4 research outputs found
Computational Models for the Analysis and Synthesis of Graffiti Tag Strokes
In this paper we describe a system aimed at the generation and analysis of graffiti tags.We argue that the dynamics
of the movement involved in generating tags is in large part — and at a higher degree with respect to many other
visual art forms — determinant of their stylistic quality. To capture this notion computationally, we rely on a biophysically
plausible model of handwriting gestures (the Sigma Lognormal Model proposed by Réjean Plamondon
et al.) that permits the generation of curves which are aesthetically and kinetically similar to the ones made
by a human hand when writing. We build upon this model and extend it in order to facilitate the interactive
construction and manipulation of digital tags. We then describe a method that reconstructs any planar curve or a
sequence of planar points with a set of corresponding model parameters. By doing so, we seek to recover plausible
velocity and temporal information for a static trace. We present a number of applications of our system: (i) the
interactive design of curves that closely resemble the ones typically observed in graffiti art; (ii) the stylisation and
beautification of input point sequences via curves that evoke a smooth and rapidly executed movement; (iii) the
generation of multiple instances of a synthetic tag from a single example. This last application is a step in the
direction of our longer term plan of realising a system which is capable of automatically generating convincing
images in the graffiti style space
Computer Aided Design of Handwriting Trajectories with the Kinematic Theory of Rapid Human Movements
We present the Sigma Lognormal model as a potential tool for curve generation in computer graphics related applications. We discuss its extension and parameterisations for the interactive definition of handwriting, drawing and calligraphic trajectories. This results in an effcient trajectory synthesis method, that has a user interface similar to the ones commonly used with Bezier curves or splines, but with the added benefit of capturing the kinematics of human drawing or writing movements. Such kinematics produced by the model can then be exploited to generate realistic stroke animations or to facilitate expressive rendering methods
iDeLog: Iterative Dual Spatial and Kinematic Extraction of Sigma-Lognormal Parameters
The Kinematic Theory of rapid movements and its associated Sigma-Lognormal
model have been extensively used in a large variety of applications. While the
physical and biological meaning of the model have been widely tested and
validated for rapid movements, some shortcomings have been detected when it is
used with continuous long and complex movements. To alleviate such drawbacks,
and inspired by the motor equivalence theory and a conceivable visual feedback,
this paper proposes a novel framework to extract the Sigma-Lognormal
parameters, namely iDeLog. Specifically, iDeLog consists of two steps. The
first one, influenced by the motor equivalence model, separately derives an
initial action plan defined by a set of virtual points and angles from the
trajectory and a sequence of lognormals from the velocity. In the second step,
based on a hypothetical visual feedback compatible with an open-loop motor
control, the virtual target points of the action plan are iteratively moved to
improve the matching between the observed and reconstructed trajectory and
velocity. During experiments conducted with handwritten signatures, iDeLog
obtained promising results as compared to the previous development of the
Sigma-Lognormal.Comment: Accepted Version published by Transactions on Pattern Analysis and
Machine Intelligenc