17,573 research outputs found
Evolved Art with Transparent, Overlapping, and Geometric Shapes
In this work, an evolutionary art project is presented where images are
approximated by transparent, overlapping and geometric shapes of different
types, e.g., polygons, circles, lines. Genotypes representing features and
order of the geometric shapes are evolved with a fitness function that has the
corresponding pixels of an input image as a target goal. A
genotype-to-phenotype mapping is therefore applied to render images, as the
chosen genetic representation is indirect, i.e., genotypes do not include
pixels but a combination of shapes with their properties. Different
combinations of shapes, quantity of shapes, mutation types and populations are
tested. The goal of the work herein is twofold: (1) to approximate images as
precisely as possible with evolved indirect encodings, (2) to produce visually
appealing results and novel artistic styles.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019),
Trondheim, Norwa
Interactive Extraction of High-Frequency Aesthetically-Coherent Colormaps
Color transfer functions (i.e. colormaps) exhibiting a high frequency luminosity component have proven to be useful in the visualization of data where feature detection or iso-contours recognition is essential. Having these colormaps also display a wide range of color and an aesthetically pleasing composition holds the potential to further aid image understanding and analysis. However producing such colormaps in an efficient manner with current colormap creation tools is difficult. We hereby demonstrate an interactive technique for extracting colormaps from artwork and pictures. We show how the rich and careful color design and dynamic luminance range of an existing image can be gracefully captured in a colormap and be utilized effectively in the exploration of complex datasets
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In
these media, dynamic and still elements are juxtaposed to create an artistic
and narrative experience. Creating a high-quality, aesthetically pleasing
cinemagraph requires isolating objects in a semantically meaningful way and
then selecting good start times and looping periods for those objects to
minimize visual artifacts (such a tearing). To achieve this, we present a new
technique that uses object recognition and semantic segmentation as part of an
optimization method to automatically create cinemagraphs from videos that are
both visually appealing and semantically meaningful. Given a scene with
multiple objects, there are many cinemagraphs one could create. Our method
evaluates these multiple candidates and presents the best one, as determined by
a model trained to predict human preferences in a collaborative way. We
demonstrate the effectiveness of our approach with multiple results and a user
study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary
materia
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