Samsung Electronics Co., Ltd.

George Q Chen

Youngjun Yoo

Chenchi Luo

Yingmao Li

Hamid Rahim Sheikh

Michael Oliver Polley

David D Liu

Kaimo Lin

Kushal Kardam Vyas

Gyeongmin Choe

Frank Z Brill

A method includes obtaining, using at least one processor, an input image frame. The method also includes identifying, using the at least one processor, one or more regions of the input image frame containing redundant information. In addition, the method includes performing, using the at least one processor, an image processing task using the input image frame. The image processing task is guided based on the one or more identified regions of the input image frame. The method may further include obtaining, using the at least one processor, a coarse depth map associated with the input image frame. Performing the image processing task may include refining the coarse depth map to produce a refined depth map, where the refining of the coarse depth map is guided based on the one or more identified regions of the input image frame.

Guided backpropagation-gradient updating for image processing task using redundant information from image

A method includes obtaining, using at least one processor, first and second input image frames, where the first and second input image frames are associated with first and second image planes, respectively. The method also includes obtaining, using the at least one processor, a depth map associated with the first input image frame. The method further includes producing another version of the depth map by performing one or more times: (a) projecting, using the at least one processor, the first input image frame to the second image plane in order to produce a projected image frame using (i) the depth map and (ii) information identifying a conversion from the first image plane to the second image plane and (b) adjusting, using the at least one processor, at least one of the depth map and the information identifying the conversion from the first image plane to the second image plane.

Super-resolution depth map generation for multi-camera or other environments

A method includes obtaining, using first and second image sensors of an electronic device, first and second images, respectively, of a scene. The method also includes obtaining, using an image depth sensor of the electronic device, a third image and a first depth map of the scene, the first depth map having a resolution lower than a resolution of the first and second images. The method further includes undistorting the first and second images using the third image and the first depth map. The method also includes rectifying the first and second images using the third image and the first depth map. The method further includes generating a disparity map using the first and second images that have been undistorted and rectified. In addition, the method includes generating a second depth map using the disparity map and the first depth map, where the second depth map has a resolution that is higher than the resolution of the first depth map.

Apparatus and method for dynamic multi-camera rectification using depth camera

A method includes obtaining a first image of a scene using a first image sensor of an electronic device and a second image of the scene using a second image sensor of the electronic device. The method also includes generating a first feature map from the first image and a second feature map from the second image. The method further includes generating a third feature map based on the first feature map, the second feature map, and an asymmetric search window. The method additionally includes generating a depth map by restoring spatial resolution to the third feature map.

Asymmetric normalized correlation layer for deep neural network feature matching

A method includes determining, using at least one processor, a depth of range of focus for a scene. The method also includes determining, using the at least one processor, multiple layers associated with the scene based on the depth of focus range, where each layer is associated with image data having a different range of disparity values. The method further includes blending, using the at least one processor, the layers to produce an image having a Bokeh effect in a foreground and a background, and focused image data within the depth of focus range. The multiple layers include at least a first layer associated with the foreground, a second layer associated with the depth of focus range, and a third layer associated with the background.

Generation of bokeh images using adaptive focus range and layered scattering

A method of capturing a photo using a mobile device is described. The method comprises detecting a signature motion of the mobile device; capturing video using a camera of the mobile device upon detecting the signature motion; and automatically identifying a start frame and an end frame of the captured images using neural networks. A mobile device for capturing a photo is also described.

Capturing a photo using a signature motion of a mobile device

A method for 3-Dimensional scanning includes generating a plurality of depth images when a depth sensor rotates around an object. The method further includes, for each depth image: estimating a rotation (R) and a translation (T) for each depth image, using data of a Truncated Signed Distance Function (TSDF) volume; and fusing each depth image accumulatively into the TSDF volume based on the estimated R and T. An apparatus for 3-Dimensional scanning includes a depth sensor configured to generate a plurality of depth images when rotating around an object. The apparatus further includes a processor configured to, for each depth image: estimate a rotation (R) and a translation (T) for each depth image, using data of a Truncated Signed Distance Function (TSDF) volume; and fuse each depth image accumulatively into the TSDF volume based on the estimated R and T.

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Plano, TX, United States of America

George Q Chen