Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.[EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digital image processing point of view, we have focused on twobasic problems: Color Quantization and filter design. Both problems have beenaddressed from the context of Vector Quantization performed by CompetitiveNeural Networks. Processing of non-stationary data is an interesting paradigmthat has not been explored with Competitive Neural Networks. We have statesthe problem of Non-stationary Clustering and related Adaptive Vector Quantizationin the context of image sequence processing, where we naturally havea Frame Based Adaptive Vector Quantization. This approach deals with theproblem as a sequence of stationary almost-independent Clustering problems.We have also developed some new computational algorithms for Vector Quantizationdesign.The works on supervised learning have been sparsely distributed in time anddirection. First we worked on the use of Self Organizing Map for the independentmodeling of skin and no-skin color distributions for color based face localization. Second, we have collaborated in the realization of a supervised learning systemfor tissue segmentation in Magnetic Resonance Imaging data. Third, we haveworked on the development, implementation and experimentation with HighOrder Boltzmann Machines, which are a very different learning architecture.Finally, we have been working on the application of Sparse Bayesian Learningto a new kind of classification systems based on Dendritic Computing. This lastresearch line is an open research track at the time of writing this Thesis

Systemic lupus erythematosus in African-American women: immune cognitive modules, autoimmune disease, and pathogenic social hierarchy

Examining elevated rates of systemic lupus erythematosus in African-American women from the perspective of the emerging theory of immune cognition suggests the disease constitutes an internalized physiological image of external patterns of psychosocial stress, a 'pathogenic social hierarchy' involving the synergism of racism and gender discrimination. The disorder represents the punctuated resetting of 'normal' immune self-image to a self-attacking 'excited' state, a process formally analogous to models of punctuated equilibrium in evolutionary theory. We speculate that this punctuated onset takes place in the context of an immunological 'cognitive module' similar to what has been proposed by evolutionary psychologists for the human mind. We discuss the broader implications of a high rate of this disorder within a marginalized population, finding it to be a leading indicator for phenomena likely to entrain powerful subgroups into a larger pattern of embedding patholog

Study and simulation of low rate video coding schemes

The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design

An efficient and straightforward online quantization method for a data stream through remove-birth updating

The growth of network-connected devices is creating an explosion of data, known as big data, and posing significant challenges to efficient data analysis. This data is generated continuously, creating a dynamic flow known as a data stream. The characteristics of a data stream may change dynamically, and this change is known as concept drift. Consequently, a method for handling data streams must efficiently reduce their volume while dynamically adapting to these changing characteristics. This paper proposes a simple online vector quantization method for concept drift. The proposed method identifies and replaces units with low win probability through remove-birth updating, thus achieving a rapid adaptation to concept drift. Furthermore, the results of this study show that the proposed method can generate minimal dead units even in the presence of concept drift. This study also suggests that some metrics calculated from the proposed method will be helpful for drift detection

Structured Psychosocial Stress and Therapeutic Intervention: Toward a Realistic Biological Medicine

Using generalized 'language of thought' arguments appropriate to interacting cognitive modules, we explore how disease states can interact with medical treatment, including, but not limited to, drug therapy. The feedback between treatment and response creates a kind of idiotypic 'hall of mirrors' generating a pattern of 'efficacy', 'treatment failure', and 'adverse reactions' which will, from a Rate Distortion perspective, embody a distorted image of externally-imposed structured psychosocial stress. This analysis, unlike current pharmacogenetics, does not either reify 'race' or blame the victim by using genetic structure to place the locus-of-control within a group or individual. Rather, it suggests that a comparatively simple series of questions to identify longitudinal and cross-sectional stressors may provide more effective guidance for specification of individual therapy than complicated genotyping strategies of dubious meaning. These latter are likely to be both very expensive and utterly blind to the impact of structured psychosocial stress -- a euphemism for various forms of racism and ethnic cleansing -- which, we contend, is often a principal determinant of treatment outcome at both individual and community levels of organization. We propose, to effectively address 'health disparities' between populations, and in contrast to current biomedical ideology based on a simplistic genetic determinism, a richer program of biological medicine reflecting Lewontin's 'triple helix' of genes, environment, and development, a program more in concert with the realities of a basic human biology marked by hypersociality unusual in vertibrates. Aggressive social, economic, and other policies of affirmative action to redress the persisting burdens of history would be an integral component of any such project

Cell Detection by Functional Inverse Diffusion and Non-negative Group Sparsity−-Part I: Modeling and Inverse Problems

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs' Green function to derive a novel parametrization of the acquired images. This parametrization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a non-negative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.Comment: published, 15 page