627 research outputs found
Example Based Caricature Synthesis
The likeness of a caricature to the original face image is an essential and often overlooked part of caricature
production. In this paper we present an example based caricature synthesis technique, consisting of shape
exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set
of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial
features. The relationship exaggeration step introduces two definitions which facilitate global facial feature
synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an
intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion
form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance
(MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a
number of constraints. The effectiveness of our algorithm is demonstrated with experimental results
Affective Game Computing: A Survey
This paper surveys the current state of the art in affective computing
principles, methods and tools as applied to games. We review this emerging
field, namely affective game computing, through the lens of the four core
phases of the affective loop: game affect elicitation, game affect sensing,
game affect detection and game affect adaptation. In addition, we provide a
taxonomy of terms, methods and approaches used across the four phases of the
affective game loop and situate the field within this taxonomy. We continue
with a comprehensive review of available affect data collection methods with
regards to gaming interfaces, sensors, annotation protocols, and available
corpora. The paper concludes with a discussion on the current limitations of
affective game computing and our vision for the most promising future research
directions in the field
Ubiquitous Integration and Temporal Synchronisation (UbilTS) framework : a solution for building complex multimodal data capture and interactive systems
Contemporary Data Capture and Interactive Systems (DCIS) systems are tied in with various
technical complexities such as multimodal data types, diverse hardware and software
components, time synchronisation issues and distributed deployment configurations. Building
these systems is inherently difficult and requires addressing of these complexities before the
intended and purposeful functionalities can be attained. The technical issues are often
common and similar among diverse applications.
This thesis presents the Ubiquitous Integration and Temporal Synchronisation (UbiITS)
framework, a generic solution to address the technical complexities in building DCISs. The
proposed solution is an abstract software framework that can be extended and customised to
any application requirements. UbiITS includes all fundamental software components,
techniques, system level layer abstractions and reference architecture as a collection to enable
the systematic construction of complex DCISs.
This work details four case studies to showcase the versatility and extensibility of UbiITS
framework’s functionalities and demonstrate how it was employed to successfully solve a
range of technical requirements. In each case UbiITS operated as the core element of each
application. Additionally, these case studies are novel systems by themselves in each of their
domains. Longstanding technical issues such as flexibly integrating and interoperating
multimodal tools, precise time synchronisation, etc., were resolved in each application by
employing UbiITS. The framework enabled establishing a functional system infrastructure in
these cases, essentially opening up new lines of research in each discipline where these
research approaches would not have been possible without the infrastructure provided by the
framework. The thesis further presents a sample implementation of the framework on a
device firmware exhibiting its capability to be directly implemented on a hardware platform.
Summary metrics are also produced to establish the complexity, reusability, extendibility,
implementation and maintainability characteristics of the framework.Engineering and Physical Sciences Research Council (EPSRC) grants - EP/F02553X/1, 114433 and 11394
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Grounding truth via ordinal annotation
The question of how to best annotate affect within
available content has been a milestone challenge for affective
computing. Appropriate methods and tools addressing that question
can provide better estimations of the ground truth which, in
turn, may lead to more efficient affect detection and more reliable
models of affect. This paper introduces a rank-based real-time
annotation tool, we name AffectRank, and compares it against the
popular rating-based real-time FeelTrace tool through a proofof-
concept video annotation experiment. Results obtained suggest
that the rank-based (ordinal) annotation approach proposed
yields significantly higher inter-rater reliability and, thereby,
approximation of the underlying ground truth. The key findings
of the paper demonstrate that the current dominant practice
in continuous affect annotation via rating-based labeling is
detrimental to advancements in the field of affective computing.The authors would like to thank all annotators that participated
in the reported experiments. We would also like to
thank Gary Hili and Ryan Abela for providing access to the
Eryi dataset. The work is supported, in part, by the EU-funded
FP7 ICT iLearnRW project (project no: 318803).peer-reviewe
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