Research Article Open Access

Image Thresholding Using the Complement Feature

Salah Ameer1
  • 1 Ontario Colleges, Canada


A new feature (the complement feature) is proposed in an Eigen formulations for performing global image thresholding. The goal is to find an intensity or gray-level value below which is the background while above it is the foreground (object). Each pixel in the image is represented by a (2D) unit vector where the x-component is the normalized (to [0,1] or [-1,1]) intensity of the pixel, while the y-component is its complement (e.g., Euclidian L2-Norm). The correlation matrix can then constructed to find the cross-correlation, Eigen vectors (axes of inertia) and Eigen values (description of respective sizes). Several implementations for each of the three previously mentioned categories are proposed to perform image thresholding. Interestingly, some of the proposed implementations do not require exhaustive search and a direct solution can be obtained. The results are promising on a wide range of images as demonstrated by comparison with the well-known Otsu method.

American Journal of Engineering and Applied Sciences
Volume 13 No. 2, 2020, 311-317


Submitted On: 10 May 2020 Published On: 8 June 2020

How to Cite: Ameer, S. (2020). Image Thresholding Using the Complement Feature. American Journal of Engineering and Applied Sciences, 13(2), 311-317.

  • 2 Citations



  • Image Thresholding
  • Image Segmentation
  • Cross-Correlation
  • Eigen Value