Executive Summary

Improvised Explosive Devices (IEDs), in different forms, are a favorite weapon for global in- surgents and terrorists, with an average of 260 IED incidents per month in Afghanistan and Iraq for the time from January 2004 until May 2010. Terrorist’s ability to quickly change the IED type requires a flexible approach in IED detection training. To create an effective training tool, an image taken in the field must be segmented into two sections, separating the IED and the image background.

Read more: Master Thesis: Image segmentation for improvised explosive devices