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As mentioned in the previous section, the lack of long-range interaction during the labelling processing in the algorithm of [1] is the key reason that oversegmentation occurs and that the algorithm is not able to assign the same class label to disjoint regions of the same texture; it is also one of the reasons why the algorithm's convergence rate cannot be accelerated.
Once in the biobank, the reception, labelling, processing, storing and tracking of each sample and its derivatives must be done correctly and correctly documented.
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On the one hand, many prototypes, validated by experimental demonstration of all-optical label processing solutions, have been developed.
A review of phlebotomy and laboratory procedures found no issues related to sample collection, labelling or processing.
In the following, we give the detailed parsing and labeling processing.
First, we use parsing and labeling processing to map the subfunctions in unit of MB in video decoder, so as to generate scalable video decoding output.
On the other hand, the primitive available technology for performing label processing poses several constraints on the label structures; this in turn significantly impacts the traffic engineering aspects of such a network.
To simplify the problem, we construct parsing and labeling processing in video decoder in first step, which is given in details in Section 2. These provide the foundation of ESVD.
Recent advances in optical header (label) processing and optical switching led to asynchronous and variable-length OPS with contention resolution in wavelength, time, and space domains at nanosecond switching speeds.
The second factor comprised of speed scores also from the four card tasks and was labelled Processing Speed.
The first factor comprised of the accuracy scores from the four card tasks which was labelled Processing Accuracy.
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