Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robustly estimating changes in image appearance
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
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
Color Illumination Models for Image Matching and Indexing
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Computation of optical flow under non-uniform brightness variations
Pattern Recognition Letters
Homography-based 2D Visual Tracking and Servoing
International Journal of Robotics Research
Efficient cumulative matching for image registration
Image and Vision Computing
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Since modeling reflections in image processing is a difficult task, most computer vision algorithms assume that objects are Lambertian and that no lighting change occurs. Some photometric models can partly answer this issue by assuming that the lighting changes are the same at each point of a small window of interest. Through a study based on specular reflection models, we explicit the assumptions on which these models are implicitly based and the situations in which they could fail. This paper proposes two photometric models, which compensate for specular highlights and lighting variations. They assume that photometric changes vary smoothly on the window of interest. Contrary to classical models, the characteristics of the object surface and the lighting changes can vary in the area being observed. First, we study the validity of these models with respect to the acquisition setup: relative locations between the light source, the sensor and the object as well as the roughness of the surface. Then, these models are used to improve feature points tracking by simultaneously estimating the photometric and geometric changes. The proposed methods are compared to well-known tracking methods robust to affine photometric changes. Experimental results on specular objects demonstrate the robustness of our approaches to specular highlights and lighting changes.