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Algorithm 5 implements this optimization for basic integral image computation.
point-to-point product computation of all features, the integral image computation of features, the integral image computation of products.
But for that, we first have to rewrite the integral image computation.
The covariance tracking algorithm is composed of three stages: 1. point-to-point product computation of all features, 2. the integral image computation of features, 3. the integral image computation of products. .
In this work, targeting this problem, a large size single image computation acceleration bilateral filtering based defogging (CABFD) scheme is presented.
As the two last stages are similar, we only present a generic version of integral image computation (Algorithm 3) and its transformation (Algorithm 4).
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Since we do not need to re-calculate the attribute data for all connected components of the image, the computation time devoted to the attribute computation remains linear.
So the scheduling (with this software implementation) takes about 63% of a one image processing computation time on a desktop computer.
This new device has enhanced ROI features including individual header, footer and inbuilt image histogram computation.
This paper reports an approach to persistence in object-oriented languages, such as Smalltalk, that require a memory-resident image for computation.
This produces two segmentations of each image, requiring computation of a consensus segmentation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com