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01551 am a22001933u 4500 |
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103786 |
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|a dc
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|a Efros, Alyosha
|e author
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Torralba, Antonio
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|a Torralba, Antonio
|e author
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|a Guest Editorial: Big Data
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|b Springer US,
|c 2016-07-21T16:50:59Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/103786
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|a Computer vision has a split personality. Within the same field, and largely guided by the same set of fundamental algorithms, it combines two problems that are utterly disparate in their aims and philosophy-here we will call them "Vision as Measurement" and "Vision as Understanding". Measurement problems deal with obtaining objective, quantifiable information about the physical world (e.g. scene depth in meters, visual angle in radians, light-source brightness in candelas-per-meter-squared, etc.). Measurement problems are akin to physics-they are well-posed and the validity of a solution can always be tested with an experiment. Employing careful physical or geometric modeling and rigorous mathematics, this area has been quite successful in solving a number of important problems, such as stereo and structure-from-motion.
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|a en
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|a Article
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|t International Journal of Computer Vision
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