Samsung Electronics Co., Ltd.

Petr Pohl

Igor Mironovich Kovliga

Valery Valerievich Anisimovskiy

Vladimir Petrovich Bogachev

Liubov Vladimirovna Stepanova

Aleksandr Aleksandrovich Khakhmovich

Roman Arzumanyan

Alexey Mikhailovich Gruzdev

Kira Sergeevna Kirillova

Sergei Dmitrievich Ilichev

Kristina Olegovna Rakova

Liudmila Igorevna Burmak

Imran Kazbekovich Navruzbekov

Alexey Viktorovich Kliuev

Maxim Alexeevich Shikunov

Xenya Iurevna Petrova

A device for gathering image sets includes: N digital cameras including one or more ground truth cameras for capturing high quality ground truth images and one or more target cameras for capturing low quality target images, the N digital cameras being optically coupled via N−1 beam splitters, at least N−1 cameras of the N digital cameras being mounted on automatically adjustable mounts; and at least one computer readable storage device storing instructions executable by at least one processor. The instructions cause the at least one processor to adjust the at least N−1 cameras by actuating the automatically adjustable mounts on which the at least N−1 cameras are mounted; synchronize image capture by the digital cameras; and control the digital cameras to gather sets of N images. The at least N−1 cameras are adjusted based on at least one set of N images of the scene.

Device and method for gathering image sets

A method and device for predictive autofocus on an object are provided. The method includes capturing an image of a target object to be tracked by a user of camera of a device, selecting a region of interest (ROI) corresponding to a target point of the captured image of the target object, initializing a target object tracker via input of the selected ROI at i-th frame, where i=1, updating the target object tracker based on data at previous (i−1)th frame for i-th frame, where i>1, determining current camera lens position for the tracked target object at current frame, when the target object is out of the camera focus, determining disparity for the selected ROI at i-th frame from previously collected data defining dependence of distance between the target object and lens surface on the camera lens position, determining delta lens position to focus the camera on the target object as a function of disparity and current lens position or target object position in the frame by a computing unit, calculating predicted target object position and delta lens position for N future frames from coordinates of current ROI and current delta lens position, and providing predicted autofocus in real time.

Device and method for predicted autofocus on an object

A method for converting a frame rate of an input video is provided. The method includes performing a motion estimation by generating at least one motion field for at least one pair of frames of the input video, wherein the at least one motion field includes a set of motion vectors for each block of a first reference frame of the input video, the set of motion vectors indicating to a second reference frame of the input video, and preparing first data for a predetermined interpolation phase for each block of an interpolated frame, where the first data include at least one parameter for a pre-trained occlusion processing (OcC) convolutional neural network (CNN), the at least one parameter being obtained based on the at least one motion field.

Occlusion processing for frame rate conversion using deep learning

A motion estimation method for video data including frames and an apparatus therefor are provided. The motion estimation method includes determining whether a current frame unit for which motion estimation is to be performed corresponds to a double block, when the current frame unit corresponds to a double block, acquiring a candidate vector set corresponding to a first single block included in the double block, as a candidate vector set of the double block, individually calculating a confidence function value of each candidate vector included in the candidate vector set of the double block, for the first single block and a second single block included in the double block, and acquiring an estimated motion vector of the first single block and an estimated motion vector of the second single block, based on the calculated confidence function value of each candidate vector.

Motion estimation method and apparatus for plurality of frames

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Lobnya, Russia

Petr Pohl