3D Printing and Camera Mapping -- Artwork: Digital Buddha
Art Exhibition
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Luo, He-Lin
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Chen, I-Chun
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Hung, Yi-Ping
Extended Abstracts of the ACM CHI'16 Conference on Human Factors in
Computing Systems
2016-05-07
v.2
p.3867-3870
© Copyright 2016 ACM
Summary: The new media artwork Digital Buddha combines 3D Printing and camera
mapping. The creative plan for Digital Buddha applies industrial design
concepts and methods that utilize multiple complex digital tools in order to
achieve effects of precision sculpting computations. In this work, a
pre-constructed abstract sculpture is decoded and transformed into a figurative
statue of Buddha when it is drawn by a motor into the image. The work takes
concepts of "coding" and "decoding", allowing a sculpture in reality to apply a
self-defined coding method to create an abstract sculpture in which certain
messages have been hidden in reality. Through computations and decoding by the
camera and by computer programs, the Buddha is restored to its figurative form
in the virtual world. this piece of work pays homage to the work TV Buddha
produced by video art master Nam June Paik.
3D Printing and Camera Mapping: Dialectic of Virtual and Reality
Art Exhibit
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Luo, He-Lin
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Chen, I-Chun
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Hung, Yi-Ping
Proceedings of the 2015 ACM International Conference on Multimedia
2015-10-26
p.721-722
© Copyright 2015 ACM
Summary: Projection Mapping, the superimposing of virtual images upon actual objects,
is already extensively used in performance arts. Applications of it are already
quite mature, therefore, here we wish to achieve the opposite, or specifically
speaking, the superimposing of actual objects into virtual images. This method
of reverse superimposition is called "camera mapping." Through cameras, camera
mapping captures actual objects, and introduces them into a virtual world. Then
using superimposition, this allows for actual objects to be rendered as virtual
objects. However, the actual objects here must have refined shapes so that they
may be superimposed back into the camera. Through the proliferation of 3D
printing, virtual 3D models in computers can be created in reality, thereby
providing a framework for the limits and demands of "camera mapping." The new
media artwork Digital Buddha combines 3D Printing and camera mapping. This work
was created by 3-D deformable modeling through a computer, then transforming
the model into a sculpture using 3D printing, and then remapping the materially
produced sculpture back into the camera. Finally, it uses the already known
algorithm to convert the model back into that of the original non-deformed
sculpture. From this creation project, in the real world, audiences will see a
deformed, abstract sculpture; and in the virtual world, through camera mapping,
they will see a concrete sculpture (Buddha). In its representation, this piece
of work pays homage to the work TV Buddha produced by video art master Nam June
Paik. Using the influence television possesses over people, this work extends
into the most important concepts of the digital era, "coding" and "decoding,"
simultaneously addressing the shock and insecurity people in the digital era
feel toward images.
Sentiment Extraction by Leveraging Aspect-Opinion Association Structure
Session 2C: Text Analysis
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Zhao, Li
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Huang, Minlie
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Sun, Jiashen
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Luo, Hengliang
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Yang, Xiankai
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Zhu, Xiaoyan
Proceedings of the 2015 ACM Conference on Information and Knowledge
Management
2015-10-19
p.343-352
© Copyright 2015 ACM
Summary: Sentiment extraction aims to extract and group the task of extracting and
grouping aspect and opinion words from online reviews. Previous works usually
extract aspect and opinion words by leveraging association between a single
pair of aspect and opinion word[5] [14] [9] [4][11], but the structure of
aspect and opinion word clusters has not been fully exploited.
In this paper, we investigate the aspect-opinion association structure, and
propose a "first clustering, then extracting" unsupervised model to leverage
properties of the structure for sentiment extraction. For the clustering
purpose, we formalise a novel concept syntactic distribution consistency as
soft constraint in the framework of posterior regularization; for the
extraction purpose, we extract aspect and opinion words based on
cluster-cluster association. In comparison to traditional word-word
association, we show that cluster-cluster association is a much stronger signal
to distinguish aspect (opinion) words from non-aspect (non-opinion) words.
Extensive experiments demonstrate the effectiveness of the proposed approach
and the advantages against state-of-the-art baselines.
Hypergraph partitioning for document clustering: a unified clique
perspective
Posters group 5: structured IR, ranking, classification and filtering
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Hu, Tianming
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Xiong, Hui
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Zhou, Wenjun
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Sung, Sam Yuan
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Luo, Hangzai
Proceedings of the 31st Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2008-07-20
p.871-872
© Copyright 2008 ACM
Summary: Hypergraph partitioning has been considered as a promising method to address
the challenges of high dimensionality in document clustering. With documents
modeled as vertices and the relationship among documents captured by the
hyperedges, the goal of graph partitioning is to minimize the edge cut.
Therefore, the definition of hyperedges is vital to the clustering performance.
While several definitions of hyperedges have been proposed, a systematic
understanding of desired characteristics of hyperedges is still missing. To
that end, in this paper, we first provide a unified clique perspective of the
definition of hyperedges, which serves as a guide to define hyperedges. With
this perspective, based on the concepts of hypercliques and shared (reverse)
nearest neighbors, we propose three new types of clique hyperedges and analyze
their properties regarding purity and size issues. Finally, we present an
extensive evaluation using real-world document datasets. The experimental
results show that, with shared (reverse) nearest neighbor based hyperedges, the
clustering performance can be improved significantly in terms of various
external validation measures without the need for fine tuning of parameters.
Prototyping and Evaluation for Smart Home Controller Based on Chinese
Families Behavior Analysis
The Elderly
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You, Fang
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Luo, Huimin
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Liang, Yinglei
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Wang, Jianmin
Proceedings of the 2008 Asia Pacific Conference on Computer Human
Interaction
2008-07-06
p.437-445
© Copyright 2008 Springer-Verlag
Summary: The fast development of Chinese architecture market and the smart home
business have caused works on user research in smart home environment to be
growing rapidly. Nonetheless, there are some problems related to product in
use; and in this paper, we examine two problems that are present in smart home
controllers from a user survey: one is the difficult in use for the Elderly;
and the other is the complex operations for Nannies. Based on this we have
examined and devised two prototypes designed for those two types of users; as
well as the task sheet evaluation.
Why web 2.0 is good for learning and for research: principles and prototypes
Social networks: applications and infrastructures
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Ullrich, Carsten
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Borau, Kerstin
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Luo, Heng
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Tan, Xiaohong
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Shen, Liping
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Shen, Ruimin
Proceedings of the 2008 International Conference on the World Wide Web
2008-04-21
p.705-714
Keywords: education, research, web 2.0
© Copyright 2008 International World Wide Web Conference Committee (IW3C2)
Summary: The term "Web 2.0" is used to describe applications that distinguish
themselves from previous generations of software by a number of principles.
Existing work shows that Web 2.0 applications can be successfully exploited for
technology-enhance learning. However, in-depth analyses of the relationship
between Web 2.0 technology on the one hand and teaching and learning on the
other hand are still rare. In this article, we will analyze the technological
principles of the Web 2.0 and describe their pedagogical implications on
learning. We will furthermore show that Web 2.0 is not only well suited for
learning but also for research on learning: the wealth of services that is
available and their openness regarding API and data allow to assemble
prototypes of technology-supported learning applications in amazingly small
amount of time. These prototypes can be used to evaluate research hypotheses
quickly. We will present two example prototypes and discuss the lessons we
learned from building and using these prototypes.
Hierarchical classification for automatic image annotation
Image retrieval
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Fan, Jianping
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Gao, Yuli
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Luo, Hangzai
Proceedings of the 30th Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2007-07-23
p.111-118
© Copyright 2007 ACM
Summary: In this paper, a hierarchical classification framework has been proposed for
bridging the semantic gap effectively and achieving multi-level image
annotation automatically. First, the semantic gap between the low-level
computable visual features and users' real information needs is partitioned
into four smaller gaps, and multiple approaches all are proposed to bridge
these smaller gaps more effectively. To learn more reliable contextual
relationships between the atomic image concepts and the co-appearances of
salient objects, a multi-modal boosting algorithm is proposed. To enable
hierarchical image classification and avoid inter-level error transmission, a
hierarchical boosting algorithm is proposed by incorporating concept ontology
and multi-task learning to achieve hierarchical image classifier training with
automatic error recovery. To bridge the gap between the computable image
concepts and the users' real information needs, a novel hyperbolic
visualization framework is seamlessly incorporated to enable intuitive query
specification and evaluation by acquainting the users with a good global view
of large-scale image collections. Our experiments on large-scale image
databases have also obtained very positive results.
Automatic image annotation by using concept-sensitive salient objects for
image content representation
Image retrieval, users and usability
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Fan, Jianping
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Gao, Yuli
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Luo, Hangzai
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Xu, Guangyou
Proceedings of the 27th Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2004-07-25
p.361-368
© Copyright 2004 ACM
Summary: Multi-level annotation of images is a promising solution to enable more
effective semantic image retrieval by using various keywords at different
semantic levels. In this paper, we propose a multi-level approach to annotate
the semantics of natural scenes by using both the dominant image components and
the relevant semantic concepts. In contrast to the well-known image-based and
region-based approaches, we use the salient objects as the dominant image
components to achieve automatic image annotation at the content level. By using
the salient objects for image content representation, a novel image
classification technique is developed to achieve automatic image annotation at
the concept level. To detect the salient objects automatically, a set of
detection functions are learned from the labeled image regions by using Support
Vector Machine (SVM) classifiers with an automatic scheme for searching the
optimal model parameters. To generate the semantic concepts, finite mixture
models are used to approximate the class distributions of the relevant salient
objects. An adaptive EM algorithm has been proposed to determine the optimal
model structure and model parameters simultaneously. We have also demonstrated
that our algorithms are very effective to enable multi-level annotation of
natural scenes in a large-scale dataset.
Semantic video classification by integrating unlabeled samples for
classifier training
Posters
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Fan, Jianping
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Luo, Hangzai
Proceedings of the 27th Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2004-07-25
p.592-593
© Copyright 2004 ACM
Summary: Semantic video classification has become an active research topic to enable
more effective video retrieval and knowledge discovery from large-scale video
databases. However, most existing techniques for classifier training require a
large number of hand-labeled samples to learn correctly. To address this
problem, we have proposed a semi-supervised framework to achieve incremental
classifier training by integrating a limited number of labeled samples with a
large number of unlabeled samples. Specifically, this emi-supervised framework
includes: (a) Modeling the semantic video concepts by using the finite mixture
models to approximate the class distributions of the relevant salient objects;
(b) Developing an adaptive EM algorithm to integrate the unlabeled samples to
achieve parameter estimation and model selection simultaneously; The
experimental results in a certain domain of medical videos are also provided.
Semantic video classification and feature subset selection under context and
concept uncertainty
Image and video digital libraries
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Fan, Jianping
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Luo, Hangzai
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Xiao, Jing
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Wu, Lide
JCDL'04: Proceedings of the 4th ACM/IEEE-CS Joint Conference on Digital
Libraries
2004-06-07
p.192-201
© Copyright 2004 ACM
Summary: As large collections of videos become one key component of digital
libraries, there is an urgent need of semantic video classification and feature
subset selection to enable more effective video database organization and
retrieval. However, most existing techniques for classifier training require a
large number of labeled samples to learn correctly and suffer from the problems
of context and concept uncertainty when only a limited number of labeled
samples are available. To address the problems of context and concept
uncertainty, we have proposed a novel framework to achieve incremental
classifier training by integrating a limited number of labeled samples with a
large number of unlabeled samples. Specifically, the contributions of this
paper include: (a) Using the salient objects to achieve a middle-level
understanding of video contents and enhance the quality of features on
discriminating among different semantic video concepts; (b) Modeling the
semantic video concepts by using the finite mixture models to approximate the
class distributions of the relevant salient objects; (c) Developing an adaptive
EM algorithm to integrate the unlabeled samples to enable incremental
classifier training and address the problem of context uncertainty; (d)
Proposing a cost-sensitive video classification technique to address the
problem of concept uncertainty over time; (e) Supporting automatic video
annotation via semantic classification Our experimental results in a certain
domain of medical education videos have also been provided a convincing proof
of our conclusions.
