2015 international workshop on geometric modeling and...

2015 International Workshop on Geometric Modeling and Interactions

February 16, 2015, Hamamatsu, Japan Shizuoka University Campus, Mechanical Engineering Building #1-214

12:45- Conference Room Opens

13:00-13:10 Welcome address by Prof.Muira, General Chair of HC-2014

13:10-14:50 Invited Presentations

Session chair: Prof. Kenjiro Miura, Shizuoka University

13:10-14:00

2D and 3D Shape Matching using Evolutionary Algorithms

Ahmad Faisal Mohamad Ayob

14:00-14:50

Log-aesthetic Magnetic Curves and its application for CAD systems

R.U. Gobithaasan, Wo Mei Seen, and Kenjiro T. Miura

14:50-15:00 Coffe Break

15:00-16:20 Regular Presentations

Session chair: Prof. Kamen Kanev, Shizuoka University

15:00-15:40

Evidence-based Technology: Case Studies and Interactions

Patrick Hung

15:40-16:20

Ontology-based relevance measures for user-group matching

PaoloBotton and Amjad Hawash

16:20-16:30 Coffe Break

16:30-17:30 Panel Discussion

Panel chair: Prof. Shin Usuki, Shizuoka University

17:30-17:40 Closing Address by Prof.Kanev, PC Chair of HC-2014

18:30-21:30 Reception

2D and 3D Shape Matching using Evolutionary Algorithms


School of Ocean Engineering, Universiti Malaysia Terengganu, Malaysia

Organizers: K.Miura, K.Kanev

Biography:

Ahmad Faisal Mohamad Ayob is Senior Lecturer of School of Ocean Engineering at

the Universiti Malaysia Terengganu. He received a B. Eng. degree in Mechanical

Engineering from the Universiti Malaya and Ph.D. in Mechanical Engineering from

the University of New South Wales, Australia. He has been active in the area of

multidisciplinary design optimization since 2008 and has been a contributor to

several engineering-specific journals. His international experience includes

programs and consultancies in Australia, Malaysia and Indonesia. His current

research involves the study of the optimum design of marine vehicles that include

high speed craft, unmanned underwater vehicle and design robotics for ocean

application.

Abstract:

Shape representation plays a major role in any shape optimization exercise. The

ability to identify a shape with good performance is dependent on both the flexibility

of the shape representation scheme and the efficiency of the optimization algorithm.

In this article, the use of evolutionary algorithm is presented for 2D and 3D shape

matching problems. The shape is represented using B-splines, in which the control

points representing the shape are repaired and subsequently evolved within the

optimization framework. The efficiency of the proposed algorithm is illustrated

using three test problems, wherein the shapes were identified using a mere 5000

function evaluations. Extension of the approach to deal with problems of unknown

shape complexity is also presented.

2D and 3D Shape Matching

and Optimization using

Evolutionary Algorithm


School of Ocean Engineering

Universiti Malaysia Terengganu

1

1

Outline

• Introduction

• An Optimization Framework for Shape Matching

Problem

– Shape representaion

– Matching Metrics

• Proposed Methods

– Repair Strategy

– Matching Error

– Optimization Algorithms

• Results and Conclusions

2

2

Flowchart of

the

Optimization

Framework for

ShShape

Matching

3

Repair Strategy

4

Results Overview: 2D Case Study

5

Evolution of the generated shape to match the

target shape within the framework

Results Overview: 3D Case Study

6

Evolution of the generated shape to match the

target shape within the framework

Log-aesthetic Magnetic Curves and its application for CAD systems

R.U. Gobithaasan*, Wo Mei Seen, and Kenjiro T. Miura

*School of Informatics & Applied Mathematics,

University Malaysia Terengganu, Terengganu, Malaysia.


Biography:

R.U. Gobithaasan received his undergraduate degree in Applied Science (Computer

Modeling), M.Sc (Mathematics) and Ph.D (Computer-Aided Geometric Design) from

Universiti Sains Malaysia. In 2009, Gobithaasan returned to work as a Senior lecturer

teaching Numerical Analysis, Computer Programming Language, Geometric Modeling

and Advanced Numerical Analysis courses to undergraduates in University Malaysia

Terengganu (UMT). His teaching is research-led and compliments all aspects of his field

of expertise. He is currently supervising a number of doctorates, masters and

undergraduate students carrying out Computer-Aided Geometric Design (CAGD)

projects. He has delivered numerous talks, has written over 50 international

journal/research papers and enjoys answering curious questions pertaining to

CAGD/CAD and Geometric Modelling. .

Abstract:

Curves are the building blocks of shapes and designs in computer aided geometric

design (CAGD). It is important to ensure these curves are both visually and

geometrically aesthetic to meet the high aesthetic need for successful product

marketing. This research started with a question on analysing the aesthetic properties

of Magnetic curves (MC) which was derived with the idea of particle tracing method

using monotonic curvature profile. In 2009, magnetic curves were proposed for

computer graphics purposes that generates a wide variety of curves and spirals under

the influence of a magnetic field. This talk covers three parts; where the first part

reformulates magnetic curves in the form of log-aesthetic curve (LAC) denoting it as

log-aesthetic magnetic curves (LMC) and log-aesthetic magnetic space curves (LMSC),

the second part elucidates vital properties of LMCs, and the final part proposes G2 LMC

design for CAD applications. LMC holds great potential in matching a single segment

with G2 Hermite data is still a cumbersome task.

Evidence-based Technology: Case Studies and Interactions

Patrick Hung

University of Ontario Institute of Technology, Canada


Biography:

Patrick C. K. Hung is an Associate Professor at the Faculty of Business and

Information Technology in University of Ontario Institute of Technology. Patrick has

been worked with Boeing Research and Technology on aviation services-related

research with a patent on mobile network dynamic workflow system. He is an

Honorary International Chair Professor at National Taipei University of Technology

and an Adjunct Professor at Wuhan University. In addition, he is a Visiting

Researcher at the Shizuoka University and University of Aizu in Japan, a Guest

Professor in University of Innsbruck in Austria, University of Trento and University

of Milan in Italy. Before that, he was a Research Scientist with Commonwealth

Scientific and Industrial Research Organization in Australia. He is a founding

committee member of the IEEE International Conference of Web Services, IEEE

International Conference on Services Computing, and IEEE BigData Congress. He

is an associate editor of the IEEE Transactions on Services Computing.

Abstract:

Many enterprises need advanced educational technologies to enhance instruction

and aid training to high quality personnel. Evidence-based technology provides a

variety of contemporary solutions to identified training problems related to the

assessment of profession training. The evidence-based technology focuses on

empirical evidence and effectiveness to achieve specific training goals.

Evidence-based training is an individual level of evidence-based process to achieve

training goals from organization resources, process, evaluation and reflection, which

is supported by information technology. Evidence-based technology emphasizes the

critical appraisal of the trainees to the training content, which is beneficial for

cultivating trainees' critical thinking and problem solving skills. This talk gives an

overview of evidence-based technology with a couple of case studies.

Patrick C. K. HungAssociate Professor, Faculty of Business and IT

University of Ontario Institute of Technology, Ontario, Canada

We need to recognize 4 gestures automatically:

Archery Fleche

Case Study – Last Friday

Advance Retreat

2

Motion Analysis• Load a fencing video• Start fencing action recognition• Save to a CSV file

Case Study – Last Friday

3

A1

Time1 A1

A2

Time2

The Complexity of Assessment

4

A2

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A4

A3

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A5

A4

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Data Quality and Quantity

Data Analysis and Interpretation

Clinical Decision Support

Filming Evidence Maxit Systems

66

Ontology-based relevance measures for user-group matching

Paolo Bottoni, Amjad Hawash

Department of Computer Science, Universita Sapienza di Roma, Italy


Biography:

Paolo Bottoni has been active in the field of Visual Communication for over 20 years. In

this area, his research interests are focused on foundational techniques for developing

domain specific languages, through uniform formalisms for the definition of their

abstract or concrete syntax and semantics, and for supporting model-based

development of interactive systems. After joining Sapienza University of Rome in 1994,

where he currently teaches courses on Software Engineering, he received a PhD in

Computer Science in 1995 from the University of Turin, with a thesis on the application

of linear logic to visual simulation. He has participated in national and European

projects on visual languages, graph transformation, and Human-Computer Interaction.

He has published 200 papers in international journals, conferences and books. He sits in

the Steering Committees of VL/HCC, ICGT, and AVI, is a member of the Editorial

Board of the Journal of Visual Languages and Computing and of Human-centric

Computing and Information Sciences and has acted as invited expert on the W3C

working group on Model-Based User Interfaces.

Abstract:

The MADCOW annotation system has been recently enriched with support for group

annotations. Users can post annotations to groups focused on common interests. As the

number of groups and users increases, the problem arises of finding matches between

users and groups. Group owners deciding to invite users to their groups share similar, if

symmetrical, problems with users looking for relevant groups. Owners want to attract

users interested in the group topic, in order to promote collaboration, while users want

to share their thoughts with people who can provide interesting feedback. We propose

the use of ontologies as a way to characterize the domain of interest for a group and

measures of correspondence between the terms populating the ontology and those used

to tag annotations, as an indication of the degree of relevance of a group for a user’s

interests. We discuss two measures and present some initial result.

Ontology-based relevance measures for user-

group matching

Paolo Bottoni, Amjad Hawash

Summary

• The MADCOW annotation system

• Groups and domains

• Users-groups relevance

T l– Two relevance measures

– Experimental tests

Hamamatsu, 16 February

2015

Relevance measures 2

MADCOW Project: Architecture and services

• Multimedia Annotation

of Digital Content Over

the Web.


2015


(http://www.web-annotations.com)

• Annotations (public/private).

• Privacy-Collaboration Conflict

(solved: Groups).

• Groups Services.

• Domain-Ontology: each ontology has to be

associated with one domain prior to its

involvement in the matching process.

• Domain-Group: domain could be associated with

more than one group

•Ontological-Based Matching: Groups Association


2015


more than one group.

• An extensive test has been conducted.

• Average invitation duration is decreased from

99.25 to 10.6 seconds.

•Experimental Test


2015


• 6 different Ontologies:– Animals (899 concepts).

– Plants (709).

– Finance (2037).

– AI (2386).

•Experimental Test: Ontologies Repository


2015


– Vehicles (168).

– Viruses (296).

P. Velardi, S. Faralli, and R. Navigli, “OntoLearn reloaded: A

graph-based algorithm for taxonomy induction,”Computational

Linguistics , vol. 39(3), 2013.

2015 international workshop on geometric modeling and...

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