Range characterizations are established for the k-weighted conical Radon transform and Compton transform by factoring into divergent beam and spherical section transforms and combining with prior consistency conditions.
IEEE transactions on image processing , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
citing papers explorer
-
Range characterization of the weighted divergent beam and cone integral transforms
Range characterizations are established for the k-weighted conical Radon transform and Compton transform by factoring into divergent beam and spherical section transforms and combining with prior consistency conditions.
-
Are Candidate Models Really Needed for Active Learning?
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.