Imaging Science and Technology – Final Project

Course grade is based on the final project.
Project submission is in 3 stages + presentation:

Preliminary submission due April 11, 2011.
Mid-submission due May 16, 2011.
Class Presentation on May 30 or June 6, 2011.
Final submission due June 26, 2011.

The project will include, Project summary, code, demos, documentation as well as a web page explaining your
project its goal and presenting some of the results (see example)..

Preliminary Submission includes Web Page (outline) + Summary of appropriate papers.
Mid-submission includes Web page complete with preliminary results.
Final submission includes – Everything! (Complete running program
and examples, user guide and web page with final results).

Project grade distribution can be found here.

Projects can be written in Matlab or as a Java applet. All programs must be stand-alone (e.g. use standard functions supported by Matlab
and not toolbox functions) (Java programs – include in submission all required downloads and libraries).

Your program should be fully interactive and GUI assisted (e.g. using MATLAB's guide), e.g. parameters should be controlled by sliders, etc.
Part of the grade will be given for usability i.e. how easy it is to manage the program (user-friendly).


Project submission should include:
4 directories:
Code –all code files, include libraries, scripts, example runs etc.
Code should be well documented (head of each file as well as within the code).
Include a readme file describing the layout of the code (Modules, Functions etc).
Docs – Project summary describing the project, goal, methods, results.
Include a short User-Guide should be associated with the program explaining how to use the program.
Supply at least one example with working input and output and parameters used.
Data – input data for the program. May include input images, example images or other input data.
Web – Directory including all files needed for the webpage. Should be stand alone - i.e. include all link files and images used
in the webpages.

Questions can be sent to me by email at hagit@cs.haifa.ac.il.

Project #1: Multi-Image Color Correction
In this project you have to correct colors of several images that have been mosiaced together. Create 2 options:
a) align all image colors globally. b) align colors locally creating a slowly varying color tone across the mosaic.
The mosaicing code is given, you will have to add to this code.

Project #2: Color Matching Application – on non-uniform background
Create an application/applet that performs color matching with non-uniform background (e.g. with sinusoidal background of varying frequency).
Create a Modulation Transfer Function for colors dependent on frequency and color of background pattern
based on data from the color-matching experiment.

Project #3: Color Correction of Images Based on Faces (and/or other objects)
Color Correction from Millions of images….

Perform color correction (white balance) of an image based on objects in the image.
Based on the paper below.

1) Based on faces: Search for faces in image. Estimate color deviation from mean face color.
(Take mean to be over many).

2) Extend to other objects. Be careful - when looking for average of objects consider that some objects
are highly distributed (e.g. cars) look at the color variance in the examples. If variance
is large and distributed, the estimate of deviation will be poor.

Try to do this: increase patch/seg size till find region that as lower variance
(e.g. for cars could be that the windshields/tires/bumpers have less deviation and are always in the image!

Object Based Illumination Classification, Hel-Or and Wandell, PR 2002
http://cs.haifa.ac.il/hagit/papers/PR02-HelOrWandell-IlluminationClassification.pdf

Project #4: Painting Faces Using Color Landmarks

Determine "optimal" painting points on faces. Given a BW image of a face determine the "magic" points
In the face for which color info must be given so that image painting will be as good as possible.

Recently, several methods for image colorization have been developed (coloring BW images).
These typically require input of selected points in the image and their associated color. The rest of the
image is then painted according to these points. In their paper, Huang and Chen studied how to determine
which are the best points to give as input. These are called landmarks.
In this project, try to implement their work on face images. Determine whether there are "magic" points
in faces that should always be used as landmarks for coloring. How many landmarks are needed?
Does this vary for faces with glasses, makeup, facial hair etc?

Image painting code must be written (possibly is available).
Face alignment must be performed.

"Landmark-Based Sparse Color Representations for Color Transfer", Huang and Chen, ICCV2009
http://www.cse.wustl.edu/~mgeorg/readPapers/byVenue/iccv2009/huang2009_iccv_landmarkSparseColorForColorTransfer.pdf

Project #5: Detecting Camera source of an image.
Determine noise patterns of 2 or more cameras and build an automatic classifier that determines for a given image
with which camera it was acquired.

Detecting Digital Image Forgeries Using Sensor Pattern Noise, with J. Lukas and M. Goljan, Proc. of SPIE

Electronic Imaging, Photonics West, January 2006
http://www.ws.binghamton.edu/fridrich/Research/LukFriSPIE06_v9.pdf

Digital Camera Identification from Sensor Noise, with J. Lukas and M. Goljan, IEEE Transactions on

Information Security and Forensics, vol. 1(2), pp. 205-214, June 2006
http://www.ws.binghamton.edu/fridrich/Research/double.pdf

You can also use: noise characterization of a digital camera
http://scien.stanford.edu/class/psych221/projects/05/gregng/index.html

Project #6: Multi-Spectral Imaging
Map a Multispectral image to 3D (RGB) such that color distances are preserved as best as possible. Also constrain the mapping
so that colors are as similar to original as possible.
Must look for multispectral source images (if camera – even better!).

Project #7: Watermarking Color Halftone Images
Watermarking (inserting a secret code in an image) has been applied to halftoned images.
Implement watermarking in halftoned color images. Use the Barycentric Screening approach
together with the grayscale watermarking technique. Both encoding and decoding must be
implemented.

"Barycentric Screening", Nur Arad, Doron Shaked, Zachi Baharav, HP Laboratories Israel
http://www.hpl.hp.com/techreports/97/HPL-97-103R1.pdf

"Copyright Labeling of Printed Images", H.Z.Hel-Or, ICIP 2000
http://cs.haifa.ac.il/hagit/papers/CONF/ICIP00-HelOr-CopyrightLabelingPrintedImages.pdf

Project #8: Creating Color Anaglyphs using red-green glasses
An Anaglyph is a pair of images superimposed on a single image and typically viewed
with special glasses that allows one image to be viewed per eye. Glasses may be color
filters or polarizes. Typically Anaglyphs are used to create 3D stereo images.
Problems when creating anaglyphs are 1) due to filtering, there is significant loss of
color 2) there are colors that map to the same values causing region merging
3) due to the non-independence of the filters, there is cross-talk (when one eye sees
what the other is supposed to see).

In this project, you are given 2 color images and the goal is to create anaglyph that has
minimal crossover and maximal colorfulness. This is of course dependent on the filters
of the glasses.
The project involves: 1) designing an interactive mechanism to model the given
filters in the glasses, and to determine the color subspace spanned by each filter.
2) Determine the optimal mapping of the images to anaglyph based on these glasses
(e.g. using the papers below).
3) Design a color mapping of the images so that their anaglyph mapping becomes more
efficient.

Methods for computing color anaglyphs, David F. McAllister, Ya Zhou, Sophia Sullivan, EI2010
http://research.csc.ncsu.edu/stereographics/ei10.pdf

A Uniform Metric for Anaglyph Calculation, Zhe Zhang and David F. McAllister, EI2006
http://research.csc.ncsu.edu/stereographics/ei06.pdf

Producing Anaglyphs from Synthetic Images, William Sanders, David F. McAllister, EI2003
http://research.csc.ncsu.edu/stereographics/ei03.pdf

See also http://www.3dtv.at/knowhow/anaglyphcomparison_en.aspx

Project #9: Make it disappear! – Experiment with Projector and Camera

Projector camera interaction.
Place a poster with various letterings in view of the camera and under the projector lighting.
Control the projector lighting so that one of the objects disappears.
Objects should be flat against the poster (otherwise shadows will interfere). Make sure the objects and poster are colored so that
projector can actually project enough light to change appearance.

Project #10: Just Noticable Difference – Experiment and Perceptual Color Mapping
Create a Just Noticable Difference - Experiment applet. Test for non uniformity of JND in different color regions, in different color directions.
Map the results per person. Since must run on RGB machine, might be more effective to use scale and not JND, i.e. define several
'distances' by example pairs and then ask user to rate distances. Given these distances, map colors to 2d (3d) color space.
Might require calibration of monitor.

Project #11: S-CIELAB

Implement S-CIELAB in JAVA. Ask users to rate perceptual distances between photos/colors. Then compare rating
with SCIELAB CIELAB and RGB values. Use images such as stripes, patch on const background as well as other images. 

Project #12: LCD and CRT Display Calibration

Build a calibration tool that measures color patches on LCD/CRT displays (using photometer) and
builds the gamma curves of the display as well as the forward and backward transformations between RGB and XYZ space.
Can use modules and code supplied by EyeOne (photometer). 

NOT OFFERED THIS YEAR

Project #3: Shadow Removal From Video Sequences
In this project you will remove shadows form video sequences. The shadows may move between video frames and may be on textured surfaces.
Implement the Arbel approach on a per-frame basis. Code for shadow removal in single frames is given.

Project #4: Color Lines – Interactive change of color of image objects
Color Lines - an interactive applet that takes an image, represents it using color lines then allows user to click on object and
with 2 interactive sliders, change its color. Based on : http://www.cs.huji.ac.il/~werman/Papers/colorLines04.pdf

Project #7: Background Subtraction

The project will detect moving forground objects by background subtraction and change detection in a sequence of video frames. A background model is created using Mixture of Gaussians. The background is automatically learnt from a predefined number of frames in the image. Once the background is learnt, each video frame is subtracted from the background model and thresholded
to obtain the foreground objects. Clean up of the foreground objects is expected.

Sources:
C. Stauffer and W. Grimson, "Learning Patterns of Activity Using Real-Time Tracking ", IEEE TPAMI, 22(8):747–757, 2000.
Al-Mazeed, A. H., Nixon, M. S. and Gunn, S. R. (2003)
"Fusing Complementary Operators to Enhance Foreground/Background Segmentation".
In: British Machine Vision Conference 2003, 2003, Norwich.

Project #10: Recoloring of BW Images

Given a BW image – color it. Must learn priors, for example by example images or by category.

Project #12: Compare different Demosaicing techniques.

Implement and compare 5-6 different demosiacing techniques. Analyze quality of demosaicing using frequency and orientation varying test pattern.

Project #13: Affective imaging - color balancing.

Color balance an image to affect the emotional percept of the image.

Project #14: Transparency and color images.

Overlaying 2 color images as transparent images often produce a scene that is not separable into the two original scenes.
This is due to masking of one image over the other. This project will study transparency masking and suggest a method that can automatically
recolor images so that masking in their transparent combination is minimized.
Will require a reference search, experimentation and coding.

Project #15: Course Scripting.

Write Matlab scripts (similar to scripts for chapters 2-4) for demonstrating various aspects taught in the course on the topic of
Display, Printers, Scanners. 

Project #16: Color blind images

Simulate what color blind people will see in an RGB image. Correct (DALTONIZE) so that they see better.
See VISCHECK http://www.vischeck.com