The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

IDENTIFICATION OF TEMPORAL DYNAMICS IN BIOLOGICAL PROCESSES

The behavior and dynamics of complex systems are the focus of many research fields. The complexity of such systems comes not only from the number of their elements, but also from the unavoidable emergence of new properties of the system, which are not just a simple summation of the properties of its elements. The behavior of dynamic complex systems relates to a number of well developed models, the majority of which do not incorporate the modularity and the evolutionary dynamics of a system simultaneously. In this work, we deploy a Bayesian model that addresses this issue. Our model has been developed within the Random Finite Set Theory's framework. We introduced the stochastic evolution diagram as a novel mathematical tool to describe the evolutionary dynamics of complex modular systems. It has been shown how it could be used in real world applications. We have extended the idea of Bayesian network for non-stationary dynamic systems by defining a new concept "labeled-edge Bayesian network" and providing a Bayesian Dirichlet (BD) metric as its score function