@conference {12589,
	title = {Symmetric Objects are Hardly Ambiguous},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 7},
	abstract = {Given any two images taken under different illumination conditions, there always exist a physically realizable object which is consistent with both the images even if the lighting in each scene is constrained to be a known point light source at infinity. In this paper, we show that images are much less ambiguous for the class of bilaterally symmetric Lambertian objects. In fact, the set of such objects can be partitioned into equivalence classes such that it is always possible to distinguish between two objects belonging to different equivalence classes using just one image per object. The conditions required for two objects to belong to the same equivalence class are very restrictive, thereby leading to the conclusion that images of symmetric objects are hardly ambiguous. The observation leads to an illumination-invariant matching algorithm to compare images of bilaterally symmetric Lambertian objects. Experiments on real data are performed to show the implications of the theoretical result even when the symmetry and Lambertian assumptions are not strictly satisfied.},
	keywords = {algorithm;symmetric, bilaterally, images;image, Lambertian, MATCHING, matching;, objects, objects;illumination-invariant, objects;symmetric, symmetric},
	doi = {10.1109/CVPR.2007.383289},
	author = {Aggarwal,G. and Biswas,S. and Chellapa, Rama}
}
@conference {13161,
	title = {Tracking Articulating Objects from Ground Vehicles using Mixtures of Mixtures},
	booktitle = {Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {1230 - 1236},
	abstract = {An algorithm for tracking articulating objects from moving camera platforms is presented. Mixtures of mixtures are used to model the appearance of the object and the background. The state of the object is tracked using a particle filter. Egomotion information are estimated and used to set the state variance of the particle filter. Results of tracking human objects from an unmanned ground vehicle are used to evaluate the tracking algorithm},
	keywords = {articulating, filter;unmanned, ground, information;mixtures, mixtures;particle, objects, of, operated, tracking;egomotion, vehicle;remotely, vehicles;robot, vision;},
	doi = {10.1109/IROS.2006.281881},
	author = {Abd-Almageed, Wael and Hussein,M. and Davis, Larry S.}
}
