Dense
Disparity Map Estimation (MSc Work)
Traffic
monitoring (PhD Work)
ImageSequence Registration
Vehicle Detection and Tracking
PostDoc
Work
NonRigid Registration of Stroboscopic Data (Image
Series)
Microtubule Segmentation
CTF Simulator (GUI)
Defocus Estimation
Focal Series
Radiation Damage Studies
Presentation: Download
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made a presentation of this page. Presentation is in a PDF format. PDF contains
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Dense
Disparity Map Estimation (MSc Work)
Fig (a) 
Fig (b) 
The objective in
dense disparity map estimation is to obtain a textured metric 3D surface model
starting with a sequence of free hand camera images (Figure a, taken from Pollefeys’
thesis). I investigated on how to find all
corresponding pixels within epipolar image while
dealing with high frequency regions, sparse structures and edge discontinuities
(Figure b).
Publications
F. Samadzadegan, F. Karimi Nejadasl, Two Algorithms for Dense
DisparityMap Estimation of Close Range Images, Iranian Scientific Information
Journal, 2003 (in Persian)
Center of
Iran, Tehran, Iran, May 2003 (in Persian)
F. Samadzadegan, F. Karimi Nejadasl, Dense DisparityMap
Estimation of Close Range Images using Optical Flow, National Conference of
National
Cartographic
Center of Iran, Tehran, Iran, 2003 (in Persian)
F. Karimi
Nejadasl, Automatic Dense DisparityMap Estimation of CloseRange Images,
Master Thesis, 2002 (in Persian)
Traffic
monitoring (PhD Work)



During
my PhD, I worked with large image sequences recorded from a helicopter hovering
above a freeway. The objective was to obtain the trajectories of each
individual vehicle to model the behavior of the drivers. For this purpose, all
images had to be registered and the camera motion had to be accounted for.
Sequential image registration would suffer from errorpropagation. A detailed
modeling of the complete camera trajectory and the scene would be a complicated
and computational expensive task. My research has shown that this problem can
be tackled by making the model scenedepth independent using homography. I developed a framework for local registration
of road areas in a robust and automatic way. As a part of the framework, the
images were registered by searching in a parameter space. The search was done
using differential evolution as a global optimizer.
An object
identification method was developed to detect many moving vehicles with sparse
details, different speeds, and low contrast with respect to their background.
For each vehicle, robust tracking was achieved by a statistical analysis of all
intermediate displacement results.
ImageSequence
Registration  Vehicle Detection and Tracking
Movie 1 :
lens corrected sequence 
Movie 2
: registered imagesequence of the road area 
Movie 3 :
cutted the road area 
Movie 4:
background / foreground identification 
Movie 5: Tracked white
car 
Movie 6: Tracked dark car 
Publications
F. Karimi
Nejadasl, R.C. Lindenbergh, Robust Automatic
ImageSequence Stabilization of Road Area, manuscript under preparation
F. Karimi
Nejadasl, R.C. Lindenbergh, Vehicle detection from an
image sequence collected by a hovering helicopter, In Proceedings, WG III/5
Photogrammetric Image Analysis 2011, Germany
F. Karimi
Nejadasl, A System for the Acquisition and Analysis of Image Sequences to Model
Longitudinal Driving Behavior, PhD thesis, 2010
F. Karimi
Nejadasl, B.G.H. Gorte, M. Snellen,
S.P. Hoogendoorn, Evaluating Behavior of Objective
Functions in Optimization based Image Sequence Stabilization in Presence of
Moving Objects, In Proceedings of XXIth ISPRS
Congress, Beijing, July 2008
F. Karimi
Nejadasl, B.G.H. Gorte, S.P. Hoogendoorn,
M.Snellen, Optimization Based Image Registration in
Presence of Moving Objects, In Proceedings of Advanced School for Computing and
Imaging, Heijn, June 2008
F. Karimi
Nejadasl, B.G.H. Gorte, S.P. Hoogendoorn,
M. Snellen, Optimization Based Image
Registration
in Presence of Moving Objects, In Proceedings of International Calibration and
Orientation Workshop, Castelldefels, February 2008
F. Karimi
Nejadasl, B.G.H. Gorte, S.P. Hoogendoorn,
Robust Vehicle Tracking in Video Images Being Taken from a Helicopter, In
Proceedings of ISPRS, Commission VII, Enschede, May
2006
F. Karimi
Nejadasl*, B.G.H. Gorte, S.P. Hoogendoorn,
Optical Flow based Vehicle Tracking Strengthened by Statistical Decisions,
ISPRS Journal of Photogrammetry and Remote Sensing, 61(34):159169, 2006
(*corresponding author)
B.G.H. Gorte, F. Karimi Nejadasl, S.P. Hoogendoorn,
Outline Extraction of a Motorway from Helicopter Image Sequence, In
Proceedings: Stilla U, Rottensteiner
F, Hinz S (Eds) CMRT05.
IAPRS, XXXVI(Part 3/W24):179184, August 2930, 2005
NonRigid
Registration of Stroboscopic Data (Image Series)
Superimposed
the first image and the 10^{th} image 
Will be
presented later 
Sum of
the registered images (only translation) 
Sum of
the registered images (nonrigid registration) 
Future
Publication
F. Karimi
Nejadasl, M. Karuppasamy, A.J. Koster,
R.B.G. Ravelli, Nonrigid image registration of
Stroboscopic CryoElectron Microscopy Data,
manuscript under preparation
Microtubule
Segmentation
Movie 1: tomogram 
Movie 2: detected Microtubules 
Convert
detected MT to point cloud and clean the data 
Skeletonize point cloud (MT) 
Segmented
MT 
CTF
Simulator (GUI)

Written in Matlab. Diplib
library is required for 2D computation and optimization toolbox for first zero
calculation.
Defocus
Estimation
(a) The first image and (b)
the average of 10 images of a stroboscopic image series collected at defocus from the extracellular hemoglobin sample suspended in the
carbon support hole. 
Defocus estimation for an image with
carbon support. The requested defocus value was . (a) Log PS image, (b) FOM image, (c) Qfactor
image. The minima are superimposed by yellow circles: (d) the black curve
with dotted line represents the radial averaged log PS; CTF zeros are marked
with vertical lines. The CTF zeros for the requested defocus value () and for the estimated
defocus are respectively shown in green (dashed) and red (continuous). The 1D
log PS curve, depicted in dark blue, has been smoothed by a Gaussian filter (σ=7) for finding the minima. The same colorcoding is used for
the graphs of the radial averaged FOM (e) and Qfactor (f). (For interpretation of the references to color
in this figure legend, the reader is referred to the web version of this
article.) 
Written in matlab: required diplib,
optimization and statistic toolbox
Publications
F. Karimi
Nejadasl, M. Karuppasamy, A.J. Koster,
R.B.G. Ravelli, Defocus Estimation from Stroboscopic CryoElectron Microscopy Data, Ultramicroscopy,
111(11): 15921598, 2011
Focal
Series
Movie 1: focal series 
Movie 2: Power spectrum of focal series 
·
Simultaneously estimate defoci
and amplitude contrast (LevenbergMarquardt
Algorithm)
·
Using all possible local minima of the radial
and periodogram averaged power spectrum
·
Using linearity constraint between estimated defoci and requested defoci
·
Taking into account uncertainties by giving
lower weight to unbailable defocus
Radial
and periodogram average power spectrum of all focal
series 
Wiener
filtered image resulting from combination of all focal series with estimated
defocus 
Radiation
Damage Studies
Qualitative investigation
of the doserate effect. The aligned and summed images of (a) and (e) lowflux, (b) and (f) mediumflux, (c) and (g) highflux, and (d) and (h) highflux shortexposure
series are shown at two different integrated flux densities of (a)(d) 50 e Å^{2} and (e)(h) 250 e Å^{2}, respectively. The scale bar shown in (a) corresponds to
30 nm 
Plots of radialaveraged
cosine phase error versus resolution for different
dose rates. (a) Radialaveraged FOMs are
given for a mediumflux series on Hb in a lowsalt
sample for integrated flux densities of 50, 100, 150, 200 and
250 e Å^{2}. (b) Closeup of (a) showing the first and second zero crossing of the CTF for a defocus
of 3.37 µm. Radialaveraged FOMs for (c) the lowflux and (d) highflux shortexposure series. 
Fourier ring phase residual
(FRPR) and Fourier ring correlation (FRC) as a function of dose. Mediumflux
data were combined in groups of three images, corresponding to an integrated
flux density of 15 e Å^{2} per combined image. The first combined image was used as a reference. 
Publications
M. Karuppasamy*, F. Karimi Nejadasl*, M. Vulovic,
A.J. Koster, R.B.G. Ravelli,
Radiation damage in singleparticle cryoelectron
microscopy: effects of dose and dose rate, Journal of Synchrotron Radiation,
18(3): 398412, 2011 (*both authors contributed equally)