Object tracking and pose estimation using light-field object models

Geometric object models have been widely used for visual object tracking. In this contribution we present particle filter based object tracking with pose estimation using an appearance based lightfield object model. A light-field is an image-based object representation which can be used to render a photo realistic view of an arbitrarily shaped object from arbitrary viewpoints. It is shown how lightfield object models can be generated and utilized. Furthermore, we show how these models fit into the probabilistic framework of dynamic state estimation by defining an appropriate likelihood distribution from an image similarity metric. Finally, we present results and accuracy evaluations from tracking experiments of different objects.

Reliable and efficient reaction path and transition state finding for surface reactions with the growing string method

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136310/1/jcc24720_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136310/2/jcc24720.pd

Statistical Modelling of Craniofacial Shape

With prior knowledge and experience, people can easily observe rich shape and texture variation for a certain type of objects, such as human faces, cats or chairs, in both 2D and 3D images. This ability helps us recognise the same person, distinguish different kinds of creatures and sketch unseen samples of the same object class. The process of capturing this prior knowledge is mathematically interpreted as statistical modelling. The outcome is a morphable model, a vector space representation of objects, that captures the variation of shape and texture. This thesis presents research aimed at constructing 3DMMs of craniofacial shape and texture using new algorithms and processing pipelines to offer enhanced modelling abilities over existing techniques. In particular, we present several fully automatic modelling approaches and apply them to a large dataset of 3D images of the human head, the Headspace dataset, thus generating the first public shape-and- texture 3D Morphable Model (3DMM) of the full human head. We call this the Liverpool-York Head Model, reflecting the data collection and statistical modelling respectively. We also explore the craniofacial symmetry and asymmetry in template morphing and statistical modelling. We propose a Symmetry-aware Coherent Point Drift (SA-CPD) algorithm, which mitigates the tangential sliding problem seen in competing morphing algorithms. Based on the symmetry-constrained correspondence output of SA-CPD, we present a symmetry-factored statistical modelling method for craniofacial shape. Also, we propose an iterative process of refinement for a 3DMM of the human ear that employs data augmentation. Then we merge the proposed 3DMMs of the ear with the full head model. As craniofacial clinicians like to look at head profiles, we propose a new pipeline to build a 2D morphable model of the craniofacial sagittal profile and augment it with profile models from frontal and top-down views. Our models and data are made publicly available online for research purposes

The polyhedral Gauss map and discrete curvature measures in geometric modelling.

The Work in this thesis is concentrated on the study of discrete curvature as an important geometric property of objects, useful in describing their shape. The main focus is on the study of the methods to measure the discrete curvature on polyhedral surfaces. The curvatures associated with a polyhedral surface are concentrated around its vertices and along its edges. An existing method to evaluate the curvature at a vertex is the Angle Deficit, which also characterises vertices into flat, convex or saddle. In discrete surfaces other kinds of vertices are possible which this method cannot identify. The concept of Total Absolute Curvature (TAC) has been established to overcome this limitation, as a measure of curvature independent of the orientation of local geometry. However no correct implementation of the TAC exists for polyhedral surfaces, besides very simple cases.For two-dimensional discrete surfaces in space, represented as polygonal meshes, the TAC is measured by means of the Polyhedral Gauss Map (PGM) of vertices. This is a representation of the curvature of a vertex as an area on the surface of a sphere. Positive and negative components of the curvature of a vertex are distinguished as spherical polygons on the PGM. Core contributions of this thesis are the methods to identify these polygons and give a sign to them. The PGM provides a correct characterisation of vertices of any type, from basic convex and saddle types to complex mixed vertices, which have both positive and negative curvature in them.Another contribution is a visualisation program developed to show the PGM using 3D computer graphics. This program helps in the understanding and analysis of the results provided by the numerical computations of curvature. It also provides interactive tools to show the detailed information about the curvature of vertices.Finally a polygon simplification application is used to compare the curvature measures provided by the Angle Deficit and PGM methods. Various sample meshes are decimated using both methods and the simplified results compared with the original meshes. These experiments show how the TAC can be used to more effectively preserve the shape of an object. Several other applications that benefit in a similar way with the use of the TAC as a curvature measure are also proposed