An approach to the control of multisensor image processing and recognition based on a suitable representation of control knowledge in symbolic form is presented. A hierarchical organization of control knowledge, corresponding to a decomposition of the image recognition process into subprocesses, is proposed. The knowledge for the control of the low-level and high-level phases is described in detail. The control problem involved in the automatic selection and tuning of image processing algorithms is addressed using data structures representing advised sequences of algorithms, a symbolic representation of quality control, and control strategies with backtracking capabilities. Error handling in the high-level phase is faced by a functional decomposition of the error-handling task into error states and types and by a hierarchical representation of the control knowledge for error detection and recovery. Results obtained in a real-world multisensor application are reported, and the improvement in classification accuracy obtained by the proposed error-handling mechanisms is evaluated.
KNOWLEDGE-BASED CONTROL IN MULTISENSOR IMAGE-PROCESSING AND RECOGNITION
ROLI F;
1993-01-01
Abstract
An approach to the control of multisensor image processing and recognition based on a suitable representation of control knowledge in symbolic form is presented. A hierarchical organization of control knowledge, corresponding to a decomposition of the image recognition process into subprocesses, is proposed. The knowledge for the control of the low-level and high-level phases is described in detail. The control problem involved in the automatic selection and tuning of image processing algorithms is addressed using data structures representing advised sequences of algorithms, a symbolic representation of quality control, and control strategies with backtracking capabilities. Error handling in the high-level phase is faced by a functional decomposition of the error-handling task into error states and types and by a hierarchical representation of the control knowledge for error detection and recovery. Results obtained in a real-world multisensor application are reported, and the improvement in classification accuracy obtained by the proposed error-handling mechanisms is evaluated.
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