Digital Image Processing
Projects Aut 2015/16
Projects Spr 2014/15
Projects Spr 2013/14
Projects Win 2013/14
Projects Aut 2013/14
Projects Spr 2012/13
Projects Spr 2011/12
Projects Spr 2010/11
Projects Spr 2009/10
Projects Spr 2007/08
Projects Spr 2006/07
Projects Spr 2005/06
Projects Spr 2003/04
Projects Spr 2002/03
Final Project for Spring 2006-2007
The project should be done individually or in groups of up to 3 people and should require about 50 hours per person. Each group will develop and implement their own algorithm to identify a painting from an image captured by a cell-phone camera.
The project should be done individually or in groups of up to 3 people and should require about 50 hours per person. Each group will develop and implement their own algorithm. The task of the project is to develop a technique which recognizes paintings on display in the Cantor Arts Center based on snapshots taken with a camera-phone. Such a scheme would be useful as part of an electronic museum guide; the user would point his camera-phone at a painting of interest and would hear commentary based on the recognition result. Applications of this kind are usually referred to as "augmented reality" applications. Implemented on hand-held mobile devices, they are called "mobile augmented reality." We are interested in the image processing part of the problem.
Many different image processing techniques may succeed in recognizing one painting from a relatively small set. You may use any algorithm that you learned in class, as well as algorithms that we have not discussed. However, solutions will probably require a combination of several schemes.
You will design and test your algorithm with a set of training images and the corresponding ground truth data that can be downloaded from the Downloads section. After you submit your algorithm implementation, we will check its performance with a set of test images, which are not in the training set. Each test image shows one of the paintings in the training set, but taken at a different time and from a different vantage point. You may assume that the variations among the training images are typical.
Painting Photographs Details:
The photographs in the training and test sets were all taken in the European Gallery of the Cantor Arts Center. At least four photos were taken of each of 33 paintings. The photos were taken with a Nokia N93. Three photos of each painting have been randomly selected from the database for you to use as training images. The museum environment presents several challenges due to low-light conditions. The images are quite noisy, and many have motion blur and/or defocus. Nevertheless, the 3 megapixel images provide a relatively high resolution for cell-phone photos.
Training Images and Ground Truth Data
Ground Truth: ground_truth.m
Group registration: 11:59 PM, May 12
(1) Please mail to email@example.com (do not c.c. to TA's or Professor's personal e-mail address).
(2) For program, please include ONLY nessessary files in ONE ZIP file named "ee368groupXX.zip" and email it with subject "ee368 groupXX program". Your 2 digit group number XX is listed below.
(3) For report, please prepare in PDF
format, name it "ee368groupXX.pdf" and email it with subject "ee368 groupXX
Your 2 digit group number XX is listed below.
(4) Note that your code will be run on the SCIEN machines by the teaching staff. Hence, it is a good idea to get an account there and make sure that there are no platform-specific problems with your code.
(5) If you are a group, then you need to submit the log of who did what with your report.
Group 01: Prashant Goyal, Vikram Srivastava