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机译:深度学习方法用于碳素钢显微组织图像的分割
Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 Odisha India;
Natl Inst Technol Dept Met & Mat Sci Rourkela 769008 Odisha India;
neural nets; carbon steel; construction industry; learning (artificial intelligence); metallurgy; steel; image segmentation; deep learning approach; plain carbon steel microstructure images; grade; quality customised; construction industry; transportation; quality; grade; specific heat treatment procedures; specific desired properties; computer-based simulations; metallurgy industry; manual experimentation errors; metal heat treatment processes; digital microstructure images; suitable forms; optimal digital forms; simulation models; raw metal microstructure image; Generative Adversarial Network architecture; steel microstructure image segmentation; authors; GAN model; conventional deep learning models; annotated ground truth segmentation masks; sufficient segmented steel microstructure images; sufficient ground truths generation; segmentation network training; related metal microstructure image processing researches; experiments;
机译:钢显微组织图像的处理和优化,以协助普通碳素钢的计算机热处理
机译:通过河流相机图像深度学习自动化河流监控:一种基于水分割和转移学习的方法
机译:使用优化的图像配准和深度学习分割方法在UAV MultiSpectral图像中的葡萄疾病检测
机译:2D OCT图像中甲状腺微观结构分割的深度学习
机译:基于深度学习和颜色量化的组织学图像的核细胞分割和现实世界丸图像分析
机译:深度学习对高级钢铁STEM图像中的缺陷进行语义分割
机译:2D OCT图像中甲状腺微观结构分割的深度学习