narayanan sundaram. conference stats ~300 papers 40 orals rest were posters ~700 people ~50% from...
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Conference stats~300 papers
40 oralsRest were posters
~700 people~50% from Europe~33% from U.S & North AmericaRest from Asia & Oceania
4 days main conference1 day tutorials2 day workshops
Some interesting trendsConvergence of vision and graphics (image
composition, restoration of old video tapes etc.)Structure from motion for elastic surfaces (mostly
faces)Facial expression/beauty analysis/synthesisUnderstanding volumes (buildings/indoor scenes etc)Object recognition/segmentation (lot of papers use
Markov random fields in some way or the other)Recognition with humans in the loopConvex optimization for vision problems (avoiding the
initialization problem)
Session on “Vision & Industry”Talks from
IBMGESiemensLockheed MartinAdobeDxO Labs (France)
Where has CV worked and where has it failed?
What are the opportunities and threats?
Visual Analytics for Shrinking Checkout Shrink (Sharathchandra Pankanti - IBM)CV from security cameras for checking retail fraud
~$10/lane/day (~10’s of billions of $ lost in US & Europe every year)
50% due to dishonest employees/cashiersFraud in self-checkouts, various types of cashier fraudUsual action sequence is Pickup->Scan->Drop
Huge variations (coupons, returns mid-way, barcode errors etc)Constrained viterbi algorithm for optimal alignment recognitionVisual Verification Service (VVS) sold a product
ChallengesOnline learning (list of products, barcodes keeps changing)20-30,000 unique barcodes/storeNeed days worth of training dataDifferentiating very similar looking products (store brand vs name
brand)
GE's Computer Vision: from Prisons to Healthcare (Peter Tu)Projects
Shopping: mostly tracking shoppersDHS : tracking people at stadium (>2000
people/hour)Mock prison riots: people tracking + group dynamicsAction recognition : crouching, throwing etc.Face recognition: Under unreliable environmentsSocial network analysis: Identify leaders in groups“People as sensors”“FBI : “Face from skull”Pathology: Cell counting
GE (contd)Main question : “Is there enough Return on
investment for customers from computer vision?”Not much at the momentMostly added benefits, not primary benefits
E.g. video analytics industry makes as much as the septic tank industry
2 death sentencesHigh false alarm rateNeed a PhD to install the product and calibrate the
setupProving algorithms in real sites under real
environments in real time will make a difference
Visual Cognition, Semantic & Quantitative Imaging (Ramesh Visvanathan - Siemens)Mostly in medical and industrial imaging
Cardiac segmentation (4D)User guided segmentation of other organsGas turbines – defect modeling + analysisPeople – queue management – How long does it take
for a person to go through airport security?Challenges
How do we represent and update knowledge systematically?
What guarantees can we give?Do not want to do what A.I did in the 80’s
(overpromise and under deliver)
Siemens (contd)Video analytics
Low end is already commoditized (badly)Algorithms are not robust at all – no guaranteesPrices down to ~$100/licenseOverpromising customers may lead to a
backlash against all commodity CV productsHigh end video analytics
Require very robust algorithms<0.1% false alarms>95% true detection at 120 meters
Applications of Computer Vision in Airborne Surveillance (Michael E. Bazakos – Lockheed Martin)“Need capabilities, not technologies”
Need timely and actionable information from data
Unless false alarm rate goes down, pilots will just switch off the CV
Specify the conditions under which the algorithm will work Daylight only (or) x pixels on objects (or) upto
n degrees of gaze etc.
From PostScript to face detectors: How computer vision is transforming Adobe (Lubomir Bourdev)Adobe doing more and more vision and graphics researchAdobe has transitioned from
Low level -> High level analysisPixel level -> Cross-image content
ExamplesRed-eye removalPanoramic compositionTagging peopleSelecting objects
ChallengesAlgorithms are not robustUser Interface issues (how to get feedback from users)
Information capacity: a measure of potential image quality of a digital camera (Frederic Guichard – DxO labs)IP licensing of IC design, optics design &
software“Image processing at capture level”Image processing on cameras is huge 2008-
2010Processing in easy (Moore’s law)Lens+sensor – costly and slow
Camera phones already at photonic/diffraction/thermal noise limitsSLRs are getting there
Computational imaging is the future
DxO labs (contd)Future:
Improve digital calibration Takes 20 sec – 1 min for calibrating a single iPhone
camera Have to use on-the-fly correction Get rid of calibration
New functions for photographers (several shots /several cameras) Extended DOF (autofocus is costly and bulky) DxO’s solution based on different focus depths for
RGB channels and correct from there
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