@conference {12701,
	title = {Illuminating light field: image-based face recognition across illuminations and poses},
	booktitle = {Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on},
	year = {2004},
	month = {2004/05//},
	pages = {229 - 234},
	abstract = {We present an image-based method for face recognition across different illuminations and different poses, where the term {\textquoteright}image-based{\textquoteright} means that only 2D images are used and no explicit 3D models are needed. As face recognition across illuminations and poses involves three factors, namely identity, illumination, and pose, generalizations from known identities to novel identities, from known illuminations to novel illuminations, and from known poses to unknown poses are desired. Our approach, called the illuminating light field, derives an identity signature that is invariant to illuminations and poses, where a subspace encoding is assumed for the identity, a Lambertain reflectance model for the illumination, and a light field model for the poses. Experimental results using the PIE database demonstrate the effectiveness of the proposed approach.},
	keywords = {Face, field;, illuminating, image-based, Lambertain, light, lighting;, model;, multidimensional, poses;, processing;, recognition;, reflectance, reflectivity;, signal},
	doi = {10.1109/AFGR.2004.1301536},
	author = {Zhou,Shaohua and Chellapa, Rama}
}
@conference {12718,
	title = {Rank constrained recognition under unknown illuminations},
	booktitle = {Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on},
	year = {2003},
	month = {2003/10//},
	pages = {11 - 18},
	abstract = {Recognition under illumination variations is a challenging problem. The key is to successfully separate the illumination source from the observed appearance. Once separated, what remains is invariant to illuminant and appropriate for recognition. Most current efforts employ a Lambertian reflectance model with varying albedo field ignoring both attached and cast shadows, but restrict themselves by using object-specific samples, which undesirably deprives them of recognizing new objects not in the training samples. Using rank constraints on the albedo and the surface normal, we accomplish illumination separation in a more general setting, e.g., with class-specific samples via a factorization approach. In addition, we handle shadows (both attached and cast ones) by treating them as missing values, and resolve the ambiguities in the factorization method by enforcing integrability. As far as recognition is concerned, a bootstrap set which is just a collection of two-dimensional image observations can be utilized to avoid the explicit requirement that three-dimensional information be available. Our approaches produce good recognition results as shown in our experiments using the PIE database.},
	keywords = {albedo, approach;, constrained, database;, databases;, decomposition;, factorization, field;, illumination, image;, information;, Lambertian, lighting;, model;, object, object-specific, PIE, rank, recognition;, reflectance, samples;, singular, three-dimensional, two-dimensional, value, variations;, visual},
	doi = {10.1109/AMFG.2003.1240818},
	author = {Zhou, S. and Chellapa, Rama}
}
@conference {14858,
	title = {Using specularities for recognition},
	booktitle = {Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on},
	year = {2003},
	month = {2003/10//},
	pages = {1512 -1519 vol.2 - 1512 -1519 vol.2},
	abstract = {Recognition systems have generally treated specular highlights as noise. We show how to use these highlights as a positive source of information that improves recognition of shiny objects. This also enables us to recognize very challenging shiny transparent objects, such as wine glasses. Specifically, we show how to find highlights that are consistent with a hypothesized pose of an object of known 3D shape. 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{\textquoteright}s reflectance properties is needed. We first present a method that finds highlights produced by a dominant compact light source, whose position is roughly known. We then show how to estimate the lighting automatically for objects whose reflection is part specular and part Lambertian. We demonstrate this method for two classes of objects. First, we show that specular information alone can suffice to identify objects with no Lambertian reflectance, such as transparent wine glasses. Second, we use our complete system to recognize shiny objects, such as pottery.},
	keywords = {3D, formation;object, glass;computer, image, information;specular, light, measurement;reflection;stereo, objects;specular, objects;wine, processing;, property;pottery;recognition, recognition;object, recognition;position, reflectance, reflection;compact, reflection;transparent, shape;Lambertian, source;highlight, systems;shiny, vision;lighting;object},
	doi = {10.1109/ICCV.2003.1238669},
	author = {Osadchy,M. and Jacobs, David W. and Ramamoorthi,R.}
}
@conference {14843,
	title = {Comparing images under variable illumination},
	booktitle = {Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on},
	year = {1998},
	month = {1998/06//},
	pages = {610 - 617},
	abstract = {We consider the problem of determining whether two images come from different objects or the same object in the same pose, but under different illumination conditions. We show that this problem cannot be solved using hard constraints: even using a Lambertian reflectance model, there is always an object and a pair of lighting conditions consistent with any two images. Nevertheless, we show that for point sources and objects with Lambertian reflectance, the ratio of two images from the same object is simpler than the ratio of images from different objects. We also show that the ratio of the two images provides two of the three distinct values in the Hessian matrix of the object{\textquoteright}s surface. Using these observations, we develop a simple measure for matching images under variable illumination, comparing its performance to other existing methods on a database of 450 images of 10 individuals},
	keywords = {conditions;matching, illumination;visual, images;point, Lambertian, matching;object, model;database;lighting, object, recognition;, recognition;image, reflectance, sources;pose;variable},
	doi = {10.1109/CVPR.1998.698668},
	author = {Jacobs, David W. and Belhumeur,P. N. and Basri,R.}
}
@conference {14956,
	title = {Efficient algorithms for estimating atmospheric parameters for surface reflectance retrieval},
	booktitle = {Parallel Processing, 1996., Proceedings of the 1996 International Conference on},
	volume = {2},
	year = {1996},
	month = {1996/08//},
	pages = {132 -141 vol.2 - 132 -141 vol.2},
	abstract = {The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects. We introduce an efficient algorithm to estimate the optical characteristics of the TM imagery and to remove the atmospheric effects from it. Our algorithm introduces a set of techniques to significantly improve the quality of the retrieved images. We pay a particular attention to the computational efficiency of the algorithm thereby allowing us to correct large TM images quite fast. We also provide a parallel implementation of our algorithm and show its portability and scalability on several parallel machines},
	keywords = {Atmospheric, correction;atmospheric, effects;atmospheric, imagery;scalability;surface, parameters;portability;remotely, reconstruction;photoreflectance;remote, reflectance, reflectance;surface, retrieval;image, sensed, sensing;},
	doi = {10.1109/ICPP.1996.537392},
	author = {Fallah-Adl,H. and JaJa, Joseph F. and Liang,Shunlin}
}
