Abstract

Cognitive Vision Systems have gained significant attention from academia and industry during the past few decades. One of the main reasons behind this interest is the potential of such technologies to revolutionize human life since they intend to work robustly under complex visual scenes (which environmental conditions may vary), adapting to a comprehensive range of unforeseen changes, and exhibiting prospective behavior. The combination of these properties supports the creation of more intelligent and efficient environments by mimicking the human capabilities. Nonetheless, preserving the environment involves gathering visual and other sensorial information and translating it into knowledge to be useful, which still remains a challenge for real time cognitive vision applications due to the complexity of such process. Experts believe the starting point is to establish a knowledge representation for Cognitive Vision technologies as a unique standard that could integrate image/video modularization and virtualization, together with information from other sources (wearable sensors, machine signals, context, etc.) and capture its knowledge. In this chapter, we present a multi-domain knowledge structure based on experience, which can be used as a comprehensive embedded knowledge representation for Cognitive Vision System, addressing the representation of visual content issue and facilitating its reuse. In addition, a successful representation and management of knowledge in cognitive systems would support communication and collaboration between humans and machines, for increased learning capabilities and enhanced decision making; this concept is a pathway towards what is called Augmented Intelligence. The implementation of such representation has been tested in a Cognitive Vision Platform for Hazard Control (CVP-HC) to address the issue of workers’ exposure to risks in industrial environments, in special for the non-use of personal protective equipment, facilitating knowledge engineering processes through a flexible and adaptable implementation.

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Authors (3)