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Human vision systems have the advantage of learning from a lifetime of experiences how to contextualize the things we see. Machine learning models, on the other hand, usually need a substantial number of real-world scenarios to learn from to be able to product reliable computer vision (CV) outputs. These examples may come in many forms:

  • 2D images and video (taken from an SLR or infrared camera)
  • 3-D images and video (taken from a camera or scanner)
  • Sensor data (taken from RADAR or LiDAR technology)
  • Sometimes a mix of the above

High-quality data is foundational to effective CV systems. Today, with the right training data, a Computer Vision system can recognize objects in images and video, including their shapes, textures, colors, sizes, locations, movements, and other relevant characteristics.

This Computer Vision eBook is focused on a data-centric approach to model development, which consists of systematically changing and enhancing datasets to improve output accuracy, as opposed to adjusting the models.

Vendor:
Appen
Posted:
Nov 24, 2021
Published:
Nov 23, 2021
Format:
PDF
Type:
eBook

This resource is no longer available.