TY - CONF T1 - Towards view-invariant expression analysis using analytic shape manifolds T2 - 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011) Y1 - 2011 A1 - Taheri, S. A1 - Turaga,P. A1 - Chellapa, Rama KW - Databases KW - Deformable models KW - Face KW - face recognition KW - facial expression analysis KW - Geometry KW - Gold KW - Human-computer interaction KW - Manifolds KW - projective transformation KW - Riemannian interpretation KW - SHAPE KW - view invariant expression analysis AB - 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. JA - 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011) PB - IEEE SN - 978-1-4244-9140-7 M3 - 10.1109/FG.2011.5771415 ER - TY - CONF T1 - Geometry and Charge State of Mixed‐Ligand Au13 Nanoclusters T2 - X-RAY ABSORPTION FINE STRUCTURE - XAFS13: 13th International Conference Y1 - 2007 A1 - Frenkel, A. I. A1 - Menard, L. D. A1 - Northrup, P. A1 - Rodriguez, J. A. A1 - Zypman, F. A1 - Dana Dachman-Soled A1 - Gao, S.-P. A1 - Xu, H. A1 - Yang, J. C. A1 - Nuzzo, R. G. KW - Atom surface interactions KW - Charge transfer KW - Data analysis KW - Extended X-ray absorption fine structure spectroscopy KW - Gold KW - nanoparticles KW - Scanning transmission electron microscopy KW - Surface strains KW - Total energy calculations KW - X-ray absorption near edge structure AB - 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. JA - X-RAY ABSORPTION FINE STRUCTURE - XAFS13: 13th International Conference PB - AIP Publishing VL - 882 UR - http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.2644652 ER - TY - CONF T1 - 3D object recognition via simulated particles diffusion T2 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989. Proceedings CVPR '89 Y1 - 1989 A1 - Yacoob,Yaser A1 - Gold,Y. I KW - 3D object recognition KW - alignment strategy KW - Computational modeling KW - Computer science KW - data mining KW - Gold KW - Layout KW - Noise shaping KW - Object detection KW - Object recognition KW - parallel projection KW - pattern recognition KW - point features KW - radio access networks KW - scene acquisition KW - shape characterisation KW - Shape measurement KW - simulated particles diffusion KW - transformation detection AB - 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's surface that are extreme in curvature (i.e. concavities and convexities). Object matching is carried out by examining the correspondence between the object's set of point features and the model's set of point features, using an alignment strategy. Triangles are constructed between all possible triples of object's point features, and then are aligned to candidate corresponding triangles of the model'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 JA - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989. Proceedings CVPR '89 PB - IEEE SN - 0-8186-1952-x M3 - 10.1109/CVPR.1989.37886 ER -