2. Short Curriculum Vitae
• Personal Homepage
http://www.hci.iis.u-tokyo.ac.jp/~kylo/
• Period of past years
University (2006 – 2010)
Internship (2010 – 2011)
Research Assistant (2012 – 2013)
• Two main research topic:
1. 2D-to-3D conversion
2. Photo Aesthetics.
• Wrap up
3. Short Curriculum Vitae
• Language: English (TOEIC
935), JLPT(N1), Chinese(Native)
• Programming: C/C++, Matlab, Java(android)
Technique: SIFT/SURF/HOG, K-
means, GMM, kNN, SVM, PCA/LDA/ITML, bad-of-
visual word, bilateral filter.
• Have traveled to: USA, Korea.
Want to travel to: China, Thailand, Spain
• Why Japan
-- historical and cultural connection
-- camera companies and electronics maker here
9. Robotics Contest @ ASABE 2009
2009
Robotics
Contest
Gender
Recognition
Contest
• Problem:
Detecting and Positioning the circular obstacle
• Technique:
Graphical simulation(OpenGL), Sensor
• Material:
Boe-Bot Toolkit, IR sensor, Ultrasonic
sensor, Zigbee wireless communication
10. Robotics Contest @ ASABE 2009
2009
Robotics
Contest
Gender
Recognition
Contest
Please Visit the following link
for viewing the video:
http://youtu.be/8EjON8Y2OJ0
13. Gender Recognition Contest
2009
Robotics
Contest
Gender
Recognition
Contest
• Problem:
Recognize the gender with single face image
• Technique:
Viola-Jones Face detector (OpenCV)
Feature-based alignment
False-alarm check
Utilize Skin color model, Eye
and Mouth detector to filter
the false-positive result from
the V-J face detector.
14. Gender Recognition Contest
2009
Robotics
Contest
Gender
Recognition
Contest
• Problem:
Recognize the gender with single face image
• Technique:
Viola-Jones Face detector (OpenCV)
Feature-based alignment
False-alarm check
• Performance
1.2 second per 480*320 image
• Result
~85% face detection accuracy
~75% gender recognition accuracy
Win 3rd place among 20 teams (Taiwan and
China). Bonus 60,0000yen.
15. Fish Recognition
2010
ISMAB
NTU
Graduate
Internship
@ TV corp.
• Problem:
Tuna species recognition for fishery
conservation and management
• Task:
Detection and Classification
Bigeye
YellowfinAlbacore
3 spices are considered2011
Navy
16. Fish Recognition
• Problem:
Tuna species recognition for fishery
conservation and management
• Task:
Detection and Classification
Fish Image are captured
in certain lighting condition
with measurement plate.
Body part is smooth,
makes it reflect light well.
72%
2010
ISMAB
NTU
Graduate
Internship
@ TV corp.
2011
Navy
17. Fish Recognition
• Problem:
Tuna species recognition for fishery
conservation and management
• Task:
Detection and Classification
B Y A
B 89 10 5
Y 9 86 3
A 10 9 81
84%
34% 72% 52% 58%
Confusion Matrix
(Head)(Abdomen)(Tail fin) (Tail)
Discriminate part!
2010
ISMAB
NTU
Graduate
Internship
@ TV corp.
2011
Navy
24. Introduction to Bilateral Filter
2010
ISMAB
NTU
Graduate
Internship
@ TV corp.
2011
Navy
Bilateral Filter [Tomasi, ICCV98]:
f(x) h(x)
“Bi” lateral = Spatial term + Range term
25. Introduction to Bilateral Filter
2010
ISMAB
NTU
Graduate
Internship
@ TV corp.
2011
Navy
Bilateral Filter [Tomasi, ICCV98]:
f(x) h(x)
“Bi” lateral = Spatial term + Range term
26. Introduction to Bilateral Filter
2010
ISMAB
NTU
Graduate
Internship
@ TV corp.
2011
Navy
Bilateral Filter [Tomasi, ICCV98]:
f(x) h(x)
“Bi” lateral = Spatial term + Range term
27. Application of Bilateral Filter
2010
ISMAB
NTU
Graduate
Internship
@ TV corp.
2011
Navy
“Bi” lateral = Spatial term + Range term
Smooth Target Edge-Preserving Result
And this one?
37. Reason?
Boundary
Alternating repetition
(Texture)
Contrast Levels of scale
Roughness
Strong centers
Positive space
Local symmetries
The Void
Not-separateness
Good shape
GradientsEchoes
Simplicity and Inner Calm
Deep interlock and ambiguity
Color
Composition
Harmonium
Richness
40. ICPR 2012
2012
Research
Assistant
ICPR
2012
ACCV
2012
As a Pattern Recognition Problem…
Comparison of feature
1. Edge distribution, Color histogram,
Hue, Saturation.. [Ke, CVPR06]
2. SIFT + BOV [Marchesotti , ICCV11]
3. Composition layout (Edge + HSV),
Color palette, contrast.. [Proposed]
Result
Item Speed on PC Accuracy
CVPR06 0.2s 81%
ICCV11 4s 85%
Proposed 0.16s 84%
[ Photo aesthetics assessment with efficiency ]
41. Extraction of Color Information
Extract N
Dominant colors
(we set N=5)
K-Nearest Neighbor
(K=20)
List of Palettes
Dictionary
HQ Palettes Dictionary
LQ Palettes Dictionary
Palettes of Photo
44. Discussion 1
Device : On-line assistive camera system
• Contextual Information (Viewing angle)
camera < human
• Feedback
from analysis to advice
• Human behavior
What do people take?
How do people take?
• Computation
Server-based v.s. Device
• Market and Needs
45. Discussion 2
Algorithm: photo aesthetic value assessment
• Definition of photo aesthetics
Expert v.s. Volkswagen
• Labeling process
Individual bias and variance.
Absolute or Relative evaluation
Effect of Labeling order
• Quantify photo aesthetic
Modeling, the Personalization
46. Thanks for your attention!
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Notes de l'éditeur
2013 Luckily be here with Sato Lab.
I started learning Japanese at the first year in Univ.NTU, National Taiwan University33,000 students (UT40,000)1.6km2 (~= UT)
After ended my first year in university, I figured out the life here is not what I want.So I planed a travel, to see people on the opposite of the earth,How they act, how they
Since there is a required bachelor thesis project at our department,I choose the lab lead by Prof. Lin, which focus on image processing and inspection technique.Start to play with OpenGL, OpenCV, and the GUI software Boaland C++
2013 Luckily be here with Sato Lab.2012 ICPR and ACCV works, sponsored by Panasonic Scholarship2012 Join Academia Sinica, literally "Central Research Academy”.~50 CS P.I. (principal investigator) in two institution. Has paper on competitive conf. such as ICCV, CVPR, MM, every year.2011 Navy, duty and vomit on the ship. Political warfare2010 Internship , HsinChu, Taiwan intern in Intelligent Video Processing Research Group • Automatic 2D-to-3D Conversion of video for 3D TV• Survey the standards of stereoscopic video• Depth map generation, depth fusion, saliency detection 2010 graduate2010 Paper Tuna fish species classification using support vector ma-chine”. International Symposium on Machinery and Mechatronics for Agricultural and Biosys-tems Engineering (ISMAB) 2009 UTech Machine Vision Prize, 3rd place • Human Face Sex Recognition (3 out of 24 teams from Taiwan and China)• Part of winning team with prize NTD 200,000.2009Robotics Competition American Society of Agricultural and Biological Engineers Annual International Meeting• Build a multi-robot system equipped with Infra-Red sensors and Ultrasonic sensor• Each robot cooperate with Server through Zigbee wireless communication• Lead a team of 4 member for 6 months preparation (rank 4 out of 8 teams from U.S.)2008 Participating in the Lab, began to learn OpenGL and OpenCV2007 Language School at Univ. Pennsylvania, United State2006 National Taiwan University (rank 80, 2012 QS)
2013 Luckily be here with Sato Lab.2012 ICPR and ACCV works, sponsored by Panasonic Scholarship2012 Join Academia Sinica, literally "Central Research Academy”.~50 CS P.I. (principal investigator) in two institution. Has paper on competitive conf. such as ICCV, CVPR, MM, every year.2011 Navy, duty and vomit on the ship. Political warfare2010 Internship , HsinChu, Taiwan intern in Intelligent Video Processing Research Group • Automatic 2D-to-3D Conversion of video for 3D TV• Survey the standards of stereoscopic video• Depth map generation, depth fusion, saliency detection 2010 graduate2010 Paper Tuna fish species classification using support vector ma-chine”. International Symposium on Machinery and Mechatronics for Agricultural and Biosys-tems Engineering (ISMAB) 2009 UTech Machine Vision Prize, 3rd place • Human Face Sex Recognition (3 out of 24 teams from Taiwan and China)• Part of winning team with prize NTD 200,000.2009Robotics Competition American Society of Agricultural and Biological Engineers Annual International Meeting• Build a multi-robot system equipped with Infra-Red sensors and Ultrasonic sensor• Each robot cooperate with Server through Zigbee wireless communication• Lead a team of 4 member for 6 months preparation (rank 4 out of 8 teams from U.S.)2008 Participating in the Lab, began to learn OpenGL and OpenCV2007 Language School at Univ. Pennsylvania, United State2006 National Taiwan University (rank 80, 2012 QS)
