method of interactively manipulating virtual object data, wherein an object database is configured to store first party object data that corresponds to a first real-world object and is further configured to store second party object data that corresponds to a second real-world object, includes obtaining the first party object data and the second party object data for storage within the object database. Access to the object database is controlled such that the first party object data and the second party object data is accessible to the first party and the second party. Modification of the first party object data by the second party is facilitated to generate modified first party object data that is in accordance with at least one context parameter of the second party object data, and the modified first party object data is communicated to the first party.
- Culver City CA, US David McKinnon - Culver City CA, US
Assignee:
Nant Holdings IP, LLC - Culver City CA
International Classification:
G06K 9/62 G06F 17/27 G06F 17/30
Abstract:
Systems and methods of generating a compact visual vocabulary are provided. Descriptor sets related to digital representations of objects are obtained, clustered and partitioned into cells of a descriptor space, and a representative descriptor and index are associated with each cell. Generated visual vocabularies could be stored in client-side devices and used to obtain content information related to objects of interest that are captured.
Fast Recognition Algorithm Processing, Systems And Methods
- Culver City CA, US Bing Song - La Canada CA, US Matheen Siddiqui - Culver City CA, US David McKinnon - Venice CA, US Jeremi Sudol - Los Angeles CA, US Patrick Soon-Shiong - Los Angeles CA, US Orang Dialameh - Santa Monica CA, US
Systems and methods of quickly recognizing or differentiating many objects are presented. Contemplated systems include an object model database storing recognition models associated with known modeled objects. The object identifiers can be indexed in the object model database based on recognition features derived from key frames of the modeled object. Such objects are recognized by a recognition engine at a later time. The recognition engine can construct a recognition strategy based on a current context where the recognition strategy includes rules for executing one or more recognition algorithms on a digital representation of a scene. The recognition engine can recognize an object from the object model database, and then attempt to identify key frame bundles that are contextually relevant, which can then be used to track the object or to query a content database for content information.
Invariant-Based Dimensional Reduction Of Object Recognition Features, Systems And Methods
- Culver City CA, US Jeremi Sudol - Los Angeles CA, US Bing Song - La Canada CA, US Matheen Siddiqui - Culver City CA, US David McKinnon - Culver City CA, US
Assignee:
Nant Holdings IP, LLC - Culver City CA
International Classification:
G06K 9/62 G06K 9/46
Abstract:
A sensor data processing system and method is described. Contemplated systems and methods derive a first recognition trait of an object from a first data set that represents the object in a first environmental state. A second recognition trait of the object is then derived from a second data set that represents the object in a second environmental state. The sensor data processing systems and methods then identifies a mapping of elements of the first and second recognition traits in a new representation space. The mapping of elements satisfies a variance criterion for corresponding elements, which allows the mapping to be used for object recognition. The sensor data processing systems and methods described herein provide new object recognition techniques that are computationally efficient and can be performed in real-time by the mobile phone technology that is currently available.
Fast Recognition Algorithm Processing, Systems And Methods
- Culver City CA, US Bing Song - La Canada CA, US Matheen Siddiqui - Culver City CA, US David McKinnon - Venice CA, US Jeremi Sudol - Los Angeles CA, US Patrick Soon-Shiong - Los Angeles CA, US Orang Dialameh - Santa Monica CA, US
Systems and methods of quickly recognizing or differentiating many objects are presented. Contemplated systems include an object model database storing recognition models associated with known modeled objects. The object identifiers can be indexed in the object model database based on recognition features derived from key frames of the modeled object. Such objects are recognized by a recognition engine at a later time. The recognition engine can construct a recognition strategy based on a current context where the recognition strategy includes rules for executing one or more recognition algorithms on a digital representation of a scene. The recognition engine can recognize an object from the object model database, and then attempt to identify key frame bundles that are contextually relevant, which can then be used to track the object or to query a content database for content information.
Fast Recognition Algorithm Processing, Systems And Methods
- Culver City CA, US Bing Song - La Canada CA, US Matheen Siddiqui - Culver City CA, US David McKinnon - Venice CA, US Jeremi Sudol - Los Angeles CA, US Patrick Soon-Shiong - Los Angeles CA, US Orang Dialameh - Santa Monica CA, US
Assignee:
Nant Holdings IP, LLC - Culver City CA
International Classification:
G06K 9/00 G06T 11/60
Abstract:
Systems and methods of quickly recognizing or differentiating many objects are presented. Contemplated systems include an object model database storing recognition models associated with known modeled objects. The object identifiers can be indexed in the object model database based on recognition features derived from key frames of the modeled object. Such objects are recognized by a recognition engine at a later time. The recognition engine can construct a recognition strategy based on a current context where the recognition strategy includes rules for executing one or more recognition algorithms on a digital representation of a scene. The recognition engine can recognize an object from the object model database, and then attempt to identify key frame bundles that are contextually relevant, which can then be used to track the object or to query a content database for content information.