Analysis and synthesis of iris images

Of all the physiological traits of the human body that help in personal identification, the iris is probably the most robust and accurate. Although numerous iris recognition algorithms have been proposed, the underlying processes that define the texture of irises have not been extensively studied. In this thesis, multiple pair-wise pixel interactions have been used to describe the textural content of the iris image thereby resulting in a Markov Random Field (MRF) model for the iris image. This information is expected to be useful for the development of user-specific models for iris images, i.e. the matcher could be tuned to accommodate the characteristics of each user\u27s iris image in order to improve matching performance. We also use MRF modeling to construct synthetic irises based on iris primitive extracted from real iris images. The synthesis procedure is deterministic and avoids the sampling of a probability distribution making it computationally simple. We demonstrate that iris textures in general are significantly different from other irregular textural patterns. Clustering experiments indicate that the synthetic irises generated using the proposed technique are similar in textural content to real iris images

Going beyond semantic image segmentation, towards holistic scene understanding, with associative hierarchical random fields

In this thesis we exploit the generality and expressive power of the Associative Hierarchical Random Field (AHRF) graphical model to take its use beyond that of semantic image segmentation, into object-classes, towards a framework for holistic scene understanding. We provide a working definition for the holistic approach to scene understanding, which allows for the integration of existing, disparate, applications into an unifying ensemble. We believe that modelling such an ensemble as an AHRF is both a principled and pragmatic solution. We present a hierarchy that shows several methods for fusing applications together with the AHRF graphical model. Each of the three; feature, potential and energy, layers subsumes its predecessor in generality and together give rise to many options for integration. With applications on street scenes we demonstrate an implementation of each layer. The first layer application joins appearance and geometric features. For our second layer we implement a things and stuff co-junction using higher order AHRF potentials for object detectors, with the goal of answering the classic questions: What? Where? and How many? A holistic approach to recognition-and-reconstruction is realised within our third layer by linking two energy based formulations of both applications. Each application is evaluated qualitatively and quantitatively. In all cases our holistic approach shows improvement over baseline methods

Markov-Gibbs Random Field Approach for Modeling of Skin Surface Textures

Medical imaging has been contributing to dermatology by providing computer-based assistance by 2D digital imaging of skin and processing of images. Skin imaging can be more effective by inclusion of 3D skin features. Furthermore, clinical examination of skin consists of both visual and tactile inspection. The tactile sensation is related to 3D surface profiles and mechanical parameters. The 3D imaging of skin can also be integrated with haptic technology for computer-based tactile inspection. The research objective of this work is to model 3D surface textures of skin. A 3D image acquisition set up capturing skin surface textures at high resolution (~0.1 mm) has been used. An algorithm to extract 2D grayscale texture (height map) from 3D texture has been presented. The extracted 2D textures are then modeled using Markov-Gibbs random field (MGRF) modeling technique. The modeling results for MGRF model depend on input texture characteristics. The homogeneous, spatially invariant texture patterns are modeled successfully. From the observation of skin samples, we classify three key features of3D skin profiles i.e. curvature of underlying limb, wrinkles/line like features and fine textures. The skin samples are distributed in three input sets to see the MGRF model's response to each of these 3D features. First set consists of all three features. Second set is obtained after elimination of curvature and contains both wrinkle/line like features and fine textures. Third set is also obtained after elimination of curvature but consists of fine textures only. MGRF modeling for set I did not result in any visual similarity. Hence the curvature of underlying limbs cannot be modeled successfully and makes an inhomogeneous feature. For set 2 the wrinkle/line like features can be modeled with low/medium visual similarity depending on the spatial invariance. The results for set 3 show that fine textures of skin are almost always modeled successfully with medium/high visual similarity and make a homogeneous feature. We conclude that the MGRF model is able to model fine textures of skin successfully which are on scale of~ 0.1 mm. The surface profiles on this resolution can provide haptic sensation of roughness and friction. Therefore fine textures can be an important clue to different skin conditions perceived through tactile inspection via a haptic device

Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina

International audienceThis chapter focuses on methods from statistical physics and probability theory allowing the analysis of spike trains in neural networks. Taking as an example the retina we present recent works attempting to understand how retina ganglion cells encode the information transmitted to the visual cortex via the optical nerve, by analyzing their spike train statistics. We compare the maximal entropy models used in the literature of retina spike train analysis to rigorous results establishing the exact form of spike train statistics in conductance-based Integrate-and-Fire neural networks

Sidestepping Intractable Inference with Structured Ensemble Cascades

For many structured prediction problems, complex models often require adopting approximate inference techniques such as variational methods or sampling, which generally provide no satisfactory accuracy guarantees. In this work, we propose sidestepping intractable inference altogether by learning ensembles of tractable sub-models as part of a structured prediction cascade. We focus in particular on problems with high-treewidth and large state-spaces, which occur in many computer vision tasks. Unlike other variational methods, our ensembles do not enforce agreement between sub-models, but filter the space of possible outputs by simply adding and thresholding the max-marginals of each constituent model. Our framework jointly estimates parameters for all models in the ensemble for each level of the cascade by minimizing a novel, convex loss function, yet requires only a linear increase in computation over learning or inference in a single tractable sub-model. We provide a generalization bound on the filtering loss of the ensemble as a theoretical justification of our approach, and we evaluate our method on both synthetic data and the task of estimating articulated human pose from challenging videos. We find that our approach significantly outperforms loopy belief propagation on the synthetic data and a state-of-the-art model on the pose estimation/tracking problem

Learning from Complex Systems: On the Roles of Entropy and Fisher Information in Pairwise Isotropic Gaussian Markov Random Fields

Markov Random Field models are powerful tools for the study of complex systems. However, little is known about how the interactions between the elements of such systems are encoded, especially from an information-theoretic perspective. In this paper, our goal is to enlight the connection between Fisher information, Shannon entropy, information geometry and the behavior of complex systems modeled by isotropic pairwise Gaussian Markov random fields. We propose analytical expressions to compute local and global versions of these measures using Besag's pseudo-likelihood function, characterizing the system's behavior through its \emph{Fisher curve}, a parametric trajectory accross the information space that provides a geometric representation for the study of complex systems. Computational experiments show how the proposed tools can be useful in extrating relevant information from complex patterns. The obtained results quantify and support our main conclusion, which is: in terms of information, moving towards higher entropy states (A --> B) is different from moving towards lower entropy states (B --> A), since the \emph{Fisher curves} are not the same given a natural orientation (the direction of time).Comment: 46 pages, 16 Figure

Niche divergence and limits to expansion in the high polyploid Dianthus broteri complex

Niche evolution in plant polyploids remains controversial and evidence for alternative patterns has been reported. Using the autopolyploid Dianthus broteri complex (2×, 4×, 6× and 12×) as a model, we aimed to integrate three scenarios – competitive exclusion, recurrent origins of cytotypes and niche filling – into a single framework of polyploid niche evolution. We hypothesized that high polyploids would tend to evolve towards extreme niches when low ploidy cytotypes have nearly filled the niche space. We used several ecoinformatics and phylogenetic comparative analyses to quantify differences in the ecological niche of each cytotype and to evaluate alternative models of niche evolution. Each cytotype in this complex occupied a distinct ecological niche. The distributions were mainly constrained by soil characteristics, temperature and drought stress imposed by the Mediterranean climate. Tetraploids had the highest niche breadth and overlap due to their multiple origins, whereas the higher ploidy cytotypes were found in different, restricted, nonoverlapping niches. Niche evolution analyses suggested a scenario with one niche optimum for each ploidy, including the two independent tetraploid lineages.Our results suggest that the fate of nascent polyploids could not be predicted without accounting for phylogenetic relatedness, recurrent origins or the niche occupied by ancestors.Aridos La Melera S.L. (FIUS project 2234/0724