Model of Bayesian tangent eye shape for eye capture

Iris recognition system captures an image of an individual's eye. In addition, the process of segmentation, normalization and feature extraction is followed by the iris of an eye image in the system. Using the algorithms proposed by J. Daugman, Iris recognition system has significantly improved over the last decade, and it has been used in so many practical applications. However, some difficulties related to Iris position and movement are still to be improved. To overcome these difficulties one can enhance the image acquisition process. Obtaining a method in extracting quality of eye images automatically from the video stream is the main area of interest in this study. Besides, a Bayesian inference solution called Bayesian Tangent Eye Shape Model (BTESM) was suggested depending on estimation of tangent shape. During image acquisition, constraints on the position and motion of the subjects can be decreased owing to this approach. Owing to maximum a posteriori estimation, we can identify similarity transform coefficients as well as the eye shape parameters in BTESM. To apply the maximum a posteriori procedure, tangent Eye shape vector was considered the state of the model which is hidden and expectation maximization depending on searching algorithm was adopted. Hence, after being tested and matched to future studies, the acquisitioned eye image has been proved to be adequate for Iris recognition system

Learning as a Nonlinear Line of Attraction for Pattern Association, Classification and Recognition

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns. It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter this picture. That is, if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented by an unknown encoded representation of a visual image. The conception of the dynamics of the nonlinear line attractor network to operate between stable and unstable states is the second contribution in this dissertation research. These criteria can be used to circumvent the plasticity-stability dilemma by using the unstable state as an indicator to create a new line for an unfamiliar pattern. This novel learning strategy utilizes stability (convergence) and instability (divergence) criteria of the designed dynamics to induce self-organizing behavior. The self-organizing behavior of the nonlinear line attractor model can manifest complex dynamics in an unsupervised manner. The third contribution of this dissertation is the introduction of the concept of manifold of color perception. The fourth contribution of this dissertation is the development of a nonlinear dimensionality reduction technique by embedding a set of related observations into a low-dimensional space utilizing the result attained by the learned memory matrices of the nonlinear line attractor network. Development of a system for affective states computation is also presented in this dissertation. This system is capable of extracting the user\u27s mental state in real time using a low cost computer. It is successfully interfaced with an advanced learning environment for human-computer interaction

Peramorphosis, an evolutionary developmental mechanism in neotropical bat skull diversity

Background The neotropical leaf‐nosed bats (Chiroptera, Phyllostomidae) are an ecologically diverse group of mammals with distinctive morphological adaptations associated with specialized modes of feeding. The dramatic skull shape changes between related species result from changes in the craniofacial development process, which brings into focus the nature of the underlying evolutionary developmental processes. Results In this study, we use three‐dimensional geometric morphometrics to describe, quantify, and compare morphological modifications unfolding during evolution and development of phyllostomid bats. We examine how changes in development of the cranium may contribute to the evolution of the bat craniofacial skeleton. Comparisons of ontogenetic trajectories to evolutionary trajectories reveal two separate evolutionary developmental growth processes contributing to modifications in skull morphogenesis: acceleration and hypermorphosis. Conclusion These findings are consistent with a role for peramorphosis, a form of heterochrony, in the evolution of bat dietary specialists