Window fusion method based on heat diffusion theory

Abstract

The invention provides a window fusion method based on the heat diffusion theory. The method includes the steps that each initial detection window corresponds to one position in a thermodynamic system; a heat conduction coefficient between positions, corresponding to any two initial detection windows, in the thermodynamic system is calculated; a linear anisotropy heat diffusion equation which is in a discrete version under a steady state is established by using of the heat conduction coefficient between any two positions; K positions are selected in the system as heat sources to enable the temperature sum of the whole system to reach the maximum under the condition of heat diffusion; the repeated initial detection windows are eliminated through a non-maxima suppression method because duplicate detection of the corresponding K detection windows still possibly exists after the K positions of the heat resources are determined. According to the window fusion method based on the heat diffusion theory, the method of maximizing the temperature sum of the system under the condition of linear anisotropy heat diffusion is utilized for simulating an objective law of the initial detection windows, and therefore precision of target detection can be greatly improved.

Description

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AI Technical Summary

Benefits of technology

In this new method described by our inventors, we cannot just remove duplicate or incorrect detects without affecting other targets being detected. Instead, these techniques help identify if there are hidden threats between them while still maintaining their accuracy.

Problems solved by technology

In summary, technical problem addressed in this patents relates to improving the accuracy of finding objects from images through algorithms like sliding Window Interval Methods. However existing techniques such as non-optimization methods often produce results where some parts get overlapped due to their own characteristics.

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