Hi, thank you for your work. I ran the code on the Adelaide dataset for the estimation of homography matrices. However, the fitting results obtained from my own are different from yours on the paper (Fast L1-NMF for Multiple Parametric Model Estimation). I got some strange results as followed:
barrsmith (235, 6)
Preference matrix size: (235, 1425)
Running regular bi-clustering
Time: 8.2033700943
Inlier sets size: [235]
GNMI: 0.0000; Precision: nan; Recall: 0.3333; ME: 0.3021;
Running compressed bi-clustering
Time: 3.55659222603
Inlier sets size: [235]
GNMI: 0.0000; Precision: nan; Recall: 0.3333; ME: 0.3021;
Comparing both solutions:
GNMI: 0.0000; Precision: nan; Recall: nan; ME: 0.0000;
And I obtained the mean misclassification error among 19 pairs of images is up to 40%. Note that I can obtain normal fitting results on the estimation of fundamental matrices.
Looking forward to your reply. Thanks.
Hi, thank you for your work. I ran the code on the Adelaide dataset for the estimation of homography matrices. However, the fitting results obtained from my own are different from yours on the paper (Fast L1-NMF for Multiple Parametric Model Estimation). I got some strange results as followed:
barrsmith (235, 6)
Preference matrix size: (235, 1425)
Running regular bi-clustering
Time: 8.2033700943
Inlier sets size: [235]
GNMI: 0.0000; Precision: nan; Recall: 0.3333; ME: 0.3021;
Running compressed bi-clustering
Time: 3.55659222603
Inlier sets size: [235]
GNMI: 0.0000; Precision: nan; Recall: 0.3333; ME: 0.3021;
Comparing both solutions:
GNMI: 0.0000; Precision: nan; Recall: nan; ME: 0.0000;
And I obtained the mean misclassification error among 19 pairs of images is up to 40%. Note that I can obtain normal fitting results on the estimation of fundamental matrices.
Looking forward to your reply. Thanks.