Amazon Technologies, Inc.

General Instrument Corporation

Arris Technology, Inc.

Arris Enterprises LLC

Amit Tikare

Vijay Kamarshi

Prasanna Venkatesh Krishnasamy

Sean Thomas McCarthy

Christopher D Coley

Menashe Haskin

Eric Wang

Ronald Joseph Degges, Jr

Robert Warren Sjoberg

Sean Mccarthy

Techniques for efficiently identifying objects of interest in an environment and, thereafter, determining the location and/or orientation of those objects. As described below, a system may analyze images captured by a camera to identify objects that may be represented by the images. These objects may be identified in the images based on their size, color, and/or other physical attributes. After identifying these potential objects, the system may define a region around each object for further inspection. Thereafter, portions of a depth map of the environment corresponding to these regions may be analyzed to determine whether any of the objects identified from the images are 'objects of interest'&#x2014;or objects that the system has previously been instructed to track. These objects of interest may include portable projection surfaces, a user's hand, or any other physical object. The techniques identify these objects with reference to the respective depth signatures of these objects.

Object recognition techniques

In some examples, a vision system includes multiple time of flight (ToF) cameras and a single illumination source. The illumination source and the multiple ToF cameras may be synchronized with each other, such as through a phase locked loop based on a generated control signal. A first one of the ToF cameras may be co-located with the illumination source, and a second one of the ToF cameras may be spaced away from the illumination source and the first ToF camera. For instance, the first ToF camera may have a wider field of view (FoV) for generating depth mapping of a scene, while the second ToF camera may have a narrower FoV for generating higher resolution depth mapping of a particular portion of the scene, such as for gesture recognition.

Single source multi camera vision system

Techniques for efficiently identifying objects of interest in an environment and, thereafter, tracking the location and/or orientation of those objects. As described below, a system may analyze images captured by a camera to identify objects that may be represented by the images. These objects may be identified in the images based on their size, color, and/or other physical attributes. After identifying these potential objects, the system may define a region around each object for further inspection. Thereafter, portions of a depth map of the environment corresponding to these regions may be analyzed to determine whether any of the objects identified from the images are 'objects of interest'&#x2014;or objects that the system has previously been instructed to track. These objects of interest may include portable projection surfaces, a user's hand, or any other physical object. The techniques identify these objects with reference to the respective depth signatures of these objects.

Object tracking techniques

In a system that monitors the positions and movements of objects within an environment, a depth camera may be configured to produce depth images based on configurable measurement parameters such as illumination intensity and sensing duration. A supervisory component may be configured to roughly identify objects within an environment and to specify observation goals with respect to the objects. The measurement parameters of the depth camera may then be configured in accordance with the goals, and subsequent analyses of the environment may be based on depth images obtained using the measurement parameters.

Determining time-of-fight measurement parameters

A system includes a data storage configured to store a model human visual system, an input module configured to receive an original picture in a video sequence and to receive a reference picture, and a processor. The processor is configured to create a pixel map of the original picture using the model human visual system. A first layer is determined from the pixel map. A weighting map is determined from a motion compensated difference between the original picture and the reference picture. A processed picture is then determined from the original picture using the weighting map and the first layer.

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Pleasanton, CA, United States of America

Amit Tikare