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Previous approaches describe the local feature based on image patch that uses single feature point as the approximate center.
The result shows that the local feature with multiple line descriptors outperforms other classical features based on image patch on robustness.
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Illustration based on images by Getty.
The third stage identifies the dominant orientations for each keypoint based on its local image patch.
Thirdly, the dominant orientations for each keypoint are identified based on the local image patch.
Figure 3 The effect of ZCA whitening on image patches.
In addition, the basic image signature (BoW histogram) based on dense patch SIFT is denoted as (left { mathbf {H}_{q},left { mathbf {H}_{d_{m}}right }_{m=1}^{M}right } ).
By iterating the patch selection based on the patch priority and its SSIM-based missing area reconstruction, we can inpaint the whole missing areas within the target image.
Based on a patch by Gary Buhrmaster.
Based on a patch by Jakob Uhd Jepsen (One.com A/S).
The weights are based on the patch intensity vector distances.
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