Detection of regions of interest and camouflage breaking by direct convexity estimation

Detection of regions of interest is usually based on edge maps. We suggest a novel nonedge-based mechanism for detection of regions of interest, which extracts 3D information from the image. Our operator detects smooth 3D convex and concave objects based on direct processing of intensity values. Invariance to a large family of functions is mathematically proved. It follows that our operator is robust to variation in illumination, or orientation, and scale, in contrast with most other attentional operators. The operator is also demonstrated to efficiently detect 3D objects camouflaged in noisy areas. An extensive comparison, with edge-based attentional operators is delineated.