Extended Visual Memory for Computer-Aided Vision


We propose a computational cognitive Extended Visual Memory (EVM) model for a Computer-Aided Vision (CAV) framework to assist human in vision-related tasks. The CAV framework exploits wearable sensors such as cameras, GPS and ambient computing facilities to empower a user’s vision and memory functions by answering four types of queries central to visual activities. Learning of EVM relies on both frequency-based and attention-driven mechanisms to store view-based visual fragments (VF), which are abstracted into high-level visual schemas (VS), both in the visual long-term memory. During inference, the visual short-term memory plays a key role in the schematic representations of, and the similarity computation between, a visual input and a VF, exemplified from VS when necessary. In this paper, we describe the CAV framework and the new EVM model followed by an implementation scenario on assisted living.

Back to Table of Contents