imagesegmentation is a key step in the process of image data processing. The quality of imagesegmentation will directly affect the accuracy of image cognitive understanding. The purpose of imagesegmentation is to d...
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imagesegmentation is a key step in the process of image data processing. The quality of imagesegmentation will directly affect the accuracy of image cognitive understanding. The purpose of imagesegmentation is to divide the image into regions with specific semantics. For the simple linear iterative clustering (SLIC) algorithm, the feature equalisation parameters need to be set manually during imagesegmentation, which results in the lack of segmentationeffects and slow processing time. By introducing the theory of intermediary mathematics, an improved adaptive SLIC super-pixel algorithm is proposed, which can adaptive generate characteristic equalisation parameters according to the specific situation of the image, thereby simplifying the operation steps and improving the image segmentation effect. After experimental verification and analysis, compared with the original SLIC algorithm and several other super-pixel contrast algorithms, the algorithm in this study can effectively shorten the processing time and achieve a better segmentationeffect.
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