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In contrast, augmented reality – in which computer graphics are layered onto a real world image – was the boring sub-technology confined to sports footage replays and technical engineering.
Experiments on real world image datasets demonstrate the effectiveness of our novel approach for image retrieval.
The second challenge comes from the large intraclass variance and interclass relationship in real world image databases.
Fig. 1 This figure illustrates that intra class variance and inter class relationship are large in real world image datasets.
Augmented Reality is the combination of virtual objects (created by computer i.e. video, texts or 3D computer models) overlay on top of real world image.
The second principal challenge in real world image classification is the existence of large intra class variance and large interclass relationship between images.
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Classifying real world images is a challenging task.
Experiments have also been carried out on real world images in order to validate the proposed method.
However in real world images, the distance to the camera is never zero (d > 0), therefore 0 ≤ t < 1.
Experiments on our collected real world images show that our method can accurately recover human body from static images with a variety of individuals, poses, backgrounds and clothing.
Experiments have been conducted using simulated and real world images to evaluate the performance of the proposed method and the results are presented.
Write better and faster with AI suggestions while staying true to your unique style.
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