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Showing that you can jump between different types of images (clip art, line drawing, faces, etc).. 'Find Similar Images' lets you find images that are visually similar to a seed image.
Using direct algorithms for a single image, a pair or triplet of images, the global orientation is deduced sequentially [3] or hierarchically [7] starting from a seed image pair.
This segmentation technique produces two binary images referred herein as the seed image B S and the target image B T. In this case B S consists of mainly incomplete but noise-free edges, whereas B T contains complete edges and noise.
While Google's Deep Dream gets all of the hype, DCGAN offers a "purer" approach, creating images from training data alone in this case VICE covers rather than attempting to match and enhance patterns from a usually arbitrarily chosen seed image, like Deep Dream.
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Harold Chrestin is thanked for the spread out seed images.
Structured for download are files containing phenotype measurements, all sets of seed images, and the seed trait measuring tool.
ImageJ output for the measures of image sets were exported to a spreadsheet where values for seed images with poor outlines were removed based on visual observation.
To identify discolored seeds, images were captured and processed to detect dark areas on the surface of the seeds.
Invaded cells were stained with toluidine blue 24 hr post-seeding, imaged, and quantified.
Ten to 14 days after seeding images were taken for quantification of spheroid size and branching.
For the 4-mm acquisition, theoretical contrast calculations suggest that PA imaging would have still outperformed US imaging had the seed been imaged in the short-axis orientation, with a slightly reduced contrast improvement (i.e., PA imaging over US imaging) of 24.4 dB (compared to a 27.9-dB improvement) being realized in this case.
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