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    Detection and localization of specular surfaces using image motion cues
    (Springer, 2014) Yilmaz, Ozgur; Doerschner, Katja
    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. 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 specularity (Doerschner et al. in Curr Biol, 2011). The algorithm combines epipolar deviations (Swaminathan et al. in Lect Notes Comput Sci 2350:508-523, 2002) and appearance distortion, and succeeds in localizing specular objects in computer-rendered and real scenes, across a wide range of camera motions and speeds, object sizes and shapes, and performs well under image noise and blur conditions.
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    Effects of surface reflectance and 3D shape on perceived rotation axis
    (Assoc Research Vision Ophthalmology Inc, 2013) Doerschner, Katja; Yilmaz, Ozgur; Kucukoglu, Gizem; Fleming, Roland W.
    Surface specularity distorts the optic flow generated by a moving object in a way that provides important cues for identifying surface material properties (Doerschner, Fleming et al., 2011). Here we show that specular flow can also affect the perceived rotation axis of objects. In three experiments, we investigate how three-dimensional shape and surface material interact to affect the perceived rotation axis of unfamiliar irregularly shaped and isotropic objects. We analyze observers' patterns of errors in a rotation axis estimation task under four surface material conditions: shiny, matte textured, matte untextured, and silhouette. In addition to the expected large perceptual errors in the silhouette condition, we find that the patterns of errors for the other three material conditions differ from each other and across shape category, yielding the largest differences in error magnitude between shiny and matte, textured isotropic objects. Rotation axis estimation is a crucial implicit computational step to perceive structure from motion; therefore, we test whether a structure from a motion-based model can predict the perceived rotation axis for shiny and matte, textured objects. Our model's predictions closely follow observers' data, even yielding the same reflectance-specific perceptual errors. Unlike previous work (Caudek & Domini, 1998), our model does not rely on the assumption of affine image transformations; however, a limitation of our approach is its reliance on projected correspondence, thus having difficulty in accounting for the perceived rotation axis of smooth shaded objects and silhouettes. In general, our findings are in line with earlier research that demonstrated that shape from motion can be extracted based on several different types of optical deformation (Koenderink & Van Doorn, 1976; Norman & Todd, 1994; Norman, Todd, & Orban, 2004; Pollick, Nishida, Koike, & Kawato, 1994; Todd, 1985).