Regression modeling is used to estimate a numerical value instead of making a decision. It is often adopted in quality estimation.

perClass Mira implements object-centered regression modelling. Individual objects can be annotated with numerical meta-data. A regression model is build that can be applied to a new object.

This enables practical applications such as:

  • Estimate brix (sugar) content in a tomato
  • Estimate a dry-matter content of a plant leaf
  • Estimate moisture content in biscuits

Regression example: Powder mixture

In this example, we will use a mixture of sodium carbonate and flour. A set of vials with different mixing proportions is scanned using Specim FX17 camera in the range between 900 and 1700nm.

In order to estimate mixing proportion we:

  1. Build a classification model that identifies area of interest for regression
  2. Define the class of interest as foreground so that objects can be segmented out
  3. Annotate number of objects with known ground-truth values for regression
  4. Build a regression model
  5. Flag some of the images for testing only to judge generalization performance
  6. Improve the model
  7. Apply the solution to a new hyperspectral scan (objects get identified and for each a value is estimated)