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This is an old revision of this page, as edited by SewerCat (talk | contribs) at 19:15, 30 January 2017 (reword first sentence for clarity and link to 'startup company'). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Image2Text technology was created by Cortica, an Israel-based startup whose technology simulates the performance of the human cortex so that computers recognize images with a high degree of accuracy.[1] Image2Text is the result of 10 years in research and development and is protected by more than 50 patents.[2]

Differentiation

Cortica's engine processes and recognizes images based on patterns, just like the brain does, providing accuracy purporting to be comparable with that of the human brain.[3]

Previous image search solutions that have relied on databases of images complied through fingerprinting, modeling and crowdsourcing.[4] Cortica patterns are clustered into digital concepts. Those are then stored and mapped to keywords and contextual taxonomies that enable it to interpret the content appearing in digital media.[5]

Uses

Cortica's Image2Text technology associates images with concepts and enables a host of business opportunities.[6] The technology has implications for Augmented Reality,[7] a visual technology that experts say will improve when it incorporates computer vision and dynamic mapping of the real world environment.[8] In addition, computer vision technologies, like those guided by Image2Text, have been integrated into self-driving cars to help identify hazards on the road.[9]

References

  1. ^ Yeung, Ken. "Israel-based Cortica raises $1.5M from Mail.Ru to fund its Image2Text visual search technology". TheNextWeb. Retrieved 20 January 2017.
  2. ^ "Visual Search Leader, Cortica, Secures $6.4 Million in Series B Financing Led by Horizons Ventures; Funding Totals $18M to Date". BusinessWire. Retrieved 20 January 2017.
  3. ^ Yeung, Ken. "Israel-based Cortica raises $1.5M from Mail.Ru to fund its Image2Text visual search technology". TheNextWeb. Retrieved 20 January 2017.
  4. ^ Chen, David. "Memory Efficient Image Databases for Mobile Visual Search" (PDF). Stanford University. IEEE Journal. Retrieved 20 January 2017.
  5. ^ Bermant, Yoel. "Igal Raichelgauz Raises $20 Million In Series C Funding For Cortica, Image Identification Technology". Jewish Business News. Retrieved 20 January 2017.
  6. ^ "Visual Search Leader, Cortica, Secures $6.4 Million in Series B Financing Led by Horizons Ventures; Funding Totals $18M to Date". BusinessWire. Retrieved 20 January 2017.
  7. ^ Raichelgauz, Igal. "Pokémon Go is nice, but here's what *real* augmented reality will look like". VentureBeat.
  8. ^ Dhillon, Sunny. "Stop referring to Pokémon Go as augmented reality". VentureBeat. Retrieved 20 January 2017.
  9. ^ Els, Peter. "How AI is Making Self-Driving Cars Smarter". RoboticsTrends. Retrieved 20 January 2017.
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