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A Rube Goldberg machine, named after American cartoonist Rube Goldberg, is a chain reaction–type machine or contraption intentionally designed to perform a simple task in an indirect and (impractically) overly complicated way. Usually, these machines consist of a series of simple unrelated devices; the action of each triggers the initiation ...
These machines and their nanoscale dynamics are far more complex than any molecular machines that have yet been artificially constructed. [81] Biological machines have potential applications in nanomedicine. [82] For example, they could be used to identify and destroy cancer cells.
Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. [1] Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face .
Kirlian photograph of two coins. Kirlian photography is a collection of photographic techniques used to capture the phenomenon of electrical coronal discharges.It is named after Soviet scientist Semyon Kirlian, who, in 1939, accidentally discovered that if an object on a photographic plate is connected to a high-voltage source, an image is produced on the photographic plate. [1]
The following outline is provided as an overview of and topical guide to machines: Machine – mechanical system that provides the useful application of power to achieve movement. A machine consists of a power source, or engine, and a mechanism or transmission for the controlled use of this power.
This spiral imaging sequence acquires images faster than gradient-echo sequences, but needs more math transformations (and consequent assumptions) since converting back to voxel space requires the data be in grid form (a set of equally spaced points in both horizontal and vertical directions).
Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos.From the perspective of engineering, it seeks to automate tasks that the human visual system can do.
In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative models, as opposed to discriminative ones, for complex data such as images. These deep generative models were the first to output not only class labels for images but also ...