CogSense: A Cognitively Inspired Framework for Perception Adaptation
This work addresses perception reliability issues in computer vision systems, presenting a novel method for error correction that is incremental in its application of existing logical frameworks.
The paper tackles the problem of perception errors in computer vision systems by proposing CogSense, a cognitively inspired framework that uses probabilistic signal temporal logic to detect errors and adapt perception parameters, resulting in reduced false positives and false negatives.
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.