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These optimizations allow a real-time execution (25 fps or about 40 ms per image) for 320×240 image size on ARM Cortex-A9 and for 640×480 on Intel Penryn-M and Haswell.
We have estimated that migrating the proposed design to a Virtex-6 model XC6VLX240T-1 [43], a considerable speed-up can be obtained, reaching 80 ms per image without compromissing accuracy in the original algorithm.
Using an MRI protocol selected for fast acquisition (15.3 ms per image) and robustness to fluid shear, images were obtained of bubbly flow in a vertical pipe of internal diameter 31 mm for systems up to a gas voidage of 41%.
X-ray settings for a single scan were 100 kV and 200 μA, with 1700 images being acquired in a full rotation at image acquisition time of 500 ms per image, with no averaging and no skipping of images (Broeckhoven et al. 2016; du Plessis et al. 2016).
The exposure time was 10 ms per image.
320 additional testing trials were then performed at 200 ms per image.
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Table 3 Effects of EGM on correct face detection Database With EGM With EGM Without EGM Without EGM rate time rate time BioID 97.6 37 96.1 12 Caltech Faces 97.1 52 96.6 21 Differences of a correct face detection rate and the average consuming time (ms) per one image.
Good defect detection ratio (>95%) was obtained for three types of defects in rods with only 8.331 ms processing time per image.
Images were acquired at 100 ms exposure time per image.
The complexity of KDE is OKDE MN) evaluations (the kernel function, multiplications and additions), assuming N image pixels and M sample points (N pixels per image and M training images).
The acquisition parameters for the flow measurement were: TR = 6.6 ms, echo spacing = 3.3 ms, matrix = 160 × 224 (320 × 448 after interpolation), slice thickness = 5 mm, FOV = 18 × 25 cm, and a parallel imaging factor of 2. The acquisition window per image was 528 ms and the experiment was repeated five times.
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