Abstract
The infinite variety of image subjects, the dependence of analysis algorithms and decision rules on the shooting conditions and image quality lead to the need to configure and retrain the computer vision system for almost every next series of images. The paper proposes the principles of organization and high-level language for description of strategies of content-based analysis of aerospace images. The decision maker specifies the strategy for processing and analyzing the image as a sequence of points of selection of actions or subtasks. In general, each action can be performed by different software modules, which require their own data structures, restrictions and rules. Accordingly, the results of the action will vary. The solver, which is controlled by the given strategy, selects variants of actions, data and constraints for each subtask, builds a decision tree, and monitors the progress of the decision. Examples of object detection strategies and results of their work on urban area aerial images characterized by a very high spatial resolution are given. Applied semantic models of actions and resources make the process of structuring and describing more visual and, at the same time, machine-readable. The process of describing the image analysis strategy is transferred from the level of specifying instructions/commands to the level of planning works and resources.
This work is supported by the Russian Science Foundation under grant No. 18-71-00109.
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Kasimov, D.R., Kuchuganov, V.N., Kuchuganov, A.V. (2020). Principles of Organization of the Strategies of Content-Based Analysis of Aerospace Images. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_30
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