Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1 results

  • 9 - Image Data

  • from Part II - Case Studies
  • Pablo Duboue
  • Book:
    The Art of Feature Engineering
    Published online:
    29 May 2020
    Print publication:
    25 June 2020, pp 212-232

  • Summary

    The last chapter using the dataset from chapter 6, this time the dataset is expanded using 80 thousand satellite images obtained from NASA for relative humidity density as it relates to vegetation. This image information is not visible information and thus pre-trained models are not useful for this problem. Instead, traditional computer vision techniques are showcased, involving histograms, local feature extraction, gradients and histograms of gradients. This domain exemplifies how to deal with high level of nuisance noise, how to normalize your features so that a small changes in the way the information was acquired does not completely throws off the underlining machine learning mechanism. These takeaways are valuable for anybody working with sensor data where the acquisition process has a high level of uncertainty. The domain also exemplifies working with large number of low-level sensor data.