Trabajo final
Trabajo final
Requisitos de finalización
(a) README
(b) firstname-lastname-project-UDeLaR-2018.pdf
(c) lrmc.m, rpca_admm.m, spectral_clustering.m,
The TA will run your scripts to generate the results. Thus, your script should include all needed plotting commands so that figures pop up automatically. Please make sure that the figure numbers match those you describe in firstname-lastname-project-UDeLaR-2018.pdf. You do not need to submit input or output images. The output images should be automatically generated by your scripts so that the TA can see the results by just running the scripts. In writing your code, you should assume that the TA will place the input images in the directory that is relevant to the question solved by your script. Also, make sure to comment your code properly.
Apertura: sábado, 12 de mayo de 2018, 00:00
Cierre: martes, 31 de julio de 2018, 23:55
ASSIGNMENT
Please complete either the 6 analytic exercises, or the 2 coding exercises, or a combination of the two that adds up to 100 points by July 31, 2018.
Please upload a file called "firstname-lastname-project-UDeLaR-2018.zip”
For analytical questions, please attach a file called ""firstname-lastname-project-UDeLaR-2018.zip”” containing your answers to each one of the analytical questions. If at all possible, you should generate this file using latex. If not possible, you can scan your handwritten solutions. But note that you must submit a single PDF file with all your answers.
For coding questions, please attach a file called "firstname-lastname-project-UDeLaR-2018.zip” containing a file called README with instructions on how to run your code as well as all your source files. Please use separate directories for each coding problem. Each directory should contain all the functions and scripts you are asked to write in separate files. For example, the structure of what you should submit could look like
(a) README
(b) firstname-lastname-project-
(c) lrmc.m, rpca_admm.m, spectral_clustering.m,
The TA will run your scripts to generate the results. Thus, your script should include all needed plotting commands so that figures pop up automatically. Please make sure that the figure numbers match those you describe in firstname-
ANALYTIC EXERCISES
Exercise 2.7 (10 points)
Exercise 3.1 (15 points)
Exercise 3.5 (15 points)
Exercise 3.7 (20 points)
Exercise 3.8 (20 points)
Exercise 8.1 (20 points)
CODING EXERCISES
(50 points) Implement Algorithm 3.2 (LRMC) and Algorithm 3.8 (RPCA_ADMM) using the format described in Exercises 3.9 and 3.10. Apply the LRMC algorithm to the face dataset available at http://www.vision.jhu.edu/teaching/learning/data/YaleB-Dataset.zip as described in Exercise 2 of http://www.vision.jhu.edu/teaching/learning/learning17/Project/project1-cachan17.pdf. Apply RPCA_ADMM to the same dataset, except that instead of removing entries from the images you will corrupt the entries.
(50 points) Implement the complete SSC algorithm, i.e., implement Algorithms 4.5, 8.5 and 8.6. Apply SSC to frontal faces from three individuals under 64 illumination conditions in the face dataset available at http://www.vision.jhu.edu/teaching/learning/data/YaleB-Dataset.zip . Plot the affinity matrix, the resulting segmentation, as well as the clustering error.
Rene Vidal, Professor
Center for Imaging Science
Department of Biomedical Engineering
Johns Hopkins University
Cal: www.cis.jhu.edu/~rvidal/calendar .html