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However, in most fielded systems, unwanted clutter and interference sources mean that the noise level changes both spatially and temporally. In this case, a changing threshold can be used, where the threshold level is raised and lowered to maintain a constant probability of false alarm. This is known as constant false alarm rate (CFAR) detection.
The false discovery rate (FDR) is then simply the following: [1] = = [], where [] is the expected value of . The goal is to keep FDR below a given threshold q . To avoid division by zero , Q {\displaystyle Q} is defined to be 0 when R = 0 {\displaystyle R=0} .
The normal deviate mapping (or normal quantile function, or inverse normal cumulative distribution) is given by the probit function, so that the horizontal axis is x = probit(P fa) and the vertical is y = probit(P fr), where P fa and P fr are the false-accept and false-reject rates.
V is the number of false positives (Type I error) (also called "false discoveries") S is the number of true positives (also called "true discoveries") T is the number of false negatives (Type II error) U is the number of true negatives = + is the number of rejected null hypotheses (also called "discoveries", either true or false)
The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining why the technique works.
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
Secondly, unlike cryptographic hash functions, CRC is an easily reversible function, which makes it unsuitable for use in digital signatures. [7] Thirdly, CRC satisfies a relation similar to that of a linear function (or more accurately, an affine function): [8]
where p(r | x) denotes the conditional joint probability density function of the observed series {r(t)} given that the underlying series has the values {x(t)}. In contrast, the related method of maximum a posteriori estimation is formally the application of the maximum a posteriori (MAP) estimation approach.