Thèse doctorat : Images et Systèmes : Villeurbanne, INSA : 2006. / Thèse rédigée en anglais. Titre provenant de l'écran-titre. Bibliogr. p. 161-172.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Yucel Altunbasak; Committee Member: Brani Vidakovic; Committee Member: Ghassan AlRegib; Committee Member: James Hamblen; Committee Member: Russ Mersereau. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Facial expression recognition has become an active research topic in recent years due to its applications in human computer interfaces and data-driven animation. In this thesis, we focus on the problem of how to e?ectively use domain, temporal and categorical information of facial expressions to help computer understand human emotions. Over the past decades, many techniques (such as neural networks, Gaussian processes, support vector machines, etc.) have been applied to facial expression analysis. Recently graphical models have emerged as a general framework for applying probabilistic models. They provide a natural framework for describing the generative process of facial expressions. However, these models often su?er from too many latent variables or too complex model structures, which makes learning and inference di±cult. In this thesis, we will try to analyze the deformation of facial expression by introducing some recently developed graphical models (e.g. latent topic model) or improving the recognition ability of some already widely used models (e.g. HMM). In this thesis, we develop three di?erent graphical models with di?erent representational assumptions: categories being represented by prototypes, sets of exemplars and topics in between. Our ¯rst model incorporates exemplar-based representation into graphical models. To further improve computational e±- ciency of the proposed model, we build it in a local linear subspace constructed by principal component analysis. The second model is an extension of the recently developed topic model by introducing temporal and categorical information into Latent Dirichlet Allocation model. In our discriminative temporal topic model (DTTM), temporal information is integrated by placing an asymmetric Dirichlet prior over document-topic distributions. The discriminative ability is improved by a supervised term weighting scheme. We describe the resulting DTTM in detail and show how it can be applied to facial expression recognition. Our third model is a nonparametric discriminative variation of HMM. HMM can be viewed as a prototype model, and transition parameters act as the prototype for one category. To increase the discrimination ability of HMM at both class level and state level, we introduce linear interpolation with maximum entropy (LIME) and member- ship coe±cients to HMM. Furthermore, we present a general formula for output probability estimation, which provides a way to develop new HMM. Experimental results show that the performance of some existing HMMs can be improved by integrating the proposed nonparametric kernel method and parameters adaption formula. In conclusion, this thesis develops three di?erent graphical models by (i) combining exemplar-based model with graphical models, (ii) introducing temporal and categorical information into Latent Dirichlet Allocation (LDA) topic model, and (iii) increasing the discrimination ability of HMM at both hidden state level and class level. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

I present theoretical foundations and perform computational studies on optimal search strategies in natural scenes performed by foveated artificial vision systems, based on novel characterizations of Natural Scene Statistics (NSS). I first develop relevant theoretical bounds on the processing of foveated--more generally LSV-filtered (Linear Scale Variant)--signals, which provide a rigorous basis to linear post-processing operations performed on foveated images. The major contribution of this dissertation, however, lies in the discovery and elucidation of two major statistical characterizations of natural scenes and their subsequent deployment for devising optimal fixation strategies. The first is a novel characterization of the contrast statistics of natural scenes, parameterized by the eccentricity at which each contrast level is measured across the LSV-filtered image. This formulation of contrast statistics finds natural application in devising fixation patterns that optimally extract contrast information from the image. I further demonstrate that the resulting fixation patterns are nearly optimal in the sense of minimizing the global MSE of the LSV-filtered image. The second is the characterization of the non-stationary structure of natural images via the development of the concept of non-stationarity indices that measure the extent of non-stationarity across the image. The theoretical motivation of our approach lies in a novel characterization of image patch statistics I developed, called Multilinear Independent Component Analysis (MICA), wherein the statistical interactions between the pseudo-independent components are captured via a multilinear expansion of the joint probability density being modeled. This modeling technique enables the derivation of a theoretical measure of non-stationary in natural scenes that subsequently motivates computationally efficient non-stationarity indices--a variant of which is then deployed to furnish optimal texture-based fixations natural images. The fixation patterns generated by our information-theoretic approaches are quantitatively shown to match very well with human fixation patterns and offer considerable explanatory and predictive power over previously well-known fixation strategies. These results point the way towards a unified information-theoretic understanding of low-level fixation processes; and further demonstrate the importance of incorporating low-level visual information into visual search strategies--thereby providing a foundation upon which high-level visual information relating to scene context and object structures can be incorporated.

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For 150 years, chemical photography had a privileged status as a truthful means of representation. The emerging technology of digital imaging is challenging this unique position. This paper proposes to examine the status of the photographic image in the digital age, as well as the debate surrounding the new technology and its implications. Chapter one begins with a brief technical history of the medium and establishes the construction behind the myth of photographic truth. Chapter two debunks the myth of photographic image's objectivity. Chapter three describes the specifities of digital imaging technology and discusses the potential problems and consequences of the invasion of digitally enhanced images in the media, as well as possible solutions. Finally, the fourth chapter considers the use of digital imaging in women's magazines and examines what such a use says about our society's values. By considering the issue of photo-manipulation, one can understand that manipulation expresses the human will to create a world of simulation.

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