Search results
Results From The WOW.Com Content Network
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.
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.
where [] is the input as a function of the independent variable , and [] is the filtered output. Though we most often express filters as the impulse response of convolution systems, as above (see LTI system theory ), it is easiest to think of the matched filter in the context of the inner product , which we will see shortly.
A redundant bit may be a complicated function of many original information bits. The original information may or may not appear literally in the encoded output; codes that include the unmodified input in the output are systematic , while those that do not are non-systematic .
The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [ 1 ] [ 2 ] It was motivated primarily by the problem of face detection , although it can be adapted to the detection of other object classes.
Wall Street's main indexes slipped on Tuesday, weighed down by technology stocks after a batch of upbeat economic data stoked uncertainty among investors about the pace of monetary policy easing ...
Italian Sea Group is not liable in relation to the fatal sinking of Mike Lynch's superyacht, which killed the British tech billionaire and six others, its chief executive said on Tuesday. The ...
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.