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A stereoscope presents 2D images of the same object from slightly different angles to the left eye and the right eye, allowing the viewer to reconstruct the original object via binocular disparity. When viewed with the proper vergence, an autostereogram does the same, the binocular disparity existing in adjacent parts of the repeating 2D patterns.
The Messier catalogue is one of the most famous lists of astronomical objects, and many objects on the list are still referenced by their Messier numbers. [1] The catalogue includes most of the astronomical deep-sky objects that can be easily observed from Earth's Northern Hemisphere; many Messier objects are popular targets for amateur ...
Lavarand, also known as the Wall of Entropy, is a hardware random number generator designed by Silicon Graphics that worked by taking pictures of the patterns made by the floating material in lava lamps, extracting random data from the pictures, and using the result to seed a pseudorandom number generator. [1]
The objective of this puzzle from UK-based fostering agency Perpetual Fostering is simple: Find the single witch hat among the cats. There are plenty of non-cat objects that stand out immediately ...
List of NGC objects. List of NGC objects (1–1000) List of NGC objects (1001–2000) List of NGC objects (2001–3000) List of NGC objects (3001–4000) List of NGC objects (4001–5000) List of NGC objects (5001–6000) List of NGC objects (6001–7000) List of NGC objects (7001–7840) List of IC objects; List of Messier objects; List of ...
Individual polygons are named (and sometimes classified) according to the number of sides, combining a Greek-derived numerical prefix with the suffix -gon, e.g. pentagon, dodecagon. The triangle, quadrilateral and nonagon are exceptions, although the regular forms trigon, tetragon, and enneagon are sometimes encountered as well.
1.1 Polygons with specific numbers of sides. 2 Curved. ... For mathematical objects in more dimensions, ... Glossary of shapes with metaphorical names;
In 2016, Reed, Akata, Yan et al. became the first to use generative adversarial networks for the text-to-image task. [5] [7] With models trained on narrow, domain-specific datasets, they were able to generate "visually plausible" images of birds and flowers from text captions like "an all black bird with a distinct thick, rounded bill".