  This paper proposes the CogSense system, which is inspired by sense-making
cognition and perception in the mammalian brain to perform perception error
detection and perception parameter adaptation using probabilistic signal
temporal logic. As a specific application, a contrast-based perception adaption
method is presented and validated. The proposed method evaluates perception
errors using heterogeneous probe functions computed from the detected objects
and subsequently solves a contrast optimization problem to correct perception
errors. The CogSense probe functions utilize the characteristics of geometry,
dynamics, and detected blob image quality of the objects to develop axioms in a
probabilistic signal temporal logic framework. By evaluating these axioms, we
can formally verify whether the detections are valid or erroneous. Further,
using the CogSense axioms, we generate the probabilistic signal temporal
logic-based constraints to finally solve the contrast-based optimization
problem to reduce false positives and false negatives.
