[Work Log] Misc.

August 26, 2013
Project Tulips
Subproject Data Association v3
Working path projects/​tulips/​trunk/​src/​matlab/​data_association_3
SVN Revision 15229
Unless otherwise noted, all filesystem paths are relative to the "Working path" named above.

Monday IVILAB infrastructure meeting

I'll be organizing the Computational Intelligence seminar this semester.

Will need to

Building efficient curve-tree ML.

Significant rework.

New field: branch_K and branch_mu to store mean and covariance of all points in curve. Prior_k now does not include offset covariance; stored in branch_K. mu_b and Sigma_b now store the branch point means and covariances for all views

fix kernel to not include offset index


Finished implementation. Has bugs.

Start-point has no effect


start_pt = [0 100];
ml = [];
for i = 1:2
    Corrs = attach(Corrs, 2, 1, start_pt(i), 0, params);
    ml(i) = curve_tree_ml_2(Corrs, params, data_);
assert(ml(1) == ml(2));

Fixed - stupid bug in attachment/attach.m - was index offset was hard-coded to zero due to a refactoring mishap.

Optimal Branch point isn't correct


start_pt = [0 10 50 100 1000 5000];
ml = [];
for i = 1:2
    Corrs = attach(Corrs, 2, 1, start_pt(i), 0, params);
    ml(i) = curve_tree_ml_2(Corrs, params, data_);
assert(all(diff(ml) > 0);

Solved - curves were reversed.

Handling reversed curves

Should add a reversed flag, which reverses indices before building likelihood and prior. currently, constructing the likelihood occurs during the "backproject and re-index" phase, in file correspondence/corr_to_likelihood.m. Should refactor likelihood construction into its own function, so we don't have to re-backproject when we don't need to.

Let's review the data-flow so we can see more clearly where everything happens.

Overview: end-to-end curve construction

  1. propose association and attachment
    • By sampling (no code yet)
    • train/labels_from_ground_truth - propose from ground truth.
    • Output: assoc {}
  2. Construct track
    • correspondence/make_correspondence
    • Output: Corrs {}; Corr.ml_2d
  3. Correspondence and triangulation
    • correspondence/build_full_correspondence.m - build from scratch
    • correspondence/merge_correspondence_2.m - merge two pieces
    • Output: corr, means, precisions, cov_error
  4. backproject and estimate curvature
    • correspondence/corr_to_likelihood.m -
    • Output: ll_{means, precisions, indices}, curve_sm*
  5. construct likelihood (flatten, sort, and reverse if needed)
    • correspondence/flatten_sort_and_reverse.m - (doesn't exist yet)
    • Output: ll_{means_flat, precisions_flat, indices_flat, S}
  6. handle attachment recursively
    1. compute conditional prior
      • attachment/att_set_start_index.m
      • Output: start_index, prior_K, prior_indices
    2. compute branch point posterior
      • attachment/att_set_branch_index.m
      • Output: branch_index, mu_b, sigma_b, branch_K, branch_mu
  7. Compute (ML, argmax, etc)

Consider renaming step 4. backproject against rough triangulation; estimate curvature at each point; determine index set. At this point, the order of points don't matter, because the index set hasn't been put to use.

Action: reverse curve - detach?, rerun step 5, 6.1 & 6.2 on self, update branch point & rerun 6.2 for children.

Test: optimize branch point and start index



Posted by Kyle Simek
blog comments powered by Disqus