Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

Ma, Boyuan; Ban, Xiaojuan; Huang, Haiyou; Yu-lian, Chen; Liu, Wanbo; Zhi, Yonghong

doi:10.3390/sym10040107

Cited by 78 publications

(41 citation statements)

References 27 publications

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“…The FCN-based image segmentation can be divided into two simple phases. In the first phase, each input image passes through a series of convolutional layers and pooling layers, which is similar to that of the CNN [17,18]. The two kinds of layers reduce the spatial dimension of the image, and generate an abstract feature map in the light of local patterns.…”

Section: Structure Of U-net Modelmentioning

confidence: 99%

A Deep Learning Model for Striae Identification in End Images of Float Glass

Jin¹,

Xu²,

Tong³

et al. 2020

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For float glass, there is a correlation between the striae in end image and the manufacturing process. If clearly understood, the correlation helps to optimize and fine-tune the manufacturing process of float glass. This paper attempts to extract the striae from the end image of float glass with deep learning (DL) neural network (NN). For this purpose, an image segmentation model was established based on improved U-Net, a fully convolutional network (FCN), and used to accurately divide the glass liquid on the end image into different layers. Firstly, the improved U-Net model was constructed to extract the striae from each liquid layer on the end image. Next, the activation function and convolutional mode of the improved U-Net model were optimized to enhance the segmentation accuracy and shorten the training/prediction time. Finally, the proposed model was tested on the float glass production line of Hebei CSG Glass Co., Ltd. The test results show that our model achieved an accuracy of 94%. The research findings lay a solid basis for striae identification on end image of float glass, and provide guidance for optimization and fine-tuning of float glass manufacturing process.

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Section: Structure Of U-net Modelmentioning

confidence: 99%

A Deep Learning Model for Striae Identification in End Images of Float Glass

Jin¹,

Xu²,

Tong³

et al. 2020

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“…Further papers [23,30,34,71,75,87,104,108,112,113] highlight specific applications of machine learning to medical segmentation or further (medical) image processing. Ref.…”

Section: Deep Learningmentioning

confidence: 99%

The tempest in a cubic millimeter: Image-based refinements necessitate the reconstruction of 3D microvasculature from a large series of damaged alternately-stained histological sections

Lobachev

2019

Preprint

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This work presents two methods that facilitate a 3D reconstruction of microscopic blood vessels in the volume slightly larger than 1 mm 3 . The source of the data are histological serial sections, i. e., microscopic images of probes, stained with immunohistochemistry. Odd and even sections have different stainings in our primary data set. Thus, firstly, an approach to register an alternately-stained series is presented. With image filtering and a feature-detection-based registration we obtain a registered stack of 148 serial sections. The series has missing sections, locally damaged sections, artifacts from acquisition. All these hinder correct connectivity of blood vessels. With our second approach we interpolate the missing information while maintaining the connectivity. We achieve this with deformations based on dense optical flow. The presented methodology is applicable to further histological series. A combination of both approaches allows us to reconstruct more than 76 % larger volumes. An important detail was the composition mode of images. Summarizing, we use methods from image processing and computer vision to create large-scale 3D models from immunostained histological serial sections.

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“…So, we need to implement a more refined local positioning function and a more powerful feature description approach. In recent years, neural networks and deep learning has achieved great success in signal detection [27][28][29] and image processing [30][31][32]. Using deep CNNs for fine-grained image classification has become a popular research route.…”

Section: Weak Supervised Classification Modelmentioning

confidence: 99%

Local Importance Representation Convolutional Neural Network for Fine-Grained Image Classification

Yang

Zhang

2018

Symmetry

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Compared with ordinary image classification tasks, fine-grained image classification is closer to real-life scenes. Its key point is how to find the local areas with sufficient discrimination and perform effective feature learning. Based on a bilinear convolutional neural network (B-CNN), this paper designs a local importance representation convolutional neural network (LIR-CNN) model, which can be divided into three parts. Firstly, the super-pixel segmentation convolution method is used for the input layer of the model. It allows the model to receive images of different sizes and fully considers the complex geometric deformation of the images. Then, we replaced the standard convolution of B-CNN with the proposed local importance representation convolution. It can score each local area of the image using learning to distinguish their importance. Finally, channelwise convolution is proposed and it plays an important role in balancing lightweight network and classification accuracy. Experimental results on the benchmark datasets (e.g., CUB-200-2011, FGVC-Aircraft, and Stanford Cars) showed that the LIR-CNN model had good performance in fine-grained image classification tasks.

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Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

Cited by 78 publications

References 27 publications

A Deep Learning Model for Striae Identification in End Images of Float Glass

A Deep Learning Model for Striae Identification in End Images of Float Glass

The tempest in a cubic millimeter: Image-based refinements necessitate the reconstruction of 3D microvasculature from a large series of damaged alternately-stained histological sections

Local Importance Representation Convolutional Neural Network for Fine-Grained Image Classification

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