A Cognitive Vision System for Nuclear Fusion Device Monitoring

International audienceWe propose a cognitive vision-based system for the intelligent monitoring of tokamaks during plasma operation, based on multi-sensor data analysis and symbolic reasoning. The practical purpose is to detect and characterize in real time abnormal events such as hot spots measured through infrared images of the in-vessel components in order to take adequate decisions. Our system is made intelligent by the use of a priori knowledge of both contextual and perceptual information for ontology-driven event modeling and task-oriented event recognition. The system is made original by combining both physics-based and perceptual information during the recognition process. Real time reasoning is achieved thanks to task-level software optimizations. The framework is generic and can be easily adapted to different fusion device environments. This paper presents the developed system and its achievements on real data of the Tore Supra tokamak imaging system

Approaches and Challenges for Cognitive Vision Systems

Abstract. A cognitive visual system is generally intended to work robustly under varying environmental conditions, adapt to a broad range of unforeseen changes, and even exhibit prospective behavior like systematically anticipating possible visual events. These properties are unquestionably out of reach of currently available solutions. To analyze the reasons underlying this failure, in this paper we develop the idea of a vision system that flexibly controls the order and the accessibility of visual processes during operation. Vision is hereby understood as the dynamic process of selective adaptation of visual parameters and modules as a function of underlying goals or intentions. This perspective requires a specific architectural organization, since vision is then a continuous balance between the sensory stimulation and internally generated information. Furthermore, the consideration of intrinsic resource limitations and their organization by means of an appropriate control substrate become a centerpiece for the creation of truly cognitive vision systems. We outline the main concepts that are required for the development of such systems, and discuss modern approaches to a few selected vision subproblems like image segmentation, item tracking and visual object classification from the perspective of their integration and recruitment into a cognitive vision system