This paper presents a novel optimization framework for the design of fuzzy partitions specifically tailored to data acquired from visual sensors operating in complex environments.

Visual sensor streams are typically high-dimensional and affected by uncertainty and vagueness arising from illumination changes, occlusions, and sensor noise, which makes their processing within a Fuzzy Logic System (FLS) particularly appealing.

In such systems, the overall performance, robustness, and interpretability crucially depend on the choice of the membership functions that define the fuzzy partition of the input space.

We address this design problem by introducing an automatic tuning scheme for the parameters of parametric membership functions, focusing on triangular and Gaussian shapes, whose centers and widths are optimized over a prescribed spectral range.

Conceptually, our approach is inspired by the view of biological sensory systems as collections of specialized fuzzifiers, where stimuli are encoded through families of overlapping receptive fields and processed via fuzzy-granular representations.

In particular, the human visual system exemplifies how a limited number of receptor types can support fine discrimination by means of suitably arranged fuzzy membership functions over the sensory domain.

Preliminary analytical evaluations of the proposed objective show that, for both triangular and Gaussian memberships, the sensitivity term admits closed-form expressions, which allow an efficient numerical implementation of the optimization procedure.

Moreover, the explicit characterization of under-threshold zones through a Marzullo-like algorithm provides a controllable mechanism to tune coverage according to application-dependent sensitivity requirements.

These first results support Gentili's thesis that perception-oriented fuzzy structures can serve as a conceptual blueprint for artificial sensory systems.