Image segmentation method, device and equipment and storage medium
An image segmentation and superpixel segmentation technology, applied in the field of computer vision, can solve the problems of complete object recognition, lack of full consideration of relevance, and inability to accurately locate the edge of the picture, so as to improve the prediction accuracy and increase the calculation cost.
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[0027] When the image segmentation model generated based on the full convolutional neural network training is used for image semantic segmentation, the image edge location is not accurate and lacks spatial consistency, so the prediction accuracy of the image segmentation model is not high. The above-mentioned inaccurate image edge positioning and lack of spatial consistency are ultimately due to the fact that the image segmentation model generated based on full convolutional neural network training cannot well recognize the underlying features such as color, brightness, texture, and gradient of the image. Based on the above, it can be considered how to make the image segmentation model generated based on the full convolutional neural network training can accurately identify the underlying features of the picture, thereby improving the prediction accuracy of the image segmentation model.
[0028] In traditional methods, the image semantic segmentation algorithm base...
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