Samsung Electronics Co., Ltd.

Tomer Peleg

Doron Sabo

Omry Sendik

Michael Dinerstein

Ran Vitek

Roy Josef Jevnisek

Alexandra Dana

Netanel Stein

Matan Shoef

Yotam Perlitz

Pablo Szekely

Maor Shutman

An object detection system may generate regions of interest (ROIs) from an input image that can be processed by a wide range of object detectors. According to the techniques described herein, an image is processed by a light-weight neural network (e.g., a heatmap network) that outputs object center and object scale heat-maps. The heatmaps are processed to define ROIs that are likely to include objects. Overlapping ROIs are then merged to reduce the aggregate size of the ROIs, and merged ROIs are downscaled to a reduced set of pre-defined resolutions. Fully-convolutional, high-accuracy object detectors may then operate on the downscaled ROIs to output accurate detections at a fraction of the computations by operating on a reduced image. For example, fully-convolutional, high-accuracy object detectors may operate on a subset of the entire image (e.g., cropped images based on ROIs) thus reducing computations otherwise performed over the entire image.

Region of interest selection for object detection

A system of convolutional neural networks (CNNs) that synthesize middle non-existing frames from pairs of input frames includes a coarse CNN that receives a pair of images acquired at consecutive points of time, a registration module, a refinement CNN, an adder, and a motion-compensated frame interpolation (MC-FI) module. The coarse CNN outputs from the pair of images a previous feature map, a next feature map, a coarse interpolated motion vector field (IMVF) and an occlusion map, the registration module uses the coarse IMVF to warp the previous and next feature maps to be aligned with pixel locations of the IMVF frame, and outputs registered previous and next feature maps, the refinement CNN uses the registered previous and next feature maps to correct the coarse IMVF, and the adder sums the coarse IMVF with the correction and outputs a final IMVF.

Efficient CNN-based solution for video frame interpolation

A method of matching stereo images includes: determining, first information indicating which orientations are dominant in each pixel location of a right image and a left image among the stereo images; determining second information indicating for each pixel location in the right image and the left image whether it lies on a pure horizontal edge; detecting points in the left image and the right image based on the first information and the second information; and performing a matching on the left image and the right image using the detected points to estimate a sparse disparity map.

Method and system for matching stereo images

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Kfar Saba, Israel

Tomer Peleg