3 research outputs found
AutoGraff: towards a computational understanding of graffiti writing and related art forms.
The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes
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Coherence in typeface design: visual similarity of characters in Cyrillic, Devanagari, and Latin
This thesis explores the visual similarity that underlies the coherence in the design of individual typefaces. Typeface designers aim to achieve a unifying coherence in their typefaces, so that characters can be identified individually as well as belonging together giving rise to an overall style. The objective is to determine whether the coherence perceived by readers differs from the coherence intended by designers. The research is cross-disciplinary, combining empirical studies of readers’ perceptions with a computational model that is based on relevant typeface design knowledge.
Character similarity is studied in multiple different typefaces (fonts) intended for continuous reading in Cyrillic, Devanagari, and Latin scripts. The studies were conducted online to collect a large number of responses. The participants were presented with a sequence of character triplets. They were asked to identify the odd one out in each of these triplets judging by their visual similarity, thus making a statement about the similarity of the two complementary characters. This method studies the similarity in context, which provides more refined details about participants’ similarity judgements.
The model interprets characters using two kinds of features: more specific parts and more general roles. The model learns the relative saliences of these features from a subset of the data collected in the studies. This allows the model to predict participants’ responses to the triplets from the studies and for other, unseen triplets. Additionally, the model can provide explanations of the criteria participants used in their similarity judgements and can generate similarity matrices.
The model achieved high scores when predicting response probabilities and identifying the overall odd ones out. A view of coherence that is supported by readers’ perception can be used to assist designers in their creative process, help with fonts’ quality assessments, and contribute to readability research and multi-script typography