Qualcomm Incorporated

Apple Inc.

Siemens Aktiengesellschaft

Siemens Corporate Research, Inc.

Siemens Corporation

Yanghai Tsin

Seyed Hesameddin Najafi Shoushtari

Yakup Genc

Kuen-Han Lin

Murali Chari

Visvanathan Ramesh

Kiyoung Kim

Peter Kaufmann

Jonathan P Ive

Alan C Dye

Stephen Olivier Lemay

Julian Jaede

Fletcher R Rothkopf

Cheng Kai Chen

Jeremy C Franklin

Jan Boese

William A Sorrentino, Iii

Daniel R Kurz

Raghuraman Krishnamoorthi

Hui Chao

Frank Sauer

Jason C Sauers

Ashwin Deepak Swaminathan

Rui Liao

Geoffrey Grant Stahl

Wey-Jiun Lin

Arthur Yasheng Zhang

Ivan S Maric

Phil M Hobson

Günter Lauritsch

Chenyang Xu

Graham Butler Myhre

Ying Zhu

Michael J Rockwell

Forrest C Wang

Yoonhoo Jo

Muhammad F Hossain

Marinus Meursing

Jia Tao

Faraz Mohammad Mirzaei

Hari Sundar

Klaus J Kirchberg

Bertrand Nepveu

Kathrin Berkner Cieslicki

Xiang Zhang

Tom Sengelaub

Brian Amberg

Jan K Quijalvo

Dheeraj Ahuja

Julian Hoenig

Jason L Slupeiks

Prasun Choudhury

Jose Jeronimo Moreira Rodrigues

Yan Li

Thibaut Weise

Branko Petljanski

James W Vandyke

Hesam Najafi

A head-mountable display device includes a housing defining a front opening and a rear opening, a display screen disposed in the front opening, a display assembly disposed in the rear opening, a first securement strap coupled to the housing, the first securement strap including a first electronic component, a second securement strap coupled to the housing, the second securement strap including a second electronic component, and a securement band extending between and coupled to the first securement strap and the second securement strap.

Head mountable display

Methods and apparatus for glint-assisted gaze tracking in a VR/AR head-mounted display (HMD). Images of a user's eyes captured by gaze tracking cameras may be analyzed to detect glints (reflections on the cornea of light sources that illuminate the user's eyes) and the pupil. The glints are matched to particular ones of the light sources. The glint-light source matches are used to determine the cornea center of the eye, and the pupil center is determined. The optical axis of the eye is reconstructed from the cornea center and the pupil center, and the visual axis is then reconstructed from the optical axis and a 3D model of the user's eye. The point of gaze on the display is then determined based on the visual axis and a 3D model of the HMD.

Glint-assisted gaze tracker

Various implementations disclosed herein include devices, systems, and methods that use event camera data to track deformable objects such as faces, hands, and other body parts. One exemplary implementation involves receiving a stream of pixel events output by an event camera. The device tracks the deformable object using this data. Various implementations do so by generating a dynamic representation of the object and modifying the dynamic representation of the object in response to obtaining additional pixel events output by the event camera. In some implementations, generating the dynamic representation of the object involves identifying features disposed on the deformable surface of the object using the stream of pixel events. The features are determined by identifying patterns of pixel events. As new event stream data is received, the patterns of pixel events are recognized in the new data and used to modify the dynamic representation of the object.

Deformable object tracking

Disclosed embodiments pertain to apparatus, systems, and methods for robust feature based tracking. In some embodiments, a score may be computed for a camera captured current image comprising a target object. The score may be based on one or more metrics determined from a comparison of features in the current image and a prior image captured by the camera. The comparison may be based on an estimated camera pose for the current image. In some embodiments, one of a point based, an edge based, or a combined point and edge based feature correspondence method may be selected based on a comparison of the score with a point threshold and/or a line threshold, the point and line thresholds being obtained from a model of the target. The camera pose may be refined by establishing feature correspondences using the selected method between the current image and a model image.

Robust tracking using point and line features

A reference in an unknown environment is generated on the fly for positioning and tracking. The reference is produced in a top down process by capturing an image of a planar object with a predefined geometric shape, detecting edge pixels of the planar object, then detecting a plurality of line segments from the edge pixels. The plurality of line segments may then be used to detect the planar object in the image based on the predefined geometric shape. An initial pose of the camera with respect to the planar object is determined and tracked using the edges of the planar object.

Accelerated geometric shape detection and accurate pose tracking

A method for estimating camera pose includes: obtaining an image of a location captured via a camera, where the image includes a target object and edge line features outside of the target object; and calculating a pose of the camera with respect to the target object based on the edge line features.

Pose estimation based on peripheral information

In several aspects, an electronic device and method index a repository of N documents by W words, by not storing between queries, N*W numbers that are specific to each word i and each document j, normally used to compute a score of relevance to a query, of each document j. Instead, the electronic device and method generate the N*W word-specific -document-specific numbers dynamically at query time, based on a set of W numbers corresponding to the W words, and one or more sets (e.g. x sets) of N numbers corresponding to the N documents. Query-time generation of word-specific-document-specific numbers reduces memory otherwise required, e.g. to store these numbers. Hence, in certain aspects W+xN numbers are maintained between queries, and these numbers are changed incrementally when a new document is added to the set or an existing document is removed. Maintaining W+xN numbers reduces processing otherwise required, to start from scratch.

Updating index information when adding or removing documents

A method of real-time tracking of an object includes capturing a first and a second image of the object. The object is detected in the first image and movement of the object is tracked between the images. Tracking of the object includes obtaining an initial pose of the camera; projecting an image of a model object onto the second image; determining a gradient profile of the second image from an edge point of the model object along a first direction that is normal to the edge of the model object; computing a radius on the gradient profile; determining a rank order of the peaks of the gradient profile along the radius; comparing the rank order with a predetermined rank order to generate a feature candidate point; and reducing a distance along the first direction between the feature candidate point and the edge point on the edge of the model object.

Tracking texture rich objects using rank order filtering

A method for compensating respiratory motion in coronary fluoroscopic images includes finding a set of transformation parameters of a parametric motion model that maximize an objective function that is a weighted normalized cross correlation function of a reference image acquired at a first time that is warped by the parametric motion model and a first incoming image acquired at a second time subsequent to the first time. The weights are calculated as a ratio of a covariance of the gradients of the reference image and the gradients of the first incoming image with respect to a root of a product of a variance of the gradients of the reference image and the variance of the gradients of the first incoming image. The parametric motion model transforms the reference image to match the first incoming image.

System and method for image-based respiratory motion compensation for fluoroscopic coronary roadmapping

Automatic measurement of morphometric and motion parameters of a coronary target includes extracting reference frames from input data of a coronary target at different phases of a cardiac cycle, extracting a three-dimensional centerline model for each phase of the cardiac cycle based on the references frames and projection matrices of the coronary target, tracking a motion of the coronary target through the phases based on the three-dimensional centerline models, and determining a measurement of morphologic and motion parameters of the coronary target based on the motion.

Automatic measurement of morphometric and motion parameters of the coronary tree from a rotational X-ray sequence

A method wherein images of different types of objects within a class are partitioned into region stacks. For each one of the stacks, the method: (a) applies a template to extract fragments having a predetermined size and one of a plurality of different spatial orientations, to generate extracted templates; (b) determines, from the extracted templates, a most frequent one thereof having only a first number of fragments with a common spatial orientations; (c) records the number of images having the determined most frequent extracted template; (d) repeats (b) and (c) with successively increasing number of fragments until the number of recoded images falls below a threshold; and (e) selects as a master extracted template the one of the most frequent templates having the largest recorded number of fragments. The master extracted templates for the stacks are combined into a map that is then compared with background images to remove extracted templates matching segment in the background.

Method for detecting objects using flexible edge arrangements

A method for modeling a vehicle, includes: receiving an image that includes a vehicle; and constructing a three-dimensional (3D) model of the vehicle, wherein the 3D model is constructed by: (a) taking a predetermined set of base shapes that are extracted from a subset of vehicles; (b) multiplying each of the base shapes by a parameter; (c) adding the resultant of each multiplication to form a vector that represents the vehicle's shape; (d) fitting the vector to the vehicle in the image; and (e) repeating steps (a)-(d) by modifying the parameters until a difference between a fit vector and the vehicle in the image is minimized.

Active shape model for vehicle modeling and re-identification

A method of tracking a pose of a moving camera includes receiving a first image from a camera, receiving a sequence of digitized images from said camera, recording, for each of said sequence of digitized images, the pose and 2D correspondences of landmarks, reconstructing a location and appearance of a 2-dimensional texture patch from 2D correspondences of the landmarks by triangulation and optimization, computing back-projection errors by comparing said reconstructed texture patch with said first received image; and reconstructing said location and appearance of said 2-dimensional texture patch from the 2D correspondences of the landmarks of said sequence of digitized images by triangulation and optimization after eliminating those landmarks with large back-projection errors.

System and method for using texture landmarks for improved markerless tracking in augmented reality applications

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San Diego, CA, United States of America

Yanghai Tsin