@conference {12452,
	title = {Towards view-invariant expression analysis using analytic shape manifolds},
	booktitle = {2011 IEEE International Conference on Automatic Face \& Gesture Recognition and Workshops (FG 2011)},
	year = {2011},
	month = {2011/03/21/25},
	pages = {306 - 313},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Facial expression analysis is one of the important components for effective human-computer interaction. However, to develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera i.e. expression models should generalize across facial poses. To perform this systematically, one needs to understand the space of observed images subject to projective transformations. However, since the projective shape-space is cumbersome to work with, we address this problem by deriving models for expressions on the affine shape-space as an approximation to the projective shape-space by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. We use landmark configurations to represent facial deformations and exploit the fact that the affine shape-space can be studied using the Grassmann manifold. This representation enables us to perform various expression analysis and recognition algorithms without the need for the normalization as a preprocessing step. We extend some of the available approaches for expression analysis to the Grassmann manifold and experimentally show promising results, paving the way for a more general theory of view-invariant expression analysis.},
	keywords = {Databases, Deformable models, Face, face recognition, facial expression analysis, Geometry, Gold, Human-computer interaction, Manifolds, projective transformation, Riemannian interpretation, SHAPE, view invariant expression analysis},
	isbn = {978-1-4244-9140-7},
	doi = {10.1109/FG.2011.5771415},
	author = {Taheri, S. and Turaga,P. and Chellapa, Rama}
}
@conference {19637,
	title = {Geometry and Charge State of Mixed-Ligand Au13 Nanoclusters},
	booktitle = {X-RAY ABSORPTION FINE STRUCTURE - XAFS13: 13th International Conference},
	volume = {882},
	year = {2007},
	month = {2007/02/02/},
	pages = {749 - 751},
	publisher = {AIP Publishing},
	organization = {AIP Publishing},
	abstract = {The integration of synthetic, experimental and theoretical tools into a self-consistent data analysis methodology allowed us to develop unique new levels of detail in nanoparticle characterization. We describe our methods using an example of Au 13 monolayer-protected clusters (MPCs), synthesized by ligand exchange methods. The combination of atom counting methods of scanning transmission electron microscopy and Au L3-edge EXAFS allowed us to characterize these clusters as icosahedral, with surface strain reduced from 5\% (as in ideal, regular icosahedra) to 3\%, due to the interaction with ligands. Charge transfer from Au to the thiol and phosphine ligands was evidenced by S and P K-edge XANES. A comparison of total energies of bare clusters of different geometries was performed by equivalent crystal theory calculations.},
	keywords = {Atom surface interactions, Charge transfer, Data analysis, Extended X-ray absorption fine structure spectroscopy, Gold, nanoparticles, Scanning transmission electron microscopy, Surface strains, Total energy calculations, X-ray absorption near edge structure},
	url = {http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.2644652},
	author = {Frenkel, A. I. and Menard, L. D. and Northrup, P. and Rodriguez, J. A. and Zypman, F. and Dana Dachman-Soled and Gao, S.-P. and Xu, H. and Yang, J. C. and Nuzzo, R. G.}
}
@conference {18307,
	title = {3D object recognition via simulated particles diffusion},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989. Proceedings CVPR {\textquoteright}89},
	year = {1989},
	month = {1989/06//},
	pages = {442 - 449},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A novel approach for 3D object recognition is presented. This approach is model-based, and assumes either 3D or 21/2 D scene acquisition. Transformation detection is accomplished along with an object identification (six degrees of freedom, three rotational and three translational, are assumed). The diffusion-like simulation recently introduced as a means for characterization of shape is used in the extraction of point features. The point features represent regions on the object{\textquoteright}s surface that are extreme in curvature (i.e. concavities and convexities). Object matching is carried out by examining the correspondence between the object{\textquoteright}s set of point features and the model{\textquoteright}s set of point features, using an alignment strategy. Triangles are constructed between all possible triples of object{\textquoteright}s point features, and then are aligned to candidate corresponding triangles of the model{\textquoteright}s point features. 21/2 range images are transformed into a volumetric representation through a parallel projection onto the 3-D space. The resultant volume is suitable for processing by the diffusion-like simulation},
	keywords = {3D object recognition, alignment strategy, Computational modeling, Computer science, data mining, Gold, Layout, Noise shaping, Object detection, Object recognition, parallel projection, pattern recognition, point features, radio access networks, scene acquisition, shape characterisation, Shape measurement, simulated particles diffusion, transformation detection},
	isbn = {0-8186-1952-x},
	doi = {10.1109/CVPR.1989.37886},
	author = {Yacoob,Yaser and Gold,Y. I}
}
