Abstract:
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.
Abstract:
A motion estimation system comprises: an initializing module generating a first initial motion field by allocating an initial motion vector to each block of a first motion field related to a first motion change of an image which accompanies a change from an (n−1)-th frame to an n-th frame and generating a second initial motion field allocating an initial motion vector to each block of a second motion field related to a second motion change of the image which accompanies a change from the n-th frame to the (n−1)-th frame; and a candidate test module generating first and second random motion fields based on a similarity function and each block of each of the first and second initial motion fields and random motion vectors, generating first and second spatial propagation motion fields based on the similarity function, and generating first and second optimum motion fields based on the similarity function.
Abstract:
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.
Abstract:
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.
Abstract:
A motion estimation system comprises: an initializing module generating a first initial motion field by allocating an initial motion vector to each block of a first motion field related to a first motion change of an image which accompanies a change from an (n−1)-th frame to an n-th frame and generating a second initial motion field allocating an initial motion vector to each block of a second motion field related to a second motion change of the image which accompanies a change from the n-th frame to the (n−1)-th frame; and a candidate test module generating first and second random motion fields based on a similarity function and each block of each of the first and second initial motion fields and random motion vectors, generating first and second spatial propagation motion fields based on the similarity function, and generating first and second optimum motion fields based on the similarity function.