Natural-like technosignatures candidates may represent a detection
problem for both artificial systems and humans. We tested traditional
computer vision models with natural formations with special
characteristics, Ahuna Mons region in Ceres in this particular case. We
looked if these artificial models may represent a trustful aid to human
detection and identification of potential technosignatures in planetary
surfaces. Ahuna Mons is a 4km particular geologic feature on the surface
of Ceres of possibly cryovolcanic origin. The special characteristics of
Ahuna Mons are also interesting in regard of its surrounding area,
especially for the big crater besides. This crater possesses
similarities with Ahuna Mons including diameter, age, morphology, etc.
Under the cognitive psychology perspective and using current computer
vision models we analyzed these two features on Ceres for comparison and
pattern recognition similarities. Several algorithms were employed
avoiding human cognitive bias. 3D analysis from images of both features
characteristics are discussed. Results showed positive results for these
algorithms about similarities of both features. Discussion is provided
about implications of this pilot computer vision techniques experiment
for Ahuna Mons and the potential cognitive bias problem of both human
and Artificial Intelligence models and the risks for the search of
technosignatures.