Foam infrared image segmentation method based on NSST saliency detection and image segmentation

Abstract

The invention relates to a foam infrared image segmentation method based on NSST saliency detection and image segmentation, and the method comprises the steps: firstly carrying out the NSST decomposition of a foam infrared image, and obtaining a low-frequency sub-band image and a multi-scale high-frequency sub-band; secondly, performing saliency detection on the low-frequency sub-band image by adopting a GBVS algorithm to obtain a saliency value and a visual saliency map; thirdly, calculating a threshold value and a scale correlation coefficient for each high-frequency direction sub-band coefficient, and removing a noise coefficient, a non-linear enhanced edge coefficient and a weak edge coefficient; and finally, performing image segmentation on the NSST reconstructed image in combinationwith visual saliency to obtain a segmentation result. The method is strong in anti-interference capability and high in segmentation precision.

Description

technical field [0001] The invention relates to the technical field of flotation, in particular to a foam infrared image segmentation method based on NSST saliency detection and graph cut. Background technique [0002] Flotation is the collision and adhesion between the minerals in the flotation machine and the micro-bubbles in the air. Using the hydrophilic and hydrophobic properties of the surface of the minerals and impurities, the highly buoyant mineral particles float up to the foam layer of the liquid surface with the bubbles, so that the target minerals A beneficiation method for sorting ore from ores with complex material composition. Studies have shown that the generation and collapse of bubbles in the flotation process can effectively reflect the mineral content, and image segmentation is the key to flotation image processing analysis and detection of newly generated and collapsed bubbles, and is widely used in computer vision and industrial production. [0003] T...

AI Technical Summary

Problems solved by technology

However, the foam infrared image has the problem of low resolution and contrast, and is easily disturbed by noise, and the high temperature area at the edge of the bubble will affect the segmentation results. At present, the segmentation methods of infrared images mainly include: the region growing segmentation algorithm first performs Gaussian filtering to remove noise, and then Using the region growing algorithm, the segmentation accuracy and speed are improved, but it will cause holes and blurred edges in the segmented area; the improved watershed segmentation method uses area reconstruction transformation to solve the problem of difficult extraction of watershed identification points, but there is a problem of blurred boundaries And the segmentation parameters need to change with the actual situation; the threshold segmentation algorithm extends Otsu threshold segmentation to Otsu multi-threshold segmentation, and introduces the group search random optimization algorithm into multi-threshold segmentation, which improves the threshold search speed and segmentation accuracy, but is not suitable for noise processing and Processing of infrared photovoltaic images with complex backgrounds; K-clustering algorithm segmentation first enhances infrared images based on morphology, and then performs image segmentation by modifying the distance formula used in k-means clustering. The segmented image target outline is clear, But the integrity of the target area is low

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