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Mike_W / Defect-Detection-in-Nanofibers-by-Image-Classification

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README.md

Defect-Detection-in-Nanofibers-by-Image-Classification

Introduction

This project concerns the detection of defective regions in SEM (Scanning Electron Microscope) images. These images have been acquired for monitoring the production of nanofibers. The images are contain in the following paper (Carrera2016). Scanning Elector Microscope image with anomalies in it. Also, we have the ground truth of the images, calculated also in (Carrera2016).

So far, in (Carrera2016) they have addressed the problem as an anomaly-detection problem, without exploiting during the learning (i.e. training) stage any example of defective regions. So the aim of this project is to address the defect-detection problem as a two-class classification problem where a test image is divided in patches (small squared regions) and each patch is classified as normal/anomalous. In total there are 46 images where 40 of them contains anomalies and 6 are completely normal images.

So the different aims of the projects are:

  • Taking patches based in the GT images where the whole patch is anomalous, or all is normal.
  • Training a classifier for predicting between anomalous or normal using a Deep Learning approach.
  • Using this classifier to predict each patch of a new image.

This is a project for the Image Analysis and Computer Vision course at Politecnico di Milano (2016/2017).

Documentation

In the documentation you could find the explanation of the project. Everything is explained there.

DataSet

Since this was the continuation for a paper, I don't know if I'm able to public the dataset I've created. If possible, it would be uploaded in the future.

Author

Francisco Carrillo Pérez (C)

< carrilloperezfrancisco@gmail.com >

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简介

本项目探讨扫描电子显微镜(SEM)影像中缺陷区域的侦测。这些图像是用来监测纳米纤维的生产的。图片包含在以下文件中(Carrera2016)。扫描电镜图像有异常。此外,我们还获得了图像的基本真实性,也计算在(Carrera2016)中。 到目前为止,在(Carrera2016)中,他们将该问题作为异常检测问题来处理,而没有在学习(即培训)阶段利用任何缺陷区域的示例。因此,本计画的目标是将缺陷侦测问题作为两类分类问题来处理,其中测试影像被分割成小块(小平方区域),每个小块被分类为正常/异常。总共有46幅图像,其中40幅包含异常,6幅是完全正常的图像。 展开 收起
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