Progress Report: Week 8

Alvaro Velasquez

Framework

Create data matrix of all non-overlapping 4x4x4 cuboids in image volume.

Learn dictionary for the data matrix using KSVD.

Obtain sparse coefficient matrix using graph regularization.

Cluster coefficient matrix using K-means. Display clusters on image volume for

segmentation.

Things Tried this Week

Append all three RGB channels to vectorized cuboids as opposed to using gray-scale

Use Pearson correlation for the distance measure when clustering as opposed to Euclidean distance.

Choose initial centroid locations when clustering as opposed to choosing random locations.

Use Max-Voting for initial centroid locations. Perform Quick-shift as a preprocessing step.

Last Week's Segmentation

Choosing Initial Centroid Locations

Centroid Locations Through Max-Voting

Work for this Week

Add Gaussian smoothing as a pre-processing step.

Make sparsity rate larger. Try different dictionary sizes (We have tried

1000 and 2000 atom dictionaries).