  Object perception is a fundamental sub-field of Computer Vision, covering a
multitude of individual areas and having contributed high-impact results. While
Machine Learning has been traditionally applied to address related problems,
recent works also seek ways to integrate knowledge engineering in order to
expand the level of intelligence of the visual interpretation of objects, their
properties and their relations with their environment. In this paper, we
attempt a systematic investigation of how knowledge-based methods contribute to
diverse object perception tasks. We review the latest achievements and identify
prominent research directions.
