Dr. Kupervasser Oleg 8-916-4516193 (10.00-22.00) 8-499-134-3965 (20.00-22.00)

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Dr. Kupervasser Oleg8-916-4516193
(10.00-22.00)8-499-134-3965 (20.00-22.00)
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•   OLEG KUPERVASSERe-mail olegkup_at_yahoo.comaddr
  ess Russia, Vavilova 54-1-71 Moscow 119296Date
  of birth 01/02/1966Citizenship Russia, Israel
• Home page http//leah.haifa.ac.il/skogan/Apache/
  mydata1/Oleg_home/Oleg_home.html

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Education

• 2004 Haifa University Three dimensional protein
  structure. Postdoctorate.(http//leah.haifa.ac.il
  /skogan/Apache/mydata1/main.html) Perl, DHTML,
  Fortran, C languages
• 2001-2002 ATLAS college Bioinformatics for
  Hi-Tech people. Final project Three dimensional
  protein structure, under supervision of Professor
  Edward N. Trifonov from Weizmann Institute of
  Science. Including Basic knowledge in Biology,
  Bioinformatics, Programming (C, SQL, JAVA,
  DHTML, Bio-PERL, UNIX, MATLAB)
• Ph.D. 1992-1999 Weizmann Institute of Science,
  Faculty of Physics. Research project Nonlinear
  dynamics (Analytical methods and numerical
  algorithms for the solution of nonlinear physics
  equations). Fortran, C languages
• 1991-1992 Tel-Aviv University, Faculty of
  Engineering, department of Physical Electronics,
  Ph.D student, Tel Aviv, Free electron laser.
  Fortran languages
• M.Sc. 1983-1989 Moscow Institute of
  Radioengineering, Electronics and Automation.
  (theoretical and experimental electro-optics,
  laser, fiber optics and radars) Diploma honoris
  causa . Fortran language.

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Employment

• 2008-2010 Algorithm developer in Moscow State
  University, Russia, Moscow. Algorithm developer.
  C language
• Computer drug design, nano-systems computer
  modeling solvent influence on molecules
  interaction took part in scientific conferences
  submitted six papers and one published in high
  impact factor journal
• 2009-2009 Algorithm developer in UltraSpect,
  Image processing in Nuclear medicine. C
  language
• 2008-2008 Algorithm developer in Vayar Vision,
  Israel. Images search in Internet ("Google" for
  images). C language
• This company develops a search engine for images.
  For some picture this search engine looks for
  similar pictures in the given data basis of
  images. This similarity is not based on objects
  recognition. The similarity is based on not
  semantic characters (for example, a number of the
  boundaries pixels over image segments and etc.).
• 2005-2008 Rafael-Technion Image processing,
  multiple view geometry of smooth bodies,
  Navigation systems took part in scientific
  conferences two published papers Matlab
  language
• It is creation of algorithms in the field of
  image processing, computer vision for navigation
  of rockets. For Rafael (the leading Israeli
  company in the field of rocket weapons) programs
  was developed for navigation of rockets by means
  of the video images and the known terrain map.
  This is inverse problem with respect to the
  problem solved by means of Google Earth. In
  Google Earth for a given trajectory and
  orientation the correspondent images can be
  found. In the developed method a trajectory and
  orientation can be found on the basis of a video.
  From this experience expansion of Google Earth
  can be developed, allowing video-navigation for a
  flying plane, a rocket or a car from a video
  film.
• 2003-2004 Algorithm developer in Intel , Israel.
  Image processing in digital TV, C language
• 2002-2002 Algorithm developer in UltraSpect,
  Israel. Image processing in Nuclear medicine, C
  languages
• 2000-2001 Algorithm developer in Orbotech LTD,
  Israel. Recognition of glass defects and plate
  marks, Matlab, C languages.
• 1999-2000 Electronics engineer in Tower
  Semiconductor LTD, Israel. Design flash-memory,
  Excel

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• Student physics Olympiad in Moscow (1 prize)
• Student physics Olympiad in USSA (2 prize)
• About 27 publications in physics, image processing

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• Continuum solution model.
• Moscow State University

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Process of solute solvation in solvent
Continuum solution model.
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Three components of Gibbs energy

• ?Gs ?Gcav ?Gnp?Gpol,
• ?Gcav Hydrophobic component
• ?Gnp - Van-der-Waals component
• ?Gpol - polarization component

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Methods for finding of polarization component
?Gpol .

• Exact numerical methods
• Solution of Poisson equation in 3D
• Solution of equivalent equation for charge on
  solvent excluded surface (PCM)
• Simplified PCM COSMO water (e78) is changed
  to metal (e8).
• Exact analytical method for spherical cavity
  ???????
• Multipole moments method,
• Mirror charges method
• Heuristic model
• Generalized Born,
• Surface Generalized Born.

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About solute surface construction in ???
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Protein Loop-Lock StructureHaifa University

• We must decompose a protein on set of closed
  loops. The developed Internet site solves this
  task.

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Protein Loop-Lock Structure

• We must decompose a protein on set of closed
  loops. The developed Internet site solves this
  task.

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Images Similarity Engine.Vayar Vision
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Similarity Engine

• Similarity Engine
• Finding images similar to some given image in
  some image database.
• First step basic features definition
• Indexing of all images in database by help these
  features
• Indexing of some given images by help these
  features
• Finding image (or images) with maximum feature
  similarity from database

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• large number of investigation exists in the
  field of images similarity. But the current
  Engine
• 1) Can generate almost infinity number of
  features used for images similarity.
• 2) The system can learned from mistakes.
• 3) The system can be easily adaptive to some new
  definition of similarity
• 4) The set of used feature has no semantic sense.
• We dont make recognition of objects on image and
  use such no sense features as number of pixels on
  object boundaries and etc.

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Found similar images by help our Engine from
terragaleria site
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Found similar images by help our Engine from
terragaleria site
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Found similar images by help our Engine from
terragaleria site
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Field of use

1. Images similarity engine in Internet (like Google
   for words)
2. Finding relevant images in film for product
   advertising setting
3. Finding relevant information about some building
   or place from photo or film.
4. Navigation by place recognition from photo or
   film (GPS equivalent) by help Google Earth
5. Find similar images for intellectual property
   rights control

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• Vision Based Navigation from Image Sequence and
  Digital Terrain Model.
• Technion

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Vision Based Navigation from Image Sequence and
Digital Terrain Model.

• It is creation of algorithms in the field of
  image processing, computer vision for navigation
  of rockets. For Rafael (the leading Israeli
  company in the field of rocket weapons) programs
  was developed for navigation of rockets by means
  of the video images and the known terrain map.
  This is inverse problem with respect to the
  problem solved by means of Google Earth. In
  Google Earth for a given trajectory and
  orientation the correspondent images can be
  found. In the developed method a trajectory and
  orientation can be found on the basis of a video.
  From this experience expansion of Google Earth
  can be developed, allowing video-navigation for a
  flying plane, a rocket or a car from a video
  film.

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Vision Based Navigation from Image Sequence and
Digital Terrain Model
Two consecutive images
Compute n feature correspondences
2n constraints (?u, ?v)
Compute pose and ego-motion that best explain the
features movement
DTM
Pose and ego-motion
12 variables
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Recovering Epipolar Geometry from Images of
Smooth Surfaces Technion
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• We present four methods for recovering the
  epipolar geometry from images of smooth surfaces.
  Existing methods for recovering epipolar geometry
  use corresponding feature points that cannot be
  found in such images. The first method is based
  on finding corresponding characteristic points
  created by illumination (ICPM - illumination
  characteristic points method). The second method
  is based on correspondent tangency points created
  by tangents from epipoles to outline of smooth
  bodies (OTPM - outline tangent points method).
  These two methods are exact and give correct
  results for real images, because positions of the
  corresponding illumination characteristic points
  and corresponding outline are known with small
  errors. But the second method is limited either
  to special type of scenes or to restricted camera
  motion. We also consider two else methods, termed
  CCPM (curve characteristic points method) and
  CTPM (curve tangent points method), for search
  epipolar geometry for images of smooth bodies
  based on a set of level curves with a constant
  illumination intensity. The CCPM method is based
  on search correspondent points on isophoto curves
  with the help of correlation of curvatures
  between these lines. The CTPM method is based on
  property of the tangential to isophoto curve
  epipolarline to map into the tangential to
  correspondent isophoto curves epipolar line.
  Unfortunately these two methods give us only
  finite subset of solution, which usually include
  "good" solution, but don't allow us to find this
  "good" solution among this subset. Exception is
  the case of epipoles in infinity. The main reason
  for such result is inexactness of constant
  brightness assumption for smooth bodies. But
  outline and illumination characteristic points
  are not influenced this inexactness. So the first
  pair of methods gives exact result.

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Correspondent points
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Epipolar geometry
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• Image processing in the Nuclear medicine.
• UltraSpect

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Diagram of parallel-hole collimator attached to a
crystal of a gamma camera. Obliquely incident
gamma-rays are absorbed by the septa.
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Gamma Camera
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Image Processing

• Direct problem
• To get Image on gamma camera from image of
  radiated particles distribution
• Inverse problem
• To get image of radiated particles distribution
  from Image in on gamma camera
• Slice of Brain

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Deinterlacing for video. Intel
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Transform of interlaced video frames to
progressive video frames.
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Main problemsMotion regions, no-smooth boundary
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Steps of Algorithm.

• Motion detection
• Interpolation or motion compensation
• 3) Detection of direction
• 4) Detection of angles
• 5) Detection of zebra
• 6) Detection of high entropy regions
• 7) taking into account history and environment
• 8) Fuzzy boundary between motion and no motion
  regions
• 9) Median filtering, Mean filtering over space
  and time.

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Detection and recognition of defects on the
glass Orbotech LTD
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Types of defects bubbles, scratchs, dirty spots
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Steps of Algorithm

• Segmentation
• Descriptors (features) definition
• 3) Finding P(XjHk) , Xj-Descriptor (N), Hk -
  type of defect (M)
• 4) Naive bayes model
• XX1,,XN
• P(XHk) ?j P(XjHk)
• P (HkX) P(Hk)P(XHk) / P(X)
• P(X)SkP(Hk) P(XHk)

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Flash memory designTower Semiconductor LTD
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SONOS Transistor
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Flash memory designTower Semiconductor LTD

• Siliconoxide-nitrideoxidesilicon memory
  transistor, where information is stored as two
  charges in nitride at the edges of the channel.
• Silicon-Oxide-Nitride-Oxide-Silicon memmory
  (SONOS)
• A SONOS memory cell is formed from a standard
  polysilicon NMOS transistor with the addition of
  a small sliver of silicon nitride inserted inside
  the transistor's gate oxide. The sliver of
  nitride is non-conductive but contains a large
  number of charge trapping sites able to hold an
  electrostatic charge. The nitride layer is
  electrically isolated from the surrounding
  transistor, although charges stored on the
  nitride directly affect the conductivity of the
  underlying transistor channel. The oxide/nitride
  sandwich typically consists of a 2 nm thick oxide
  lower layer, a 5 nm thick silicon nitride middle
  layer, and a 510 nm oxide upper layer.SONOS
  promises lower programming voltages and higher
  program/erase cycle endurance than
  polysilicon-based flash. SONOS distinguished from
  mainstream flash by the use of silicon nitride
  (Si3N4) instead of polysilicon for the charge
  storage material.