28,394 research outputs found
Analyzing Visual Mappings of Traditional and Alternative Music Notation
In this paper, we postulate that combining the domains of information
visualization and music studies paves the ground for a more structured analysis
of the design space of music notation, enabling the creation of alternative
music notations that are tailored to different users and their tasks. Hence, we
discuss the instantiation of a design and visualization pipeline for music
notation that follows a structured approach, based on the fundamental concepts
of information and data visualization. This enables practitioners and
researchers of digital humanities and information visualization, alike, to
conceptualize, create, and analyze novel music notation methods. Based on the
analysis of relevant stakeholders and their usage of music notation as a mean
of communication, we identify a set of relevant features typically encoded in
different annotations and encodings, as used by interpreters, performers, and
readers of music. We analyze the visual mappings of musical dimensions for
varying notation methods to highlight gaps and frequent usages of encodings,
visual channels, and Gestalt laws. This detailed analysis leads us to the
conclusion that such an under-researched area in information visualization
holds the potential for fundamental research. This paper discusses possible
research opportunities, open challenges, and arguments that can be pursued in
the process of analyzing, improving, or rethinking existing music notation
systems and techniques.Comment: 5 pages including references, 3rd Workshop on Visualization for the
Digital Humanities, Vis4DH, IEEE Vis 201
The implications of embodiment for behavior and cognition: animal and robotic case studies
In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of
Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5
Visualising Discourse Coherence in Non-Linear Documents
To produce coherent linear documents, Natural Language Generation systems have traditionally exploited the structuring role of textual discourse markers such as relational and referential phrases. These coherence markers of the traditional notion of text, however, do not work in non-linear documents: a new set of graphical devices is needed together with formation rules to govern their usage, supported by sound theoretical frameworks. If in linear documents graphical devices such as layout and formatting complement textual devices in the expression of discourse coherence, in non-linear documents they play a more important role. In this paper, we present our theoretical and empirical work in progress, which explores new possibilities for expressing coherence in the generation of hypertext documents
Context-aware Synthesis for Video Frame Interpolation
Video frame interpolation algorithms typically estimate optical flow or its
variations and then use it to guide the synthesis of an intermediate frame
between two consecutive original frames. To handle challenges like occlusion,
bidirectional flow between the two input frames is often estimated and used to
warp and blend the input frames. However, how to effectively blend the two
warped frames still remains a challenging problem. This paper presents a
context-aware synthesis approach that warps not only the input frames but also
their pixel-wise contextual information and uses them to interpolate a
high-quality intermediate frame. Specifically, we first use a pre-trained
neural network to extract per-pixel contextual information for input frames. We
then employ a state-of-the-art optical flow algorithm to estimate bidirectional
flow between them and pre-warp both input frames and their context maps.
Finally, unlike common approaches that blend the pre-warped frames, our method
feeds them and their context maps to a video frame synthesis neural network to
produce the interpolated frame in a context-aware fashion. Our neural network
is fully convolutional and is trained end to end. Our experiments show that our
method can handle challenging scenarios such as occlusion and large motion and
outperforms representative state-of-the-art approaches.Comment: CVPR 2018, http://graphics.cs.pdx.edu/project/ctxsy
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