Detecting Specular Surfaces on Natural Images

Recognizing and localizing specular (or mirror-like) surfaces from a single image is a great challenge to computer vision. Unlike other materials, the appearance of a specular surface changes as function of the surrounding environment as well as the position of the observer. Even though the reflection on a specular surface has an intrinsic ambiguity that might be resolved by high level reasoning, we argue that we can take advantage of low level features to recognize specular surfaces. This intuition stems from the observation that the surrounding scene is highly distorted when reflected off regions of high curvature or occluding contours. We call these features static specular flows (SSF). We show how to characterize SSF and use them for identifying specular surfaces. To evaluate our result we collect a dataset of 120 images containing specular surfaces. Our algorithm can achieve good performances on this challenging dataset. Particularly, our results outperform other methods that follow a more naive approach.