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公开(公告)号：US20170262737A1
公开(公告)日：2017-09-14
申请号：US15457990
申请日：2017-03-13
Applicant: Magic Leap, Inc.
Inventor： Andrew Rabinovich , Vijay Badrinarayanan , Daniel DeTone , Srivignesh Rajendran , Douglas Bertram Lee , Tomasz Malisiewicz
CPC classification number: G06K9/66 , G06K9/4628 , G06K9/6267 , G06K9/6272
Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
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公开(公告)号：US10963758B2
公开(公告)日：2021-03-30
申请号：US16366047
申请日：2019-03-27
Applicant: MAGIC LEAP, INC.
Inventor： Andrew Rabinovich , Vijay Badrinarayanan , Daniel Detone , Srivignesh Rajendran , Douglas Bertram Lee , Tomasz Malisiewicz
Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
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公开(公告)号：US11776242B2
公开(公告)日：2023-10-03
申请号：US16973971
申请日：2019-06-14
Applicant: MAGIC LEAP, INC.
Inventor： Douglas Bertram Lee
IPC: G06V40/20 , G06V10/82 , G06F18/214 , G06F18/231 , G06F18/243 , G06V10/44 , G06V20/10 , G06V40/10
CPC classification number: G06V10/82 , G06F18/214 , G06F18/231 , G06F18/24323 , G06V40/28 , G06V10/454 , G06V20/10 , G06V40/107
Abstract: A computer implemented method for recognizing a hand gesture using a random forest model includes training the random forest model. The method also includes obtaining image data. The method further includes clustering a plurality of pixels from the image data to generate a plurality of clusters. Moreover, the method includes analyzing the plurality of clusters using a rejection cascade to generate a plurality of selected candidates. In addition, the method includes analyzing the plurality of selected candidates using a classification decision tree from the random forest model. The method also includes skeletonizing the plurality of selected candidates to generate a one dimension plus branches hand model. The method further includes analyzing the one dimension plus branches hand model using a regression decision tree from the random forest model.
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公开(公告)号：US20210182636A1
公开(公告)日：2021-06-17
申请号：US17183021
申请日：2021-02-23
Applicant: MAGIC LEAP, INC.
Inventor： Andrew Rabinovich , Vijay Badrinarayanan , Daniel DeTone , Srivignesh Rajendran , Douglas Bertram Lee , Tomasz Malisiewicz
Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
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公开(公告)号：US10977820B2
公开(公告)日：2021-04-13
申请号：US16880752
申请日：2020-05-21
Applicant: Magic Leap, Inc.
Inventor： Adrian Kaehler , Douglas Bertram Lee , Vijay Badrinarayanan
IPC: G06T19/00 , G06T7/70 , G06T7/20 , G06F3/01 , G06F3/0481 , G06N3/02 , G02B27/01 , G06F3/0346 , G06T7/246 , G06F1/16 , G02B27/00
Abstract: Disclosed herein is a wearable display system for capturing retraining eye images of an eye of a user for retraining a neural network for eye tracking. The system captures retraining eye images using an image capture device when user interface (UI) events occur with respect to UI devices displayed at display locations of a display. The system can generate a retraining set comprising the retraining eye images and eye poses of the eye of the user in the retraining eye images (e.g., related to the display locations of the UI devices) and obtain a retrained neural network that is retrained using the retraining set.
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Patent Agency Ranking
公开(公告)号：US20200286251A1
公开(公告)日：2020-09-10
申请号：US16880752
申请日：2020-05-21
Applicant: Magic Leap, Inc.
Inventor： Adrian Kaehler , Douglas Bertram Lee , Vijay Badrinarayanan
IPC: G06T7/70 , G06T7/20 , G06F3/01 , G06F3/0481 , G06N3/02 , G02B27/01 , G06F3/0346 , G06T7/246 , G06F1/16 , G02B27/00
Abstract: Disclosed herein is a wearable display system for capturing retraining eye images of an eye of a user for retraining a neural network for eye tracking. The system captures retraining eye images using an image capture device when user interface (UI) events occur with respect to UI devices displayed at display locations of a display. The system can generate a retraining set comprising the retraining eye images and eye poses of the eye of the user in the retraining eye images (e.g., related to the display locations of the UI devices) and obtain a retrained neural network that is retrained using the retraining set.
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公开(公告)号：US09832437B2
公开(公告)日：2017-11-28
申请号：US14992958
申请日：2016-01-11
Applicant: Magic Leap, Inc.
Inventor： Michael Kass , Douglas Bertram Lee
CPC classification number: H04N9/3182 , G06F3/01 , G06F3/013 , H04N9/3111 , H04N9/312 , H04N9/3155 , H04N9/3164 , H04N9/67
Abstract: Described are improved approaches to implement color sequential displays that can mitigate problems with conventional display technologies. Color-breakup is mitigated by modifying the original color channels and adding one or more additional color channels derived from the original ones.
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公开(公告)号：US11775836B2
公开(公告)日：2023-10-03
申请号：US16879736
申请日：2020-05-20
Applicant: MAGIC LEAP, INC.
Inventor： Prajwal Chidananda , Ayan Tuhinendu Sinha , Adithya Shricharan Srinivasa Rao , Douglas Bertram Lee , Andrew Rabinovich
IPC: G06N3/084 , G06F3/01 , G06V40/10 , G06N3/045 , G06V10/764 , G06V10/778 , G06V10/44 , G06V40/20
CPC classification number: G06N3/084 , G06F3/017 , G06N3/045 , G06V10/454 , G06V10/764 , G06V10/7784 , G06V40/11 , G06V40/113 , G06V40/20 , G06V40/117
Abstract: A neural network in multi-task deep learning paradigm for machine vision includes an encoder that further includes a first, a second, and a third tier. The first tier comprises a first-tier unit having one or more first-unit blocks. The second tier receives a first-tier output from the first tier at one or more second-tier units in the second tier, a second-tier unit comprises one or more second-tier blocks, the third tier receives a second-tier output from the second tier at one or more third-tier units in the third tier, and a third-tier block comprises one or more third-tier blocks. The neural network further comprises a decoder operatively the encoder to receive an encoder output from the encoder as well as one or more loss function layers that are configured to backpropagate one or more losses for training at least the encoder of the neural network in a deep learning paradigm.
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公开(公告)号：US11657286B2
公开(公告)日：2023-05-23
申请号：US17183021
申请日：2021-02-23
Applicant: MAGIC LEAP, INC.
Inventor： Andrew Rabinovich , Vijay Badrinarayanan , Daniel DeTone , Srivignesh Rajendran , Douglas Bertram Lee , Tomasz Malisiewicz
IPC: G06K9/00 , G06V30/194 , G06N3/082 , G06V10/44 , G06F18/24 , G06F18/2413 , G06N3/045
CPC classification number: G06V30/194 , G06F18/24 , G06F18/24137 , G06N3/045 , G06N3/082 , G06V10/454
Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
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公开(公告)号：US20210327085A1
公开(公告)日：2021-10-21
申请号：US17221250
申请日：2021-04-02
Applicant: Magic Leap, Inc.
Inventor： Adrian Kaehler , Douglas Bertram Lee , Vijay Badrinarayanan
IPC: G06T7/70 , G06T7/20 , G06F3/01 , G06F3/0481 , G06N3/02 , G02B27/01 , G06F3/0346 , G06T7/246 , G06F1/16 , G02B27/00
Abstract: Disclosed herein is a wearable display system for capturing retraining eye images of an eye of a user for retraining a neural network for eye tracking. The system captures retraining eye images using an image capture device when user interface (UI) events occur with respect to UI devices displayed at display locations of a display. The system can generate a retraining set comprising the retraining eye images and eye poses of the eye of the user in the retraining eye images (e.g., related to the display locations of the UI devices) and obtain a retrained neural network that is retrained using the retraining set.
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