Constraining Object Features Using a Polarization Reflectance Model
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
Geometry and photometry in three-dimensional visual recognition
Geometry and photometry in three-dimensional visual recognition
Object shape and reflectance modeling from observation
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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We propose a new method for classification of photometric factors, such as diffuse reflection, specular reflection, attached shadow, and cast shadow. For analyzing real images, we utilize the photometric linearization method which was originally proposed for image synthesis. First, we show that each pixel can be photometrically classified by the simple comparison of the pixel intensity. Our classification algorithm requires neither 3D shape information nor color information of the scene. Then, we show that the accuracy of the photometric linearization can be improved by introducing a new classification-based criterion to the linearization process. Experimental results show that photometric factors can be correctly classified without any special device.