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It is often used for image matching.
We have proposed one-time key based phase scrambling for image matching.
Finally, a two-way matching method based on adaptive Euclidean nearest/second nearest discrimination law is designed for image matching.
A binary key sequence is used only once for scrambling a template and is not required for image matching.
In this paper, we propose a new image similarity measure combining the Hausdorff distance with a normalized gradient consistency score for image matching.
In one-time key based phase scrambling, a key is used once for scrambling but is not required for image matching.
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It then searches for images matching your search term — curly hair, for example — and looks for "doppelganger sets," images where the subject's face is in a similar position to your own.
IND then used this information, to cluster and select vantage images for dense image matching.
This prompted us to take a biologically inspired look at building a cognitive architecture that uses artificial neural nets at the face detection stage and adapts a single image per person (SIPP) approach for face image matching.
We present distributed computing algorithms for the tie-points extraction and for the dense image matching.
One-time key based phase scrambling is proposed for privacy-protected image matching.
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