Detecting targets from infrared (IR) images in the absence of a priori information is a very difficult task. In this paper, we present an unsupervised detection algorithm based on Gabor functions for detecting targets from a single IR image frame. The only explicit assumption made is that the targets can be considered a rectangle. The algorithm consists of three steps. First, it locates potential targets based on a rectangle matching pattern by using low resolution Gabor functions which resist noise and background clutter effects. Then it removes false targets and eliminates redundant target points based on a similarity measure. These two steps mimic human vision processing but are different from Zeevi's Foveating Vision System. Finally, it uses both low and high resolution Gabor functions to verify target existence. This algorithm has been successfully tested on several IR images that contain multiple examples of military vehicles and aircraft with different size and brightness in various background scenes and orientations.