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Therefore, a Bio-Hashed minutiae template based on a chip matching algorithm is proposed.
To acquire this information, the correlation matching algorithm is applied in this paper.
A projection matching algorithm is proposed to efficiently reveal all match projections according to the projective properties of spatial edges.
Finally, the machining feature coupled graph (MFCG) based subpart matching algorithm is presented to calculate the similarity between matched subparts.
The proposed face matching algorithm is evaluated on the YMU makeup dataset and is shown to achieve very good results.
An efficient pattern matching algorithm is then applied to extract the high-level structural representation from these condensed circuit graphs.
Finally, different visual navigation scenarios were simulated by using a landscape generation toolbox, from which the effect of hazard detection on proposed hazard matching algorithm is further examined.
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A pixel is considered as well-matched if its correspondant which is obtained thanks to a given stereo-matching algorithm, is the true correspondant.
The re-matching algorithm is straightforward and easy to implement.
The most well-known pattern-matching algorithm is nearest neighbor (NN) [7].
Next, a block-matching algorithm is applied using the interpolated decoded frame.
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