Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

The Impact of Mindfulness On Balance, Cognition and Arousal

The control group study investigated the impact of a mindfulness centering technique, taken from the Japanese martial art Shin Shin Toitsu Aikido, on balance and reaction time performance as well as on concurrent levels of galvanic skin response (arousal). Study design and analysis occurred within a social neuroscience framework that included the cultural view of mind, body, and emotion as an integrated whole, and brain research from multiple disciplines revealing the neural integrated organism. Thirty-one subjects were tested in a visual-stimulus reaction time task and in an unstable rocker-board balancing task. Prior to repeating the tests, experimental group participants learned the centering technique and control group participants received a brief lecture. Significant improvement for the experimental group over the control group was limited to one balance measure. Results in general indicated a possible trend to improved balance performance with centering. Arousal level correlated significantly with performance and task type for the entire sample. In light of ongoing neuroscience research, the study\u27s findings point to the value of approaching clinical studies of performance from an integrated organism perspective

The Multiple Roles of Anticipation in Developmental Robotics

Anticipatory systems have been shown to be useful in discrete, symbolic systems. However, non­symbolic anticipatory systems are less well understood. In this paper, we explore the use of anticipation within the framework of connectionist networks to bootstrap from an innate behavior; to drive a reinforcement signal; and to provide feedback on the learnability of a task

A distributional model of semantic context effects in lexical processinga

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›

A semidiscrete version of the Citti-Petitot-Sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition

In his beautiful book [66], Jean Petitot proposes a sub-Riemannian model for the primary visual cortex of mammals. This model is neurophysiologically justified. Further developments of this theory lead to efficient algorithms for image reconstruction, based upon the consideration of an associated hypoelliptic diffusion. The sub-Riemannian model of Petitot and Citti-Sarti (or certain of its improvements) is a left-invariant structure over the group $SE(2)$ of rototranslations of the plane. Here, we propose a semi-discrete version of this theory, leading to a left-invariant structure over the group $SE(2,N)$, restricting to a finite number of rotations. This apparently very simple group is in fact quite atypical: it is maximally almost periodic, which leads to much simpler harmonic analysis compared to $SE(2).$ Based upon this semi-discrete model, we improve on previous image-reconstruction algorithms and we develop a pattern-recognition theory that leads also to very efficient algorithms in practice.Comment: 123 pages, revised versio