Qualcomm Incorporated

Meng-Lin Wu

An M Chen

Chung-Chi Tsai

Venkata Ravi Kiran Dayana

Fatih Murat Porikli

Shubhankar Mangesh Borse

Methods, systems, and apparatuses for image segmentation are provided. For example, a computing device may obtain an image, and may apply a process to the image to generate input image feature data and input image segmentation data. Further, the computing device may obtain reference image feature data and reference image classification data for a plurality of reference images. The computing device may generate reference image segmentation data based on the reference image feature data, the reference image classification data, and the input image feature data. The computing device may further blend the input image segmentation data and the reference image segmentation data to generate blended image segmentation data. The computing device may store the blended image segmentation data within a data repository. In some examples, the computing device provides the blended image segmentation data for display.

Apparatus and methods for image segmentation using machine learning processes

Systems and techniques are described herein for modifying the scale and/or position of objects in images. For instance, a system can obtain a two-dimensional (2D) input image from a camera and a three-dimensional (3D) representation of the 2D input image. The system can further determine a first portion of the 3D representation of the 2D input image corresponding to a target object in the 2D input image. The system can adjust a pose of the first portion of the 3D representation of the 2D input image corresponding to the target object. The system can further generate a 2D output image having a modified version of the target object based on the adjusted pose of the first portion of the 3D representation of the 2D input image corresponding to the target object to be output on a display.

Image modification techniques

Systems and techniques are described for processing image data to generate an image with a synthetic depth of field (DoF). An imaging system receives first image data of a scene captured by a first image sensor. The imaging system receives second image data of the scene captured by a second image sensor. The first image sensor is offset from the second image sensor by an offset distance. The imaging system generates, using at least the first image data and the second image data as inputs to one or more trained machine learning systems, an image having a synthetic depth of field corresponding to a simulated aperture size. The simulated aperture size is associated with the offset distance. The imaging system outputs the image.

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San Diego, CA, United States of America

Meng-Lin Wu