SenseStage is a research-creation project to develop a wireless sensor network infrastructure for live performance and interactive, real-time environments. The project is motivated by the economic and technical constraints of live performance contexts and the lack of existing tools for artistic work with wireless sensing platforms. The development is situated within professional artistic contex...

We study the use of the generative systems known as computational ecosystems to convey artistic and narrative aims. These are virtual worlds running on computers, composed of agents that trade units of energy and emulate cycles of life and behaviors adapted from biological life forms. In this article we propose a conceptual framework in order to understand these systems, which are involved in p...

Artists pictures rarely have photo-realistic detail. Tools to create pictures from digital photographs might, therefore, include methods for removing detail. These tools such as Gaussian and anisotropic diffusion filters and connected-set morphological filters (sieves) remove detail whilst maintaining scale-space causality, in other words new detail is not created using these operators. Non-pho...

Participation in extracurricular activities has surfaced as an important context for adolescent development and may be a fruitful avenue for fostering future attitudes, a concept shown to predict adolescent behaviors. In this study of youth from Trinidad and Tobago, we examined gender differences in activity participation and perceived life chances in 1,385 adolescents (59% male). Trinidadian a...

We present a novel approach to design artistic and believable trees in a cartoon-like style, which can be rendered by an animated camera to produce a convincing 3D-like experience. While computer assisted traditional animation is able to generate the desired effects, this approach still depends too much on the creation of many hand drawn images. Existing approaches fully depending on 3D geometr...

The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture th...

Many non-photorealistic rendering techniques exist to produce artistic effects from given images. Inspired by various artists, interesting effects can be produced by using a minimal rendering, where the minimum refers to the number of tones as well as the number and complexity of the primitives used for rendering. Our method is based on various computer vision techniques, and uses a combination...

Evolutionary search has been extensively used to generate artistic images. Raw images have high dimensionality which makes a direct search for an image challenging. In previous work this problem has been addressed by using compact symbolic encodings or by constraining images with priors. Recent developments in deep learning have enabled a generation of compelling artistic images using generativ...

This paper describes an approach to automatically classify digital pictures of paintings by artistic genre. While the task of artistic classification is often entrusted to human experts, recent advances in machine learning and multimedia feature extraction has made this task easier to automate. Automatic classification is useful for organizing large digital collections, for automatic artistic r...

Score following research is one of the active disciplines of sound and music computing since almost 30 years that have haunted both algorithmic and computational development in realtime music information retrieval, as well as artistic applications in interactive computer music. This paper explores the creative use of such technologies and brings attention to new scientific paradigms that emerge...