Promoting spin-off companies from engineering

Augusto SartiDipartimento di Elettronica e Informazione

Politecnico di Milano

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Why?o Take academic research a few steps ahead (engineering, industrialization, production, ...)o Keep in touch with the industrial world (cross-fertilization btw industry and Academia)o Contribute to development of industrial potential (in highly technological fields)o Give more opportunities to our graduates and alumni (this also simplifies tech transfer)o Make the University take a stand on socio-economic development at regional or even

national level

Why not?o Spin-Offs should not get in competition with Departments (not so small a risk or so trivial to

manage, as government research funds dry out by the day)o Background transfer needs be kept under watch

All such issues have been carefully addressed by the Politecnico di Milano through the Technological Transfer Office

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Spin-offs ... and Deptartments

Motivations and risks

TRE:Information from SpaceTRE:Information from Space

Tele-Rilevamento Europa

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In particular, our work is focused on:

detection,

measurement,

monitoring,

of land movement

TRE Mission

Committed to developing and

implementing radar-based technologies

that provide reliable information from

remote-sensing data and solve real-life

problems

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TRE: from research to business

o 20 years of research in Data Signal Processing Radar Group, Eln. Dept. Politecnico di Milano

o May 1999: Permanent Scatterers Technique Patent IT, EU, USA (ext. Australia, Japan) POLIMI PS Technique™ - PSInSAR™

o March 2000: TRE foundation first spin-off company of Politecnico di Milano Worldwide exclusive licensee of the patent

o Shareholders: 10% Politecnico, 90% the inventors

o June 2005: ISO 9001:2000 certification

o January 2008: TRE expands to North America, creating TRE-Canada, a Canadian subsidiary of TRE

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Core businessSan Francisco Bay Area

Los Angeles Basin

Las Vegas

New Orleans

IstanbulVancouve

r

Rome

Long Beach, L.A.

Venice

thousands of satellite images processed all over the world

Detecting and monitoring ground displacements

hundreds of projects carried out using radar data

Tokyo

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Oilfield MonitoringMiddle East

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TRE in 8 years…

o Staff: > 25 peopleo Turnover: 4.2 M€ (2007), 4.7 M€ (forecast 2008)o Proprietary PSInSAR™ software: > 400,000 C-code lineso Processing Center: 128-node Linux Clustero > 14,000 radar scenes processedo > 750,000 sqKm analyzed o > 100,000,000 PS identifiedo TRE Canada incorporated in January 2008

TRE Proc. Centre

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Company growth

Turnover

Markets

Oil & Gas56%

Pub. Adm.14%

Univ. & Research12%

Industry8%

Project7%

Other3%

€ -

€ 500.000,00

€ 1.000.000,00

€ 1.500.000,00

€ 2.000.000,00

€ 2.500.000,00

€ 3.000.000,00

€ 3.500.000,00

€ 4.000.000,00

€ 4.500.000,00

2004 2005 2006 2007

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PS DataPS Data

Public AuthoritiesPublic AuthoritiesHazard mitigationHazard mitigation

Oil&Gas Comp.Oil&Gas Comp.Oil/gas field monitoringOil/gas field monitoring

Insurance Insurance CompaniesCompaniesClaim assessmentClaim assessment

Civil ProtectionCivil ProtectionRisk/Hazard mapsRisk/Hazard maps

Engineering Comp.Engineering Comp.Stability check - Track routingStability check - Track routing

Research & UniversitySurface deformation monitoring Surface deformation monitoring

Target Markets

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Main customers

• Eni S.p.A.

• Shell

• Petroleum Development Oman

• Devon Canada

• Civil Protection

• Environment Ministry

• Regional environment Agency of Emilia

– Romagna, Lombardia, Piemonte

• Region of Lazio

• Region of Liguria

• Province of Trento

• Polytechnic University of Milano

• INGV (National Institute of Geology)

• University of Florence, of Bologna, of

Calabria

• University of Berkeley

• Stanford University

• University of Miami

• University of Alaska

• Snam Rete Gas (ENI Group)

• AEM S.P.A (Electricity supplier)

• CAVET (Engineering)

• CESI (Electricity supplier)

• Enel.Hydro (Electricity supplier)

• Image ONE (Japan)

Energy Sector and Engineering Oil Companies

University and Research Centers Public Administrations

Kee Square:Sensing intelligenceKee Square:Sensing intelligence

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Kee Squarecompany overview:

shareholders

Spin Off company of the Politecnico di Milano, founded in July 2007

Founding partners– Politecnico di Milano (directly and through the participation of

Prof. Augusto Sarti and Prof. Stefano Tubaro)– Celin Technology Innovation SRL, company founded to deploy innovative products

in the ICT market– Ghirlanda Smart Card Solutions SpA, production of magnetic and microprocessor

cards for banks, ID, security and health

Financial partner– Quantica SGR with Principia Fund is the first Italian Private Equity Fund promoted

by experienced managers and prestigious research and university institutions. Principia selects and proposes to investors scientific innovations on the technological frontier to be transferred with profit into market-driven products

– Technical due diligence from CNR (National Research Council)

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Kee Squarecompany overview:

mission

Development of innovative techniques in video and audio processing for:

– biometric identification and tracking– detection of hazardous events– Sensing intelligence

Advanced Know-How collected in 15 years of research on

– Digital Image Processing – Digital Audio Processing– Statistical

Pattern Analisys– Parallel

Programming

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Kee Squareproducts based on

face analysis

Kee Square face analysis products (stand-alone or SDK):– Morpheus FF – Face Finder– Morpheus FR – Face Recognition– Morpheus ZS – Zone Screening– Morpheus ICAO – ISO 19794/5 Conformity Check– Morpheus AI – Audience intelligence

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Morpheus FF:fast, robust & accurate

Face Finder

Morpheus Face Finder is based on two pipelined algorithms that are specialized for

– Fast localization of image areas that are likely to contain faces• Fast multi-scale and multi-location search

– Accurate localization of facial features (eyes, mouth, eyebrows etc.)• Robust against unfavorable illumination conditions • Robust against occlusions (dark sunglasses, scarves, etc.)

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Morpheus FF:training

process

Proprietary tagged face image database

Tag information classes:– Position of Face Mask points– Photografic set: focus, illumination,

background, etc.– Subject: gender, age, ethnic group, etc.– Face: pose, eye expression, gaze,

mouth expression, etc.– Morphology: eye type, lip type,

nose type, mouth type, etc.

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Morpheus FR database

search

Law enforcement agencies use face recognition for automatic mugshot database reduction in their forensic investigative work

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Morpheus ZS:face recognition

for Zone Screening

Suitable for crowded areas (airports, rail stations, etc.) as it relies on distant cameras to identify people

Screening method that selects only those people that are worth checking: the final decision on their identity can then be made by an operator (security monitor, pocket pc, etc.)

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Morpheus ICAOISO/IEC 19794-5

conformity check

Evaluates facial images according to the ICAO ISO/IEC 19794-5, which defines the requirements for digital image geometry and scenery, and returns Token Frontal Face Images and Full Frontal Face Images that are compliant with it

Competitive Advantage– Accurate and Robust Face Finder– Accurate Quality Control Check– Very fast processing: real-time ISO-compliant check & acquisition

Token FrontalFace Image

Digital Face Image

Full FrontalFace Image

company profile - September 0821

Morpheus AIAudience Intelligence

for Advertisingsoftware package for audience measurement based on the analysis of

face images

o Through standard webcams, Morpheus AI monitors people while they are looking at an advertisement. Morpheus AI, in fact, can automatically extract audience data from image stills or video streams, to be used as an immediate feedback on marketing effectiveness, or for collecting statistics.

o Morpheus AI is able to automatically extract information such as :

– Gender (male, female)– Age (children, young, adult, elderly)– Ethnic (Caucasian, African, Asian)– Attention span

More to come...

o Automatic video archive taggingo Novel intelligent applications for MIDs (mobile internet devices), in

collaboration with INTEL– User profiling– Environment profiling– Data access control– ...

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Audio security:motivations

The information carried by sounds is complementary to that carried by images

There are a wide range of sounds that clearly identify hazardous events– Impulsive sounds: gunshot, explosion, car crash, glass shattering, etc.– Continuous sounds: structural collapse, human scream, brawl, etc.

These sounds exhibit distinctive characteristics that allow us to recognize them from background noise

Localization and recognition are of great interestto numerous applications

– Threat alert– Early warning on threats, potential risks,

acts of vandalism, suspiciousor forbidden behavior

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The KeeSonicsuite:

overview

Class of products for the intelligent acoustic monitoringof outdoor and indoor environments to recognize,classify and localize sounds that are associatedto dangerous conditions

The location and the nature of the hazard can be used for triggering a reaction on the part of the environment– Pointing and tracking cameras– Warnings or deterrent actions– Police intervention

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The KeeSonicsuite:

modules

KeeSonic Baseline – Recognition of specific classes of acoustic events from single listening points

• Screams, gunshots and explosions, smashed windows or vehicles, car accidents, running engines, etc.

KeeSonic Evolution– Recognition of subtle and high-level acoustic events through tracking of

temporal evolution of features• Aggressions, mobs, brawls, etc.

KeeSonic Enhanced– Baseline/Evolution + advanced array processing

• Virtual directional microphones, virtual acoustic screens, beamformers, etc.KeeSonic WideRange

– Baseline/Evolution + localization + acoustic focusing• Direction of Arrival (DOA)• Location (with two arrays)• Risk map (with multiple arrays)

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Productreferences

Working installation of KeeSonic at the Monte Santo subway/train station in Naples, in cooperation with Nexera SCPA

Ground floor (approx 2000 sqm) covered with– 8 listening points (Kee Sonic baseline)– 2 arrays of 4 mics each (Kee Sonic Baseline + Kee Wave)– 1000m of mic wire– 4 PC racks and two 8-mic audio rack units– Detection of screams, gunshots, and other events– Resilient to specific noise sources

Last-minute news

– Kee Square turns out to be one of the three finalists in the ACES awards in TWO different categories, winner to be selected on Dec. 2, 2008 in Stockholm

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Eventrecognition

What’s behind it?– Acquisition

• Audio streams are acquired and transferred tocomputing units through appropriate channels(wires, optical fibers, wireless connections,audio over IP, etc.)

– Pre-processing• Preliminary signal conditioning

– Feature extraction• Classification and recognition are based on

metrics applied to audio features– Classification

• Complex and effective classification engines areused for deciding what event the soundcorresponds to, and assigning each audio tag alikelihood “score”

Acquisition

Pre-processing

Feature Extraction

Classification

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Morpheus FRFRGC 2.0 test

results

Good performance with medium-to-lowfacial image resolution(70-35 px eye-to-eye distance)

Good performance withuncontrolled facial image

Small template size250 floating point array, 1.7 KB

Fast template generation25 biometric templates per second*

Fast similarity score generation500.000 templates per second*

*cpu entry level Core 2 Duo 1.86 GHz

Exp Target(n. identities)

Query(n. identities)

Compares(millions)

1 16028 16028 257

4 16028 8014 128