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To reason about the meaning of an image, useful information should be provided with that image; however, images often contain little to no textual information about the objects they are depicting, which is the precise reason why there is a need for CBIR systems that exploit only the correlations present in the raw pixel data.
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The first column shows the raw pixel data (in blue).
Following this assumption, the raw pixel values in luminance channel are used as the color propagation domain [3, 16].
All it gets access to is the raw pixel inputs and the game's score.
Since we measured the raw pixel value, which is inversely related to radiodensity (i.e., as radiopacity increases the pixel value decreases), an inverse ratio is used in the above formula.
The raw pixel data have been deposited to the Gene Expression Omnibus (GEO) database Series # GSE8184.
The intraoperative classification of residual fluorescence was based on the raw pixel counts normalized to exposure time.
However, results from the present pixel-level technique suggest that these changes are limited to the central metatarsals, and that regional methods can suggest significance where none exists in the actual raw (pixel-level) data, due to signal aliasing and, in particular, to conflation of regional boundaries.
The important output data from the ACA are the scaled raw pixel intensities in regions (4×4, 6 ×6, or 8×8 pixel) centered on each of the star and fiducial light images.
In addition to raw pixel density, the industry has unleashed a steady stream of enticing features like selfies, panorama shots, slow motion, time lapses, and so on.
Not in raw pixel count (though that's increased) but absolutely in image quality and finesse of image processing.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com