Convexity-Based Visual Camouflage Breaking

Abstract

Camouflage is frequently used by animals and humans (usually for military purposes) in order to conceal objects from visual surveillance or inspection. Most camouflage methods are based on superposing multiple edges on the object that is supposed to be hidden, such that its familiar contours and texture are masked. In this work, we present an operator, Darg, that is applied directly to the intensity image in order to detect 3D smooth convex (or equivalently: concave) objects. The operator maximally responds to a local intensity configuration that corresponds to curved 3D objects, and thus, is used to detect curved objects on a relatively flat background, regardless of image edges, contours, and texture. In that regard, we show that a typical camouflage found in some animal species seems to be a “counter measure” taken against detection that might be based on our method. Detection by Darg is shown to be very robust, from both theoretic considerations and practical examples of real-life images. As a part of the camouflage breaking demonstration, Darg, which is non-edge-based, is compared with a representative edge-based operator. Better performance is maintained by Darg for both animal and military camouflage breaking.