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    Please note that a high detection rate for the conventional method is not necessarily an advantage as the final results should be tangible and excessive jitters can render the whole detection results useless.

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    Input: A set of distinct binary fingerprints of length l bits: F, key length: m, the number of keys: n Output: A two dimensional index table T with 2m rows and n columns.

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    Download ohs 12c album 2017 downloadThus, the algorithm results in a scored list of indices, with each score representing a level of similarity between the query and the corresponding fingerprint in the database.

    Please note that for memory management purposes, instead of a fingerprint, its index, a unique number assigned to each fingerprint, is stored in the buckets of the table.

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    On the other hand, if a stability measure is available for individual bits of the query fingerprint, this extra information can be used to decrease the number of pseudo hash vectors generated by a great extent.

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    EWH will examine all the rows and columns of the index table, resulting in the same computational complexity as the exhaustive search, which is not desired.

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    Download ohs 12c album 2017 downloadFor finding the optimal values for these parameters we need to know the distribution of the similarity score s that is assigned to the fingerprints by the algorithm.

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    Approximate nearest neighbour search As mentioned in the introduction, searching for the nearest neighbour of a query fingerprint, in a pool of previously extracted fingerprints is at the heart of any copy detection system.

    Download ohs 12c album 2017 downloadOn the other hand, the conventional method has an increasing jitter, a steady large retrieval time, and a detection rate that abruptly drops for large bit error rates.

    Download ohs 12c album 2017 downloadTo further decrease the search time, the algorithm stops looking for more candidates, after a certain number of candidates have already been added to the list.

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    Download ohs 12c album 2017 downloadThe detection performance of the conventional approach is higher for large amounts of overlap, which in turn leads to upto about three orders of magnitude longer search times compared to the proposed approach (which is upto 900 times faster).

    To find the Probability Distribution Function (PDF) of s, p(s), we first need to determine the probability that a hash vector of a query contains r errors compared to the corresponding hash vector of a fingerprint in the database.

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    Download ohs 12c album 2017 downloadWe propose a NN search algorithm that resolves this failure by taking erroneous hash vectors into consideration and by using them to generate a refined candidate list.

    We assume that errors happen due to distortions such as noise, frame dropping, changes in the brightness, etc (but not as a result of time shifts).

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    For our proposed method the average detection rate (TPR) is 95.

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