Confidence estimation for optical flow
First Claim
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1. A method of confidence estimation for optical flow comprising the steps of:
- computing a set of features for each pixel of an input image, wherein the set of features includes an image feature, a matching cost, and a flow feature, wherein the image feature includes an image gradient feature, wherein the flow feature includes a flow gradient feature, and wherein computing the set of features comprises;
computing a gradient Gu of a U component of the optical flow;
computing a gradient Gv of a V component of the optical flow;
computing a cost (or distance) C0 corresponding to a best match;
computing a Delta matching cost Cd; and
computing a gradient magnitude Gm of the input image;
computing a smoothed version of each feature of the set of features;
constructing a feature vector for each pixel of the input based on the set of features and the smoothed versions of the set of features;
computing a classifier score from the feature vector using a set of decision tree classifiers; and
converting the classifier score into a confidence score, the confidence score representing a confidence of the optical flow.
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Abstract
A confidence map for optical flow gradients is constructed calculating a set of gradients for each pixel of an image, filtering said gradients and extracting confidence values from said gradients using a plurality of decision tree classifiers. A confidence map is then generated from said confidence values.
6 Citations
10 Claims
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1. A method of confidence estimation for optical flow comprising the steps of:
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computing a set of features for each pixel of an input image, wherein the set of features includes an image feature, a matching cost, and a flow feature, wherein the image feature includes an image gradient feature, wherein the flow feature includes a flow gradient feature, and wherein computing the set of features comprises; computing a gradient Gu of a U component of the optical flow; computing a gradient Gv of a V component of the optical flow; computing a cost (or distance) C0 corresponding to a best match; computing a Delta matching cost Cd; and computing a gradient magnitude Gm of the input image; computing a smoothed version of each feature of the set of features; constructing a feature vector for each pixel of the input based on the set of features and the smoothed versions of the set of features; computing a classifier score from the feature vector using a set of decision tree classifiers; and converting the classifier score into a confidence score, the confidence score representing a confidence of the optical flow. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of confidence estimation for optical flow comprising the steps of:
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computing a set of features for each pixel of an input image, wherein the set of features includes an image feature, a matching cost, and a flow feature; computing a smoothed version of each feature of the set of features by; computing a smoothed version Su of Gu, wherein Gu is a gradient of a U component of the optical flow; computing a smoothed version Sv of Gv, wherein Gv is a gradient of a V component of the optical flow; computing a smoothed version S0 of C0, wherein C0 is a cost or distance corresponding to a best match; computing a smoothed version Sd of Cd, wherein Cd is a delta matching cost; and computing a smoothed version Sm of Gm, wherein Gm is a gradient magnitude of the input image; constructing a feature vector for each pixel of the input image; computing a classifier score from the feature vector using set of decision tree classifiers; and converting the classifier score into a confidence score, the confidence score representing a confidence of the optical flow. - View Dependent Claims (9, 10)
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Specification