Eliazar
Austin Eliazar, Morrisville, NC US
Patent application number | Description | Published |
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20120075343 | AUGMENTED REALITY (AR) SYSTEM AND METHOD FOR TRACKING PARTS AND VISUALLY CUEING A USER TO IDENTIFY AND LOCATE PARTS IN A SCENE - An AR system both identifies and visually tracks parts for a user by maintaining spatial awareness of the user's pose and provides instructions to the user for the use of those parts. Tracking the identified parts, both inside and outside the current Field of View (FOV), and any missing parts for use with the current instruction improves the effectiveness and efficiency of both novice and experienced user alike. | 03-29-2012 |
Austin Eliazar, Mountain View, CA US
Patent application number | Description | Published |
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20160055673 | DISTRIBUTED APERTURE VISUAL INERTIA NAVIGATION - A system and method for visual inertial navigation for augmented reality are described. In some embodiments, at least one camera of a wearable device generates a plurality of video frames. At least one inertial measurement unit (IMU) sensors of the wearable device generates IMU data. Features in the plurality of video frames for each camera are tracked. The plurality of video frames for each camera are synchronized and aligned based on the IMU data. A dynamic state of the wearable device is computed based on the synchronized plurality of video frames with the IMU data for each camera. Augmented reality content is generated and positioned in a display of the wearable device based on the dynamic state of the wearable device. | 02-25-2016 |
Austin I.d. Eliazar, Morrisville, NC US
Patent application number | Description | Published |
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20080243439 | Sensor exploration and management through adaptive sensing framework - The identification and tracking of objects from captured sensor data relies upon statistical modeling methods to sift through large data sets and identify items of interest to users of the system. Statistical modeling methods such as Hidden Markov Models in combination with particle analysis and Bayesian statistical analysis produce items of interest, identify them as objects, and present them to users of the system for identification feedback. The integration of a training component based upon the relative cost of sampling sensors for additional parameters, provides a system that can formulate and present policy decisions on what objects should be tracked, leading to an improvement in continuous data collection and tracking of identified objects within the sensor data set. | 10-02-2008 |
20090312985 | Multiple hypothesis tracking - Multiple hypothesis tracking is a system which enables an analytic sensor framework to capture sensor data and simultaneously account for many possible instantiations of objects, trajectories and behaviors that may be represented within the captured data. Each data instantiation is represented by a different likelihood of possibility based upon data used to train the recognition module of the analytic sensor framework and/or prior knowledge of an analyst. The data instantiations for objects, trajectories, and behaviors are identified in real time. | 12-17-2009 |
Austin I. D. Eliazar, Morrisville, NC US
Patent application number | Description | Published |
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20080243425 | Tracking target objects through occlusions - A computerized object tracking method uses data captured from any of a number of sensor suites deployed in an area of interest to identify and track objects of interest within the area covered by the sensors. Objects of interest are uniquely identified utilizing an ellipse-based model and tracked through complex data sets through the use of particle-filtering techniques. The combination of unique object identification and particle-filtering techniques produces the ability to track any of a number of objects of interest through complex scenes, even when the objects of interest are occluded by other objects within the dataset. The tracking action is presented in real-time to a user of the system and accepts direction and requests from the system user. | 10-02-2008 |