人工智能
计算机科学
卷积神经网络
模式识别(心理学)
分割
离散小波变换
小波
深度学习
帕斯卡(单位)
小波变换
图像分割
计算机视觉
程序设计语言
出处
期刊:Lecture notes in electrical engineering
日期:2022-01-01
卷期号:: 315-320
被引量:1
标识
DOI:10.1007/978-981-19-0386-1_39
摘要
Deep Learning algorithms based on convolutional neural network (CNN) have achieved huge success in semantic segmentation. However, in these networks, sub-sampling will cause a large loss of image detailed information. In this work, we design a novel method for recovering some of the lost pixels. We use two-dimensional discrete Wavelet Transform (DWT) to extract image boundary detailed information and combine the segmentation result of the convolutional network to recover some of the lost details. We analyze the influence of the algorithm parameters and wavelet function on the final prediction. In our experiments, our algorithm has accuracy improvement compared to the deep network on the PASCAL VOC 2012 dataset.
科研通智能强力驱动
Strongly Powered by AbleSci AI