Modelling and Animation using Partial Differential Equations. Geometric modelling and computer animation of virtual characters using elliptic partial differential equations.

This work addresses various applications pertaining to the design, modelling and animation of parametric surfaces using elliptic Partial Differential Equations (PDE) which are produced via the PDE method. Compared with traditional surface generation techniques, the PDE method is an effective technique that can represent complex three-dimensional (3D) geometries in terms of a relatively small set of parameters. A PDE-based surface can be produced from a set of pre-configured curves that are used as the boundary conditions to solve a number of PDE. An important advantage of using this method is that most of the information required to define a surface is contained at its boundary. Thus, complex surfaces can be computed using only a small set of design parameters. In order to exploit the advantages of this methodology various applications were developed that vary from the interactive design of aircraft configurations to the animation of facial expressions in a computer-human interaction system that utilizes an artificial intelligence (AI) bot for real time conversation. Additional applications of generating cyclic motions for PDE based human character integrated in a Computer-Aided Design (CAD) package as well as developing techniques to describe a given mesh geometry by a set of boundary conditions, required to evaluate the PDE method, are presented. Each methodology presents a novel approach for interacting with parametric surfaces obtained by the PDE method. This is due to the several advantages this surface generation technique has to offer. Additionally, each application developed in this thesis focuses on a specific target that delivers efficiently various operations in the design, modelling and animation of such surfaces.The project files will not be available online

A new method for generic three dimensional human face modelling for emotional bio-robots

Existing 3D human face modelling methods are confronted with difficulties in applying flexible control over all facial features and generating a great number of different face models. The gap between the existing methods and the requirements of emotional bio-robots applications urges the creation of a generic 3D human face model. This thesis focuses on proposing and developing two new methods involved in the research of emotional bio-robots: face detection in complex background images based on skin colour model and establishment of a generic 3D human face model based on NURBS. The contributions of this thesis are: A new skin colour based face detection method has been proposed and developed. The new method consists of skin colour model for skin regions detection and geometric rules for distinguishing faces from detected regions. By comparing to other previous methods, the new method achieved better results of detection rate of 86.15% and detection speed of 0.4-1.2 seconds without any training datasets. A generic 3D human face modelling method is proposed and developed. This generic parametric face model has the abilities of flexible control over all facial features and generating various face models for different applications. It includes: The segmentation of a human face of 21 surface features. These surfaces have 34 boundary curves. This feature-based segmentation enables the independent manipulation of different geometrical regions of human face. The NURBS curve face model and NURBS surface face model. These two models are built up based on cubic NURBS reverse computation. The elements of the curve model and surface model can be manipulated to change the appearances of the models by their parameters which are obtained by NURBS reverse computation. A new 3D human face modelling method has been proposed and implemented based on bi-cubic NURBS through analysing the characteristic features and boundary conditions of NURBS techniques. This model can be manipulated through control points on the NURBS facial features to build any specific face models for any kind of appearances and to simulate dynamic facial expressions for various applications such as emotional bio-robots, aesthetic surgery, films and games, and crime investigation and prevention, etc

Facial Modelling and animation trends in the new millennium : a survey

M.Sc (Computer Science)Facial modelling and animation is considered one of the most challenging areas in the animation world. Since Parke and Waters’s (1996) comprehensive book, no major work encompassing the entire field of facial animation has been published. This thesis covers Parke and Waters’s work, while also providing a survey of the developments in the field since 1996. The thesis describes, analyses, and compares (where applicable) the existing techniques and practices used to produce the facial animation. Where applicable, the related techniques are grouped in the same chapter and described in a chronological fashion, outlining their differences, as well as their advantages and disadvantages. The thesis is concluded by exploratory work towards a talking head for Northern Sotho. Facial animation and lip synchronisation of a fragment of Northern Sotho is done by using software tools primarily designed for English.Computin

4D (3D Dynamic) statistical models of conversational expressions and the synthesis of highly-realistic 4D facial expression sequences

In this thesis, a novel approach for modelling 4D (3D Dynamic) conversational interactions and synthesising highly-realistic expression sequences is described. To achieve these goals, a fully-automatic, fast, and robust pre-processing pipeline was developed, along with an approach for tracking and inter-subject registering 3D sequences (4D data). A method for modelling and representing sequences as single entities is also introduced. These sequences can be manipulated and used for synthesising new expression sequences. Classification experiments and perceptual studies were performed to validate the methods and models developed in this work. To achieve the goals described above, a 4D database of natural, synced, dyadic conversations was captured. This database is the first of its kind in the world. Another contribution of this thesis is the development of a novel method for modelling conversational interactions. Our approach takes into account the time-sequential nature of the interactions, and encompasses the characteristics of each expression in an interaction, as well as information about the interaction itself. Classification experiments were performed to evaluate the quality of our tracking, inter-subject registration, and modelling methods. To evaluate our ability to model, manipulate, and synthesise new expression sequences, we conducted perceptual experiments. For these perceptual studies, we manipulated modelled sequences by modifying their amplitudes, and had human observers evaluate the level of expression realism and image quality. To evaluate our coupled modelling approach for conversational facial expression interactions, we performed a classification experiment that differentiated predicted frontchannel and backchannel sequences, using the original sequences in the training set. We also used the predicted backchannel sequences in a perceptual study in which human observers rated the level of similarity of the predicted and original sequences. The results of these experiments help support our methods and our claim of our ability to produce 4D, highly-realistic expression sequences that compete with state-of-the-art methods

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

3D face structure extraction from images at arbitrary poses and under arbitrary illumination conditions

With the advent of 9/11, face detection and recognition is becoming an important tool to be used for securing homeland safety against potential terrorist attacks by tracking and identifying suspects who might be trying to indulge in such activities. It is also a technology that has proven its usefulness for law enforcement agencies by helping identifying or narrowing down a possible suspect from surveillance tape on the crime scene, or quickly by finding a suspect based on description from witnesses.In this thesis we introduce several improvements to morphable model based algorithms and make use of the 3D face structures extracted from multiple images to conduct illumination analysis and face recognition experiments. We present an enhanced Active Appearance Model (AAM), which possesses several sub-models that are independently updated to introduce more model flexibility to achieve better feature localization. Most appearance based models suffer from the unpredictability of facial background, which might result in a bad boundary extraction. To overcome this problem we propose a local projection models that accurately locates face boundary landmarks. We also introduce a novel and unbiased cost function that casts the face alignment as an optimization problem, where shape constraints obtained from direct motion estimation are incorporated to achieve a much higher convergence rate and more accurate alignment. Viewing angles are roughly categorized to four different poses, and the customized view-based AAMs align face images in different specific pose categories. We also attempt at obtaining individual 3D face structures by morphing a 3D generic face model to fit the individual faces. Face contour is dynamically generated so that the morphed face looks realistic. To overcome the correspondence problem between facial feature points on the generic and the individual face, we use an approach based on distance maps. With the extracted 3D face structure we study the illumination effects on the appearance based on the spherical harmonic illumination analysis. By normalizing the illumination conditions on different facial images, we extract a global illumination-invariant texture map, which jointly with the extracted 3D face structure in the form of cubic morphing parameters completely encode an individual face, and allow for the generation of images at arbitrary pose and under arbitrary illumination.Face recognition is conducted based on the face shape matching error, texture error and illumination-normalized texture error. Experiments show that a higher face recognition rate is achieved by compensating for illumination effects. Furthermore, it is observed that the fusion of shape and texture information result in a better performance than using either shape or texture information individually.Ph.D., Electrical Engineering -- Drexel University, 200

Measuring long-term memories at the feature level reveals mechanisms of interference resolution

When memories share similar features, this can lead to interference, and ultimately forgetting. At the same time, many highly similar memories are remembered vividly for years to come. Understanding what causes interference and how it is overcome is key to understanding the vast human memory capacity. One unresolved challenge is that interference has primarily been studied with dichotomous measures of memory (“remembered”, “forgotten”). This limits our understanding because memories are not all-or-none, they are comprised of multiple features, each of which can be recalled with different levels of detail or bias. In order to investigate this issue, this dissertation focuses on the use of face stimuli. Faces are a unique class of stimuli for studying memory interference in that they are readily parameterizable and humans are experts at perceiving them. This means that they can be manipulated to be similar enough to cause interference, but subtle differences can also be stored and later probed from long-term memory. This dissertation develops a methodology to create synthetic faces that can be manipulated and probed along a set of perceptually-important feature dimensions. This development process included documenting face landmark positions, sorting faces based on perceived similarity, and collecting subjective ratings on a corpus of 1,148 face images. In a series of three experiments, I then applied this novel methodology to understand how memories change at the feature level when there is interference between highly similar memories. I found two memory changes that specifically occurred when there was interference between highly similar stimuli: (1) during recollection there was a bias to exaggerate the subtle differences and (2) distinguishing features were recalled with greater consistency. Critically, these memory changes were adaptive in that they were associated with less interference-related errors. Finally, in a separate fMRI experiment, I used the same corpus of faces and feature dimensions to reconstruct faces based on patterns of fMRI activity evoked while viewing them. I argue that this approach can be utilized in the future to measure neural representational changes during interference resolution. Together our findings provide important insights into how the memory system resolves interference between highly similar memories