Offshore scene significance detection method

Abstract

The invention discloses an offshore scene significance detection method. The method comprises the following steps of: 1, extracting an offshore scene image sequence; 2, transferring each frame image to the CIELab colour space, and extracting the characteristic pattern of luminance and colour passages; 3, using the absolute value of the difference between the extracted characteristics and the global mid value as the global significance map; 4, using the absolute value of the difference between the characteristics and the local mean value filtration as the local significance map; 5, combining the global significance map and the local significance map of the characteristics to obtain an overall significance map; 6, linearly combining the significance maps of the colour passages of frame images, and respectively combining the combined significance maps with the luminance significance maps to form an overall significance map; 7, accumulating by using each frame detection result as the centre and modifying the significance map of the current frame; and 8, converting the overall significance map into a binarization image to obtain an offshore scene significance target area. By adopting the method, the significance area in the offshore scene can be extracted rapidly, and the interference of sea noise wave can be favorably inhibited. The method is simple in implementation, and is suitable for real-time application.

Description

technical field [0001] The invention relates to a detection technology in the field of machine vision and image processing, in particular to a method for detecting the saliency of a marine scene using image processing and machine vision technology. Background technique [0002] At present, most of the visual attention computing models at home and abroad directly perform saliency detection in the spatial domain of images and extract saliency maps. Compared with the idea of ​​using the image spectrum, this type of method does not need to perform orthogonal transformations such as Fourier transform or discrete cosine transform on the image. [0003] The core of saliency detection methods based on spatial domain is how to define saliency. Various methods currently proposed mainly use the feature difference between pixels in the spatial domain to measure saliency, and commonly used features include brightness, color, direction, texture, etc. At the same time, many scholars have...

AI Technical Summary

Problems solved by technology

Due to the large amount of sea clutter and the fact that most of the sea targets are small targets in the sea scene, the effect of the existing methods is not ideal.

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