A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning
This is a review paper, so it is incremental, summarizing existing work without new results.
The paper systematically investigates how knowledge-based methods contribute to diverse object perception tasks in computer vision, reviewing the latest achievements and identifying prominent research directions.
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.