By Robert Sedgewick
Represents the fundamental first half Sedgewick's entire paintings. Its 4 elements: basics, information constructions, sorting, and looking out. Appeals both to either the educational markets. Softcover.
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Additional resources for Algorithms in Java, Parts 1-4 (3rd Edition)
11. Plot used in FDR algorithm. See text for details. Right: zoom. Let P1 , . . , PN denote the p-values of the N coeﬃcents at a given scale, ordered from smallest to largest. The algorithm to calculate the FDR threshold is the following: – Plot the curve Fp (i) versus i/N where Fp (i) is the p-value of the ith ordered coeﬃcients (see Fig. 11, black curve). We consider as signiﬁcant (active) all coeﬃcients whose p-values are less than or equal to a given value PD . – Plot the line y = Nαj cN , with cN = 1 when the p-values are statistically independent (see Fig.
2003a) will outperform the wavelet transform. 4 The Multiresolution Support A multiresolution support of a data set describes in a logical or Boolean way if the data s contains information at a given scale j and at a given position (s) l. If Mj,k = 1 (or = true), then s contains information at scale j and at the position k. M depends on several parameters: – – – – The input data. The algorithm used for the multiresolution decomposition. The noise. All additional constraints we want the support to satisfy.
J, then the pixel k ∈ S. 6. For all the selected pixels k, compute the values sk − cJ,k and compute (n+1) the standard deviation σs of these values (we compute the diﬀerence between s and cJ in order not to include the background in the noise estimation). 42 2. Filtering 7. n = n + 1 |σ (n) −σ (n−1) | 8. If s (n)s > σs then go to 4. This method converges in a few iterations, and allows noise estimation to be improved. The approach is in fact physically meaningful. It consists of detecting the set N of pixels which does not contain any signiﬁcant signal (only the background + noise).
Algorithms in Java, Parts 1-4 (3rd Edition) by Robert Sedgewick