The information available to a moving observer from specularities
Image and Vision Computing - 4th Alvey Vision Meeting
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Planning multiple observations for object recognition
International Journal of Computer Vision - Special issue on active vision II
Voxel Carving for Specular Surfaces
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Transparent Surface Modeling from a Pair of Polarization Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
What do reflections tell us about the shape of a mirror?
APGV '04 Proceedings of the 1st Symposium on Applied perception in graphics and visualization
Local Shape from Mirror Reflections
International Journal of Computer Vision
Modelling Reflections via Multiperspective Imaging
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Sketching shiny surfaces: 3D shape extraction and depiction of specular surfaces
ACM Transactions on Applied Perception (TAP)
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
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Image invariants are those properties of the images of an object that remain unchanged with changes in camera parameters, illumination etc. In this paper, we derive an image invariant for smooth surfaces with mirror-like reflectance. Since, such surfaces do not have an appearance of their own but rather distort the appearance of the surrounding environment, the applicability of geometric invariants is limited. We show that for such smooth mirror-like surfaces, the image gradients exhibit degeneracy at the surface points that are parabolic. We leverage this result in order to derive a photometric invariant that is associated with parabolic curvature points. Further, we show that these invariant curves can be effectively extracted from just a few images of the object in uncontrolled, uncalibrated environments without the need for any a priori information about the surface shape. Since these parabolic curves are a geometric property of the surface, they can then be used as features for a variety of machine vision tasks. This is especially powerful, since there are very few vision algorithms that can handle such mirror-like surfaces. We show the potential of the proposed invariant using experiments on two related applications - object recognition and pose estimation for smooth mirror surfaces.