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The implementations of the LoG detector in the Python scikit-image library [41] and SIFT in OpenCV [42] were used throughout.
The bridge output is processed via high speed digitizer and an in-phase/quadrature (I/Q) demodulator for pulsed work or sent to a wideband, high dynamic range log detector for CW.
An additional aspect of the LoG worth mentioning is that the presence of other instrumental factors, such as blurring, only affects the scale at which the feature is detected, but not the accuracy of the LoG detector.
Note, however, that the LoG detector has a strong response to features near edges (of an image), which, in practice, can lead to an overestimation of the detection accuracy.
The gamma log detector converts incoming gamma radiation from the surrounding geologic formation into electrical impulses, which are proportional to the number of gamma rays detected and sends the amplified electrical impulses to the surface electronic device to be recorded and displayed.
Robust image de-noising strategies can, of course, be employed in practice to increase the accuracy of atomic column detection by the LoG detector, but this was not performed here as the de-noising constitutes a separate problem from the focus of this paper, and is well covered in both electron microscopy and image recognition literature.
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In order to evaluate the impact of edge detectors on the performance of HAR, the edge information is also detected by Sobel and Log edge detectors.
Thus, the algorithm has the same performance of the Max-Log ML detector.
This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector.
The reduced complexity max-log MAP detector has a much lower complexity than the MMSE receiver [22].
In this section, the computational complexity of the proposed soft-decision aided detectors and the Max-Log-LLR detector will be discussed in detail.
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