Experience from EU funded research projects and knowledge ...

Experience from EU funded
research projects and knowledge
transfer to industry
Václav Hlaváč
Czech Technical University in Prague
Faculty of Electrical Engineering
Department of Cybernetics
Head of the Center for Machine Perception
Karlovo náměstí 13
121 35 Prague 2, Czech Republic
hlavac@fel.cvut.cz; http://cmp.felk.cvut.cz

Outline of the talk
2
1. Who we are?
2. EU funded projects,
mainly „Cognitive …“
3. Current running
projects snapshots.
4. Transfer to industry.
5. How to submit a
successfull
application?
• CMP belongs to the
Department of
Cybernetics
• Head: Prof. V. Mařík

CMP at glance
• Established 1986,
name CMP since
1995.
• Research interests:
• computer vision,
• pattern recognition,
• mathematics of
uncertainty,
• robotics
• medical
imaging.
Impact & excellence
• numerous science prizes,
• hundreds of ISI citations
per year,
• competitive results in
contests,
• collaboration with hightech
companies
• significant % of funding
from EU and private
companies,
• two spin-out companies..
3

CMP personnel
• 29 staff members (2 profs., 4 assoc. profs.,
6 assist. profs., 15 researchers, 2 support
staff)
• 10 full time PhD students, 15 MSc student
theses in progress.
• Key researchers, heads of teams:
B. Flach, V. Hlaváč, J. Matas, M. Navara, T.
Pajdla, R. Šára.
• Postdocs: J. Čech, P. Doubek, O. Drbohlav, V. Franc, O.
Chum, J. Kostlivá, J. Kybic, Š. Obdržálek, M. Matoušek, T.
Svoboda, L. Špaček, A. Torii, T. Werner, K. Zimmermann.
4

Academic research interests
• Geometry of 3D vision (T. Pajdla)
• Correspondence problem, stereo (R. Šára)
• Statistical and structural pattern recognition
(V. Hlaváč, J. Matas)
• Optimization on MRFs (B. Flach.,T. Werner)
• Object detection and recognition in images,
face detection (J. Matas, V. Hlaváč)
• Mathematics of uncertainty (M. Navara, P.
Pták)
• Multicamera systems (T. Svoboda)
• Registration in medical images (J. Kybic)
5

CMP has two spin-outs
6
Two hi-tech companies:
• Neovision s.r.o.
• Eydea Recognition s.r.o.
• Cooperation within CTU, inside the
department, …

Neovision s.r.o.
• Spin-off company
• Founded in 1996
•18 employees
• Transfer from CMP to industry
7
Customers
AVX Czech Republic s.r.o.
Czech Republic
Tantalum capacitors
Texas Instruments, Inc.
USA
quality control &
precise measurement
by image processing
Preciosa a.s.
Czech Republic
Siemes VDO
Frenštát p. Radhoštěm

• Founded in 2006, 4 staff, http://www.eyedea.cz
• Commercializes results of J. Matas’s group.
• Main applications in traffic surveillance.
8

Past EU IST funded projects
• RECCAD (V. Hlaváč, COPERNICUS, till 1999)
3-D Surface Reconstruction for CAD Modelling
• ActIPret (V. Hlaváč, 5 th FP, till 2004)
Interpreting and Understanding Activities of Expert
Operators for Teaching and Education
• BENOGO (T. Pajdla, 5 th FP, till 2005)
Being There - Without Going
• COSPAL (V. Hlaváč, 5 th FP, till 2007)
COgnitiveSystems using Perception-Action Learning
• e-Trims (R. Šára, STREP, 6 th FP, till 2009)
Interpreting Images of Man-Made Scenes
• VISIONTRAIN (V. Hlaváč, Marie-Curie RTN, till 2009)
Computational and Cognitive Vision Systems: A Training
European Network
9

EU IST funded projects
Current
• DIRAC (T. Pajdla, IP, 6 th FP)
Detection & Identification of Rare Audio-Visual Cues
• WARTHE (V. Hlaváč, 6 th FP), Marie-Curie RTN
Wide Area Research Training in Health Engineering
• DIPLECS (T. Werner, STREP, 7 th FP)
Dynamic Interactive Perception-action LEarning in Cognitive Systems
Started from January 1, 2009, 7 th FP
• Humavips (V. Hlaváč, STREP, from Feb 1)
Humanoids with auditory and visual abilities in populated spaces
• NIFTi (V. Hlaváč, IP)
Natural human-robot cooperation in dynamic environments
• interactIVe (R. Šára, IP)
Accident avoidance by active intervention for Intelligent Vehicles
• MASH (J. Matas, STREP)
Massive sets of Heuristics for Machine Learning
10

Competitive industrial support
• Google research projects, two of the three in the Czech
Republic.
• Microsoft research project.
• Toyota, has been supported 3 full time researchers since 5
years.
• VW, was 2 years project, EU project interactIVe follows.
• Daimler research, 2 part time PhD students from Daimler.
• Honeywell Research Labs projects.
• Many smaller contracts with local and foreign companies.
11
• Often knowledge transferred, e.g., to our two spin-out
companies.

3D reconstruction from many views
12
250 input images
pairwise depth maps
+
computed 3d model
computed camera poses

DIRAC, Detection of pedestrians
13
Result of the EC funded project DIRAC, T. Pajdla.

DIPLECS
• ultra-fast learned trackers
14

“Reading” Facades
Task: Find same-style windows
15
Many facades around us:
consider Google StreetView

Object detection and recognition
Application: Detection and Recognition of Traffic Signs
16

Stereoscopic Perception
17
correspondence search
result: depth map
Depth perception from a moving stereo pair

Sputnik tracker
18
• Tracking with a
companion.
• Finds the
companion
automatically.
• Does not loose
tracks when
the template is
not visible.
Video, click on it.

Daimler truck problem
19
• The main problem: truck drivers have limited
view

Motivation
• Large blind spot areas emerge in front of the
truck, on the passenger side, and behind the
vehicle 2.5m
20
• Mirrors can only cover parts
of the areas
• View fragmented into many
mirrors

The Goal
Develop a method for constructing a view of
the area around the vehicle without blind spots
in real time
21

Overview
22
Four catadioptric cameras were
mounted on the vehicle
2 cameras on the truck
2 cameras on the trailer

Bird’s Eye View
23
Virtual image plane
A virtual pinhole camera is placed above the
vehicle and bird’s-eye view is generated from
catadioptric images

Corridors for maneuvering
24
• Green - the bumpers
of the truck and
trailer
• Blue – desired
parking position
• Red – must be free

Result: Daimler Actros Truck & Trailer
25

Aircondition pipes cleaning robot
• Dry ice
cleaning
26
• Tracks
driven

EU applications, the competition
27
• Cognitive systems and
robotics.
• Very competitive,
about 10% success
rate.
• The winner gets all.
• Fair evaluation. Do not
listen to rumors.
Quality matters.
• Random factor.
• Track record matters.
• It is funded only
because it is felt as a
strategic competence.
• Funding a proposal is
a risky investment
(even venture
capitalists do not pay
for it).
• Visionary proposals
expected (sometimes
even too much).

Why CMP has been successful?
• We were lucky, off course, not always.
• We have had excellent academic results.
• We have been building systematically links and
relations. Example: J. Matas is the IEEE PAMI
editor-in-chief.
• The group is known to deliver.
• CMP is a big and internally cooperating team, a lot
of background knowledge.
• We managed to obtain competitive Czech
Governments “the basis” financial support.
• We have had an enlightened direct boss.
28

Couple of advices
29
• Be in a good consortium.
Outsiders do not play this
game.
Example: Project ARTTS. There
was a Rumanian academic
partner which delivered probably
the most academically valuable
results in the project.
• Demonstrate your
competences, e.g., by
papers, demos.
• Have a good web page
with the interesting
content.
• Start the work on the
proposal in time. Your
future role in a project is
determined at the proposal
writing.
• Join networking activities,
e.g., EUCogII.
• Meet “to-be-partners”
personally, at conferences,
invite them to your lab, etc.
• Long term strategy: send
your fresh PhDs to good
groups as postdocs.

New member state, avoiding local obstacles
30
• Czech Republic, the Czech
Technical University
example.
• The “island” policy. Inside
(our group, department)
“normal” rules are valid
unlike at the faculty,
university level …
• Best people wanted. ⇒ Do
not be afraid to pay people
more than is the university
average.
• Contract money help.
• Ignore deliberately stupid
rules and do not use it in
your optimization criterion.
Example: Czech university
funding policy in last ≈10
years paying per student
irrespective to the quality
which resulted to drop of
the “average student
quality”. CMP stressed the
quality instead to be
prepared for normal times.
• Own financial manager.

31
Thank you for coming
and for the attention.