Amazon Technologies, Inc.

Abhishek Singh

Rohith Mysore Vijaya Kumar

Amit Kumar Agrawal

Qiang Liu

Visesh Uday Kumar Chari

Lelin Zhang

Ambrish Tyagi

Prakash Ramu

David Ting-Yu Wu

Apoorv Chaudhri

Noam Sorek

Reto Kramer

Yadunandana Nagaraja Rao

David Nunnerley

Anssi Alaranta

Weijian Wang

Jeffrey Hoffman

A debugger core captures one or more inputs to a distributed application during live execution of the distributed application in a production environment. The debugger core applies the one or more inputs to the distributed application during replay execution of the distributed application in a debugging environment and performs debugging of the distributed application in the debugging environment.

Microservice replay debugger

Devices and techniques are generally described for image editing. In various examples, color image data and segmentation image data may be identified. In some examples, the segmentation image data may identify a first portion of the color image data as a foreground region and a second portion of the color image data as a background region. A segmentation boundary may be identified between the foreground region and the background region. A first area of the second portion may be identified, where the first area extends from the segmentation boundary to a first area outer boundary. A second area of the second portion may be identified, where the second area extends from the first area outer boundary to a second area outer boundary. A blended and blurred representation of the first area and the second area may be generated.

Generation of semantic depth of field effect

Devices and techniques are generally described for segmentation of object image data from background image data. In some examples, the segmentation of object image data may comprise capturing image data comprising color data and depth data. In some examples, the segmentation of object image data may further include separating the depth data into a plurality of clusters of image data, wherein each cluster is associated with a respective range of depth values. In various examples, the segmentation of object image data may comprise selecting a main cluster of image data as corresponding to an object of interest in the image data. In various other examples, the segmentation of object image data may comprise identifying pixels of the main cluster that correspond to the object of interest.

Segmentation of object image data from background image data

Devices and techniques are generally described for segmentation of image data using depth data. In various examples, color image data may be received from a digital camera. In some examples, depth image data may be received from a depth sensor. In various examples, the depth image data may be separated into a plurality of clusters of depth image data, wherein each cluster is associated with a respective range of depth values. In some further examples, a determination may be made that a first cluster of image data corresponds to an object of interest, such as a human subject, in the image data. In various examples, pixels of the first cluster may be encoded with foreground indicator data. In some further examples, segmented image data may be generated. The segmented image data may comprise pixels encoded with the foreground indicator data.

Image data segmentation using depth data

Various examples are directed to systems and methods for detecting regions in video frames. For example, a computing device may receive a video comprising a plurality of frames and a video frame sequence of the plurality of frames. The computing device may select a plurality of scene point location from a first frame. The computing device may determine a plurality of columns in the first frame and fit a first sinusoidal function to a distribution of average column Y-axis displacements for the plurality of columns by column position. The computing device may determine a first difference based at least in part on the first scene point Y-axis displacement and an output of the first sinusoidal function at the X-axis position of the first scene point and determine that the first difference is greater than a threshold distance.

Detecting foreground regions in panoramic video frames

Various examples are directed to systems and methods for determining foreground regions in video frames. A computing device may select from a first frame of a video, a plurality of scene point locations and divide the first frame into a plurality of sections. For a first section of the first frame, the computing device may generate a first vector subspace, basis vectors of the first vector subspace are trajectories of scene point locations in the first section. The computing device may determine that a projection error for a first scene point location in the first section is greater than a projection error threshold and write an indication of the first pixel value to a listing of foreground pixel values.

Detecting foreground regions in video frames

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Mountain View, CA, United States of America

Abhishek Singh