Adaptive Sampling by Histogram Equalization
Development of an adaptive sampling method that efficiently varies the sampling rate in local regions of a function based on the distribution of already collected samples. The algorithm does not rely on gradients in the parameter space and therefore allows to create accurate representation with less collected or computed sample points. In cases where he acquisition of samples is expensive, like computer simulations or experiments, ASHE has the advantage of requiring significantly less data points.
References
- Fadiran, O. O. & Molnár, P. (2006). Maximizing diversity in synthesized hyperspectral images. Targets and Backgrounds XII: Characterization and Representation, SPIE, 6239, 11--22.
- Fadiran, O. O., Molnár, P., & Kaplan, L. M. (2006). Adaptive sampling via histogram equalization using an active walker model. 5th IEEE/ACIS International Conference on Computer and Information Science and 1st IEEE/ACIS International Workshop on Component-Based Software Engineering, Software Architecture and Reuse (ICIS-COMSAR'06), IEEE, 424--432.