Intel Corporation

Nike, Inc.

Intel Corproation

Nike Innovate C.v.

Manan Goel

Anthony Rhodes

Lakshman Krishnamurthy

Saurin Shah

Bradley W Wilkins

Swarnendu Kar

John Michael Schmitt

Corey Dow-Hygelund

Santoshkumar Balakrishnan

Kate Cummings

Jeff Hazel

David Switzer

Michael Lapinsky

Ke Ding

Steven Xing

Sebastian Imlay

Peter Laigar

Kevin James Doucette

Matthew Pinner

Christopher Andon

Mario Angelbert Lafortune

Narayan Sundararajan

Brian Kenneth Vogel

Jason Blanchard

Jason Wright

Francis M Tharappel

Suresh V Golwalkar

Mirza Sabanovic

Bobby LeGaye

Andrew Owenson

Brad Wilkins

Arthur D Webb

Mario LaFortune

Randal J Marsden

Christopher L Andon

Kathleen Cummings

Techniques related to automatically segmenting video frames into per pixel fidelity object of interest and background regions are discussed. Such techniques include applying tessellation to a video frame to generate feature frames corresponding to the video frame and applying a segmentation network implementing context aware skip connections to an input volume including the feature frames and a context feature volume corresponding to the video frame to generate a segmentation for the video frame.

High fidelity interactive segmentation for video data with deep convolutional tessellations and context aware skip connections

Gesture-controlled virtual reality systems and methods of controlling the same are disclosed herein. An example apparatus includes an on-body sensor to output first signals associated with at least one of movement of a body part of a user or a position of the body part relative to a virtual object and an off-body sensor to output second signals associated with at least one of the movement or the position relative to the virtual object. The apparatus also includes at least one processor to generate gesture data based on at least one of the first or second signals, generate position data based on at least one of the first or second signals, determine an intended action of the user relative to the virtual object based on the position data and the gesture data, and generate an output of the virtual object in response to the intended action.

Gesture-controlled virtual reality systems and methods of controlling the same

Methods, systems, and apparatus for high-fidelity vision tasks using deep neural networks are disclosed. An example apparatus includes a feature extractor to extract low-level features and edge-enhanced features of an input image processed using a convolutional neural network, an eidetic memory block generator to generate an eidetic memory block using the extracted low-level features or the extracted edge-enhanced features, and an interactive segmentation network to perform image segmentation using the eidetic memory block, the eidetic memory block used to propagate domain-persistent features through the segmentation network.

Methods and apparatus for high-fidelity vision tasks using deep neural networks

Various embodiments are generally directed to techniques for image segmentation utilizing context, such as with a machine learning (ML) model that injects context into various training stages. Many embodiments utilize one or more of an encoder-decoder model topology and select criteria and parameters in hyper-parameter optimization (HPO) to conduct the best model neural architecture search (NAS). Some embodiments are particularly directed to resizing context frames to a resolution that corresponds with a particular stage of decoding. In several embodiments, the context frames are concatenated with one or more of data from a previous decoding stage and data from a corresponding encoding stage prior to being provided as input to a next decoding stage.

Techniques for interactive image segmentation networks

A method of displaying a cryptographically secured digital collectable on a digital display includes receiving the cryptographically secured digital collectable; receiving an indication from a user to display the cryptographically secured digital collectable on the digital display provided on an article of footwear or apparel; receiving an article identifier from communication circuitry provided on the article of footwear in response to receiving the indication to display the digital collectable on the article of footwear or apparel; transmitting the cryptographically secured digital collectable to the article of footwear or apparel for display on the digital display; and recording or requesting the recordation that the cryptographically secured digital collectable is displayed in connection with the article identifier of the article of footwear or apparel.

Wearable article with integrated display for displaying cryptographically secured digital images

Methods, apparatus, systems, and articles of manufacture are disclosed for interactive image segmentation. An example apparatus includes an inception controller to execute an inception sublayer of a convolutional neural network (CNN) including two or more inception-atrous-collation (IAC) layers, the inception sublayer including two or more convolutions including respective kernels of varying sizes to generate multi-scale inception features, the inception sublayer to receive one or more context features indicative of user input; an atrous controller to execute an atrous sublayer of the CNN, the atrous sublayer including two or more atrous convolutions including respective kernels of varying sizes to generate multi-scale atrous features; and a collation controller to execute a collation sublayer of the CNN to collate the multi-scale inception features, the multi-scale atrous features, and eidetic memory features.

Methods, apparatus, and articles of manufacture for interactive image segmentation

Methods, systems and apparatuses may provide for technology that trains a neural network by inputting video data to the neural network, determining a boundary loss function for the neural network, and selecting weights for the neural network based at least in part on the boundary loss function, wherein the neural network outputs a pixel-level segmentation of one or more objects depicted in the video data. The technology may also operate the neural network by accepting video data and an initial feature set, conducting a tensor decomposition on the initial feature set to obtain a reduced feature set, and outputting a pixel-level segmentation of object(s) depicted in the video data based at least in part on the reduced feature set.

High resolution interactive video segmentation using latent diversity dense feature decomposition with boundary loss

An athletic performance monitoring system, including a gesture recognition processor configured to execute gesture recognition processes. Interaction with the athletic performance monitoring system may be based, at least in part, on gestures performed by a user, and may offer an alternative to making selections on the athletic performance monitoring system using physical buttons, which may be cumbersome and/or inconvenient to use while performing an athletic activity. Additionally, recognized gestures may be used to select one or more operational modes for the athletic performance monitoring system, such that a reduction in power consumption may be achieved.

Gesture recognition

Techniques related to automatically segmenting a video frame into fine grain object of interest and background regions using a ground truth segmentation of an object in a previous frame are discussed. Such techniques apply multiple levels of segmentation tracking and prediction based on color, shape, and motion of the segmentation to determine per-pixel object probabilities, and solve an energy summation model to generate a final segmentation for the video frame using the object probabilities.

Fine-grain object segmentation in video with deep features and multi-level graphical models

An apparatus, method, system and computer readable medium for video tracking. An exemplar crop is selected to be tracked in an initial frame of a video. Bayesian optimization is applied with each subsequent frame of the video by building a surrogate model of an objective function using Gaussian Process Regression (GPR) based on similarity scores of candidate crops collected from a search space in a current frame of the video. A next candidate crop in the search space is determined using an acquisition function. The next candidate crop is compared to the exemplar crop using a Siamese neural network. Comparisons of new candidate crops to the exemplar crop are made using the Siamese neural network until the exemplar crop has been found in the current frame. The new candidate crops are selected based on an updated surrogate model.

Video tracking with deep Siamese networks and Bayesian optimization

Techniques related to automatically segmenting video frames into per pixel dense object of interest and background regions are discussed. Such techniques include applying a segmentation convolutional neural network (CNN) to a CNN input including a current video frame, a previous video frame, an object of interest indicator frame, a motion frame, and multiple feature frames each including features compressed from feature layers of an object classification convolutional neural network as applied to the current video frame to generate candidate segmentations and selecting one of the candidate segmentations as a final segmentation of the current video frame.

Deep learning for dense semantic segmentation in video with automated interactivity and improved temporal coherence

A wrist-worn athletic performance monitoring system, including a gesture recognition processor configured to execute gesture recognition processes. Interaction with the performance monitoring system may be based, at least in part, on gestures performed by the user, and offer an alternative to making selections on the performance monitoring system using physical buttons, which may be cumbersome and/or inconvenient to use while performing an athletic activities. Additionally, recognized gestures may be used to select one or more operational modes for the athletic performance monitoring systems such that a reduction in power consumption may be achieved.

Growing community of inventors

Beaverton, OR, United States of America

Manan Goel