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Unfortunately, the precedence relation of YVRG filter causes a disadvantage if the filter is applied to a small-sized image on massive parallel computers.
In this article, we are interested in binary cosegmentation and observe that for most applications of binary cosegmentation several criteria should be followed: (1) automation, i.e., it is executed without user interactions; (2) scalability, i.e., it can be applied to hundreds of images instead of two images or small sized image sets; (3) focusing on "object" instead of "stuff".
Therefore, it only works on small sized images (e.g. 100 × 90).
A small sized experimental autonomous bicycle is developed.
The proposed method was tested in a small sized hospital.
Using the proposed schemes, a small sized label is needed.
In this study, LAD has been applied on a small sized dataset.
Chop up a small sized fresh cucumber into fine pieces.
Take a small sized bucket and get in the tank.
However, the latest version of CEDD1 adapts to the description of smaller sized images and according to the image's size in question, and defines a minimum of 20×20 block division of at least 2×2 pixels each.
Using a small-sized support image, the extraction is carried out on size-reduced level, bringing computational advantage to our method.
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