Simulation of Multi Vision Image Feature Tensor Segmentation Based on Improved U-Net Network
This paper put forward a method of segmenting multi-vision image features based on improved U-Net network.At first,we sorted the gray values in the same window,and then calculated the maximum and minimum val-ues of a pixel point.According to the relationship between angle and pixel,we detected noise points and put the con-taminated noise points into a set.Moreover,we replaced the point with other pixel points,thus completing the filtering.Furthermore,we extracted multi-vision features of the image from color,texture and shape,and thus providing a refer-ence basis for image segmentation.Meanwhile,we constructed a U-Net network including encoder,decoder and jump connection layer.After that,we used the extracted features as network input,and added a deep residual module to the U-Net network.After residual learning,the feature mapping was achieved.In addition,we introduced the attention module to reduce the feature dimension,thus determining the tensor weight.Finally,we used spliced feature dimen-sions by pooling layer,thus outputting the segmented feature tensor.Experimental results show that the proposed method is sensitive to the segmentation of the target and is not prone to over-segmentation and under-segmentation.
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