ASHISH KAPOOR
Pilots at 11 Ave, Kirkland, WA
License number
Washington A4883189
Issued Date
Dec 2013
Expiration Date
Dec 2018
Category
Airmen
Type
Authorized Aircraft Instructor
Address
Address
137 11Th Ave, Kirkland, WA 98033
Personal information
See more information about ASHISH KAPOOR at radaris.comName
Address
Phone
137 11Th Ave, Kirkland, WA 98033
558 Central Way, Kirkland, WA 98033
Kirkland, WA
Professional information
Assisted Face Recognition Tagging
US Patent:
8325999, Dec 4, 2012
Filed:
Jun 8, 2009
Appl. No.:
12/479879
Inventors:
Ashish Kapoor - Kirkland WA, US
Gang Hua - Kirkland WA, US
Amir Akbarzadeh - Bellevue WA, US
Simon J. Baker - Medina WA, US
Gang Hua - Kirkland WA, US
Amir Akbarzadeh - Bellevue WA, US
Simon J. Baker - Medina WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06K 9/00
US Classification:
382118, 382224, 382229
Abstract:
The described implementations relate to assisted face recognition tagging of digital images, and specifically to context-driven assisted face recognition tagging. In one case, context-driven assisted face recognition tagging (CDAFRT) tools can access face images associated with a photo gallery. The CDAFRT tools can perform context-driven face recognition to identify individual face images at a specified probability. In such a configuration, the probability that the individual face images are correctly identified can be higher than attempting to identify individual face images in isolation.
Generalized Active Learning
US Patent:
2010033, Dec 30, 2010
Filed:
Jun 24, 2009
Appl. No.:
12/490449
Inventors:
Ashish Kapoor - Kirkland WA, US
Eric Horvitz - Kirkland WA, US
Eric Horvitz - Kirkland WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06N 5/04, G06N 7/02
US Classification:
706 12, 706 52
Abstract:
Active learning is extended to decisions on information acquisition of both missing labels and missing features within one or more cases. In one example, desired (e.g., optimal) information to acquire about a case at hand and about cases in a training library during diagnostic sessions can be computed concurrently. A joint distribution of variables, comprising observed and unobserved labels and features for one or more cases, is modeled and probability distributions are determined for unobserved variables. An unobserved variable is selected from the joint distribution that has a return on information (ROI) metric having a combination of a desired uncertainty metric for a value of the unobserved variable and a desired cost for observing the value of the unobserved variable. The value of the variable is observed, and the probability distributions for the respective unobserved variables in the joint distribution are updated using the value of the identified variable.
Image Tagging Based Upon Cross Domain Context
US Patent:
8645287, Feb 4, 2014
Filed:
Feb 4, 2010
Appl. No.:
12/699889
Inventors:
Simon John Baker - Medina WA, US
Ashish Kapoor - Kirkland WA, US
Gang Hua - Los Angeles CA, US
Dahua Lin - Cambridge MA, US
Ashish Kapoor - Kirkland WA, US
Gang Hua - Los Angeles CA, US
Dahua Lin - Cambridge MA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 17/00
US Classification:
706 11, 706 12, 706 52
Abstract:
A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.
Exploring Data Using Multiple Machine-Learning Models
US Patent:
8595153, Nov 26, 2013
Filed:
Jun 9, 2010
Appl. No.:
12/797395
Inventors:
Steven Mark Drucker - Bellevue WA, US
Kayur Dushyant Patel - Seattle WA, US
Desney S. Tan - Kirkland WA, US
Ashish Kapoor - Kirkland WA, US
James Anthony Fogarty - Seattle WA, US
Kayur Dushyant Patel - Seattle WA, US
Desney S. Tan - Kirkland WA, US
Ashish Kapoor - Kirkland WA, US
James Anthony Fogarty - Seattle WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06N 5/00
US Classification:
706 12, 706 45
Abstract:
A multiple model data exploration system and method for running multiple machine-learning models simultaneously to understand and explore data. Embodiments of the system and method allow a user to gain a greater understanding of the data and to gain new insights into their data. Embodiments of the system and method also allow a user to interactively explore the problem and to navigate different views of data. Many different classifier training and evaluation experiments are run simultaneously and results are obtained. The results are aggregated and visualized across each of the experiments to determine and understand how each example is classified for each different classifier. These results then are summarized in a variety of ways to allow users to obtain a greater understanding of the data both in terms of the individual examples themselves and features associated with the data.
Visualizing Predicted Affective States Over Time
US Patent:
2013020, Aug 8, 2013
Filed:
Feb 3, 2012
Appl. No.:
13/365265
Inventors:
Ashish Kapoor - Kirkland WA, US
Amy Karlson - Bellevue WA, US
Mary P. Czerwinski - Kikland WA, US
Asta Roseway - Bellevue WA, US
Daniel Jonathan McDuff - Cambridge WA, US
Amy Karlson - Bellevue WA, US
Mary P. Czerwinski - Kikland WA, US
Asta Roseway - Bellevue WA, US
Daniel Jonathan McDuff - Cambridge WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 19/00
US Classification:
702 19
Abstract:
Described herein are various technologies pertaining to estimating affective states of a user by way of monitoring data streams output by sensors and user activity on a computing device. Models of valence, arousal, and engagement can be learned during a training phase, and such models can be employed to compute values that are indicative of valence, arousal, and engagement of a user in near-real time. A visualization that represents estimated affective states of a user over time is generated to facilitate user reflection.
Image Quality Assessment
US Patent:
2013029, Nov 7, 2013
Filed:
Jul 3, 2013
Appl. No.:
13/935384
Inventors:
Ashish KAPOOR - Kirkland WA, US
Huixuan TANG - Toronto, CA
Huixuan TANG - Toronto, CA
International Classification:
G06K 9/46
US Classification:
382192
Abstract:
Methods and systems for image quality assessment are disclosed. A method includes accessing an image, identifying features of the image, assessing the features and generating subjective scores for the features based upon a mapping of the features to the subjective scores and based on the subjective scores, generating an image quality score. Access is provided to the image quality score.
Interactive Optimization Of The Behavior Of A System
US Patent:
2011025, Oct 13, 2011
Filed:
Apr 8, 2010
Appl. No.:
12/756183
Inventors:
Ashish Kapoor - Kirkland WA, US
Bongshin Lee - Issaquah WA, US
Desney S. Tan - Kirkland WA, US
Eric J. Horvitz - Kirkland WA, US
Bongshin Lee - Issaquah WA, US
Desney S. Tan - Kirkland WA, US
Eric J. Horvitz - Kirkland WA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
G06F 15/18, G06N 5/02, G06F 1/24
US Classification:
706 12, 713100, 706 52
Abstract:
An interactive tool is described for modifying the behavior of a system, such as, but not limited to, the behavior of a classification system. The tool uses an interface mechanism to present a current global state of the system. The tool accepts one or more refinements to this global state, e.g., by accepting individual changes to parameter settings that are presented by the interface mechanism. Based on this input, the tool computes and displays the global implications of the updated parameter settings. The process of iterating over one or more cycles of user updates, followed by computation and display of the implications of the attempted refinements, has the effect of advancing the system towards a global state that exhibits desirable behavior.
Image Quality Assessment
US Patent:
8494283, Jul 23, 2013
Filed:
Dec 21, 2010
Appl. No.:
12/975026
Inventors:
Neel Joshi - Seattle WA, US
Ashish Kapoor - Kirkland WA, US
Huixuan Tang - Toronto, CA
Ashish Kapoor - Kirkland WA, US
Huixuan Tang - Toronto, CA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06K 9/46
US Classification:
382190, 382191
Abstract:
Methods and systems for image quality assessment are disclosed. A method includes accessing an image, identifying features of the image, assessing the features and generating subjective scores for the features based upon a mapping of the features to the subjective scores and based on the subjective scores, generating an image quality score. Access is provided to the image quality score.
Non-Linguistic Signal Detection And Feedback
US Patent:
2011029, Dec 1, 2011
Filed:
May 27, 2010
Appl. No.:
12/789142
Inventors:
Byungki Byun - Atlanta GA, US
Philip A. Chou - Bellevue WA, US
Mary P. Czerwinski - Kirkland WA, US
Ashish Kapoor - Kirkland WA, US
Bongshin Lee - Issaquah WA, US
Philip A. Chou - Bellevue WA, US
Mary P. Czerwinski - Kirkland WA, US
Ashish Kapoor - Kirkland WA, US
Bongshin Lee - Issaquah WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
H04N 7/14
US Classification:
348 1408, 348E07077
Abstract:
Non-linguistic signal information relating to one or more participants to an interaction may be determined using communication data received from the one or more participants. Feedback can be provided based on the determined non-linguistic signals. The participants may be given an opportunity to opt in to having their non-linguistic signal information collected, and may be provided complete control over how their information is shared or used.
Resource-Aware Computer Vision
US Patent:
2011030, Dec 15, 2011
Filed:
Jun 9, 2010
Appl. No.:
12/796686
Inventors:
Ashish Kapoor - Kirkland WA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
G06K 9/46, G06K 9/34
US Classification:
382174, 382190
Abstract:
The described implementations relate to computer vision. In one case image data is received. Resource constraints associated with processing the image data are evaluated. Further, a visual recognition and detection processing strategy is selected for the image data based at least in part on the evaluated resource constraints.