Amazon Technologies, Inc.

Arris Enterprises LLC

Dolby Laboratories Licensing Corporation

General Instrument Corporation

Arris Technology, Inc.

Rawles LLC

Vijay Kamarshi

Amit Tikare

Sean Thomas McCarthy

Prasanna Venkatesh Krishnasamy

Menashe Haskin

Ning Yao

Robert Warren Sjoberg

Gregory John Ward

Ajit Ninan

Christopher D Coley

Yue Liu

Kavitha Velusamy

Qiang Liu

Eric Wang

Sowmya Gopalan

Peter A Borgwardt

Ronald Joseph Degges, Jr

Rahul Agrawal

Shiv Saxena

Kevin Wayne Arthur

Robert Ramsey Flenniken

Shwetha Ram

Domagoj Baricevic

Helder Jesus Ramalho

Paul Matthew Bombach

Sean T Mccarthy

Sean Mccarthy

Saliency regions are identified in a global scene depicted by volumetric video. Saliency video streams that track the saliency regions are generated. Each saliency video stream tracks a respective saliency region. A saliency stream based representation of the volumetric video is generated to include the saliency video streams. The saliency stream based representation of the volumetric video is transmitted to a video streaming client.

Representing volumetric video in saliency video streams

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

A video processing system is provided to create quantization data parameters based on human eye attraction to provide to an encoder to enable the encoder to compress data taking into account the human perceptual guidance. The system includes a perceptual video processor (PVP) to generate a perceptual significance pixel map for data to be input to the encoder. Companding is provided to reduce the pixel values to values ranging from zero to one, and decimation is performed to match the pixel values to a spatial resolution of quantization parameter values (QP) values in a look up table (LUT). The LUT table values then provide the metadata to provide to the encoder to enable compression of the original picture to be performed by the encoder in a manner so that bits are allocated to pixels in a macroblock according to the predictions of eye tracking.

Method of bit allocation for image and video compression using perceptual guidance

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

Encoding a video signal including pictures includes generating perceptual representations based on the pictures. Reference pictures are selected and motion vectors are generated based on the perceptual representations and the reference pictures. The motion vectors and pointers for the reference pictures are provided in an encoded video signal. Decoding may include receiving pointers for reference pictures and motion vectors based on perceptual representations of the reference pictures. The decoding of the pictures in the encoded video signal may include selecting reference pictures using the pointers and determining predicted pictures, based on the motion vectors and the selected reference pictures. The decoding may include generating reconstructed pictures from the predicted pictures and the residual pictures.

Encoding and decoding using perceptual representations

A vision system associated with a projection system includes multiple optical pathways. For instance, when the projection system projects an image onto a generally vertical surface, the vision system may operate in a rear sensing mode, such as for detecting one or more gestures made by a user located behind the projection system. Alternatively, when the projection system projects the image onto a generally horizontal surface the vision system may operate in a front sensing mode for detecting gestures made by a user located in front of the projection system. One or more thresholds may be established for switching between the front sensing mode and the rear sensing mode based on orientation information. As another example, the vision system may be operated in both the front sensing mode and the rear sensing mode contemporaneously.

Forward and backward looking vision system

A system may utilize a projector, a camera, and a depth sensor to produce images within the environment of a user and to detect and respond to user actions. Depth data from the depth sensor may be analyzed to detect and identify items within the environment. A coordinate transformation may then be used to identify corresponding color values from camera data, which can in turn be analyzed to determine the colors of detected items. A similar coordinate transformation may be used to identify color values of a projected image that correspond to the detected items. In some cases, camera color values corresponding to an item may be corrected based on the corresponding color values of a projected image. In other cases, projected color values corresponding to an item may be corrected based on the corresponding camera color values.

Object-based color detection and correction

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.

System for reducing noise in video processing

A passive projection screen presents images projected thereon by a projection system. A surface of the screen includes elements that are reflective to non-visible light, such as infrared (IR) light. When non-visible light is directed to the screen, the non-visible light is reflected by the reflective elements back. Part of the reflected light may contact and reflect from a user's fingertip or hand (or other object, such as a stylus) while another part is reflected to the projection system. The projection system differentiates among distances to the surface and distances that include the additional travel to the fingertip. As the fingertip moves closer to the surface, the distances approach equality. When the distances are approximately equal, the finger is detected as touching the surface. In this manner, a projection surface equipped with reflective elements facilitates more accurate touch detection.

Projection surface with reflective elements for non-visible light

Growing community of inventors

Cupertino, CA, United States of America

Vijay Kamarshi