Samsung Electronics Co., Ltd.

Omri Govrin

Dan Gluck

Sharon Alpert

Yowon Jeong

Tomer Avidor

Yonatan Winetraub

A method for segmenting images for estimating motion includes scanning block rows of an image to determine whether image blocks that are adjacent within a row are similar, and grouping similar image blocks into a row segment. If a block in a row is not similar to a preceding adjacent block, the block is used to initialize a new row segment. The method further includes calculating data for each row segment in an image, and using this data to determine whether a motion vector field for a row segment is consistent, whether discontinuities exist in a row segment that can be corrected, or whether discontinuities exist in a row segment that cannot be corrected, and correcting those row segment discontinuities that can be fixed to remove artifacts from a set of motion vectors associated with said row segment.

One-dimensional segmentation for coherent motion estimation

Methods, devices and computer-readable mediums for detecting occlusions which occur due to foreground object movement with respect to a background between first and second successive frames. Occlusion detection may use motion estimation with respect to at least a third frame temporally preceding the first frame. Occlusion detection may be based on one or more assumptions such as: occlusion motion vectors are different than other background motion vectors; occlusion motions are likely to be similar to foreground occluding motion; and/or motion estimation will match an occlusion block with a block belonging to a common background object. Occlusion detection may be combined with motion error detection based on a motion field divergence using a motion vector assigned to the occlusion, e.g., for generating an intermediate frame in frame up rate conversion (FRUC).

Image processing with occlusion and error handling in motion fields

A method for selecting a motion vector includes selecting a candidate block included in a second image that corresponds to a processing block of a first image, computing a first probability for the processing block and the candidate block, selecting a random block for the candidate block, computing a second probability for the processing block and the random block, saving a greater of the first probability and the second probability as a first comparison result, computing a third probability for the processing block, a first neighboring block of the processing block, a matching block in the second image that is matched to the first neighboring block, and a second neighboring block of the matching block, and saving a greater of the first comparison result and the third probability as a second comparison result.

Method for selecting a matching block

Disclosed is a global motion detecting method which includes receiving a video sequence of input images, calculating local motion vectors, one for each image block of a current input image, grouping image blocks of the current input image into image block groups, calculating a group motion parameter of each of the image block groups based on local motion vectors of the image blocks in each respective image block group, and determining a global motion parameter of the currently input image according to the group motion parameters.

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Pardes Hana, Israel

Omri Govrin