[Work Log] Re-run training, Re-reconstruction, Curve-Flipping

August 11, 2013
Project Tulips
Subproject Data Association v2
Working path projects/​tulips/​trunk/​src/​matlab/​data_association_2
Unless otherwise noted, all filesystem paths are relative to the "Working path" named above.


Re-ran training after several bug-fixes.

New Files:


All results generated by exp_2013_08_11_train_all.m.

No-perturb model

        smoothing_variance: 0.0024
            noise_variance: 1.2308
         position_variance: 1.6072e+04
             rate_variance: 0.2743
perturb_smoothing_variance: 1
     perturb_rate_variance: 1
 perturb_position_variance: 1
             perturb_scale: 2.5000

Ind-perturb model

        smoothing_variance: 0.0019
            noise_variance: 0.7192
         position_variance: 1.6132e+04
             rate_variance: 0.2451
perturb_smoothing_variance: 7.1854e-19
     perturb_rate_variance: 1.1292e-06
 perturb_position_variance: 0.4849
             perturb_scale: 2.5000

OU-perturb model

        smoothing_variance: 0.0019
            noise_variance: 0.7204
         position_variance: 1.6111e+04
             rate_variance: 0.2465
perturb_smoothing_variance: 7.1854e-19
     perturb_rate_variance: 1.1296e-06
 perturb_position_variance: 0.5931
             perturb_scale: 2.4654

SqExp-perturb model

        smoothing_variance: 0.0018
            noise_variance: 0.7207
         position_variance: 1.6117e+04
             rate_variance: 0.2480
perturb_smoothing_variance: 7.1854e-19
     perturb_rate_variance: 1.1355e-06
 perturb_position_variance: 0.5172
             perturb_scale: 0.9202

Surprised to see that noise-variance only changed by a factor of 10, not 100. However, the resulting noise_variance is right in the range that you'd expect arising from pixel-grid rasterization error.

OU perturb-scale is lower than in the last case, and perturb position and rate variance is lower, too.

SqExp perturb-scale is higher than in the last case, and perturb rate variance is lower.

Lower position and rate variance makes sense after correcting curve-reversals.

However, since we trimmed the pre-tails, a higher global and perturb position variance should result. The result we're seeing is a combination of these competing effects.


Some curves are flipped; need to an approach that will detect and correct flipped curves.

Images and javascript generated by ../experiments/exp_2013_08_11_reconstruct_for_web.m

Ind-perturb model

OO-perturb model

SqExp-perturb model

Detecting and Flipping Curves

Experiment: ../experiments/exp_2013_08_11_flip_curves.m

Result: doesn't really work. Lots of false negatives.

Algorithm output: 2 4 5 6 8 10 12 Ground Truth: 1 2 4 5 6 8 9 10 11 12 14 15

Not really sure why this is failing. After flipping, most of these curves are closer to the origin, which is promoted by position_variance. And any tip-perturbations should be better modelled after flipping.


Posted by Kyle Simek
blog comments powered by Disqus