AbstractsComputer Science

I present a new method of visual search for objects that include straight lines. This is usually the case for machine-made objects. I describe existing machine vision search methods and show how my method of visual search gives better performance on objects that have straight lines. Inspired from human vision, a two-step process is used. First, straight line segments are detected in an image and characterized by their length, mid-point location, and orientation. Second, hypotheses that a particular straight line segment belongs to a known object are generated and tested. The set of hypotheses is constrained by spatial relationships in the known objects. I discuss implementation of my method and its performance and limitations in real and synthetic images. The speed and robustness of my method make it immediately applicable to many machine vision problems.