A motion feature-based algorithm for the detection of specular objects in natural scenes

Abstract

Successful identification of specularities in an image can be crucial for an artificial vision system when extracting the semantic content of an image or while interacting with the environment. We developed an algorithm that relies on scale and rotation invariant feature extraction techniques and uses motion cues to detect and localize specular surfaces in an office scene. Appearance change in feature vectors is used to quantify the appearance distortion on specular surfaces, which has previously been shown to be a powerful indicator for shininess (Doerschner et al, 2011 Current Biology 21(23), 2010–2016). The algorithm combines epipolar deviations (Swaminathan et al, 2002 ECCV167–172) and appearance distortion cues and succeeds in localizing specular objects across a wide range of camera motions and speeds, object sizes and shapes, and performs well under image noise and blur conditions.