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  2. Constant false alarm rate - Wikipedia

    en.wikipedia.org/wiki/Constant_false_alarm_rate

    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.

  3. Detection error tradeoff - Wikipedia

    en.wikipedia.org/wiki/Detection_error_tradeoff

    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.

  4. False discovery rate - Wikipedia

    en.wikipedia.org/wiki/False_discovery_rate

    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} .

  5. Viola–Jones object detection framework - Wikipedia

    en.wikipedia.org/wiki/Viola–Jones_object...

    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.

  6. Canny edge detector - Wikipedia

    en.wikipedia.org/wiki/Canny_edge_detector

    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.

  7. Fault detection and isolation - Wikipedia

    en.wikipedia.org/wiki/Fault_detection_and_isolation

    Fault detection, isolation, and recovery (FDIR) is a subfield of control engineering which concerns itself with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Two approaches can be distinguished: A direct pattern recognition of sensor readings that indicate a fault and an analysis ...

  8. Hessian affine region detector - Wikipedia

    en.wikipedia.org/wiki/Hessian_Affine_region_detector

    The Hessian affine region detector is a feature detector used in the fields of computer vision and image analysis. Like other feature detectors, the Hessian affine detector is typically used as a preprocessing step to algorithms that rely on identifiable, characteristic interest points .

  9. Phase detector characteristic - Wikipedia

    en.wikipedia.org/wiki/Phase_detector_characteristic

    A phase detector characteristic is a function of phase difference describing the output of the phase detector. For the analysis of Phase detector it is usually considered the models of PD in signal (time) domain and phase-frequency domain. [ 1 ]