Surface Reflection: Physical and Geometrical Perspectives
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining Reflectance Properties of an Object Using Range and Brightness Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring and modeling anisotropic reflection
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Geometry and photometry in three-dimensional visual recognition
Geometry and photometry in three-dimensional visual recognition
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Reflectance based object recognition
International Journal of Computer Vision
A Theory of Specular Surface Geometry
International Journal of Computer Vision
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
A Reflectance Model for Computer Graphics
ACM Transactions on Graphics (TOG)
Texture and reflection in computer generated images
Communications of the ACM
Illumination for computer generated pictures
Communications of the ACM
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Voxel Carving for Specular Surfaces
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Incorporating the Torrance and Sparrow Model of Reflectance in Uncalibrated Photometric Stereo
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Using Specularities for Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Extracting layers and analyzing their specular properties using epipolar-plane-image analysis
Computer Vision and Image Understanding
Local Shape from Mirror Reflections
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Non-Negative Lighting and Specular Object Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
On the Equivalence of Common Approaches to Lighting Insensitive Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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We aim to create systems that identify and locate objects by comparing known, 3D shapes to intensity images that they have produced. To do this we focus on verification methods that determine whether a known model in a specific pose is consistent with an image. We build on prior work that has done this successfully for Lambertian objects, to handle a much broader class of shiny objects that produce specular highlights. Our core contribution is a novel method for determining whether a known 3D shape is consistent with the 2D shape of a possible highlight found in an image. We do this using only a qualitative description of highlight formation that is consistent with most models of specular reflection, so no specific knowledge of an object's specular reflectance properties is needed. This allows us to treat non-Lambertian image effects as a positive source of information about object identity, rather than treating them as a potential source of noise. We then show how to integrate information about highlights into a system that also checks the consistency of Lambertian reflectance effects. Also, we show how to model Lambertian reflectance using a reference image, rather than albedos, which can be difficult to measure in shiny objects. We test each aspect of our approach using several different data sets. We demonstrate the potential value of our method of handling specular highlights by building a system that can locate shiny, transparent objects, such as glassware, on table tops. We demonstrate our hybrid methods on pottery, and our use of reference images with face recognition experiments.