Adaptive Locally Aligned Ant Technique for Manifold Detection and Denoising

The detection and extraction of noisy manifolds from data have various applications.In Astronomy, the detection of faint streams and filaments is particularly difficult due to background contamination, which immerses and hides them in noise.The biologically inspired Locally Aligned Ant Technique (LAAT) has been demonstrated as an efficient and flexible algorithm to detect and denoise versatile structures within noisy backgrounds.Our contribution extends LAAT two-fold: (1) introduction of a dynamic local radius, and (2) locally variable pheromone deposition.The former avoids highlighting spurious patterns in noisy regions and allows smaller jumps in areas with strong alignment.The latter increases pheromone deposition in fainter zones.We demonstrate this in 2 datasets.