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readme.txt
Feb 7, 2023 - Image enhancement code: time-resolved tomograms of EICP application using 3D U-net
Plain Text - 2.1 KB - MD5: d378b2f2e6e39b343cb17bd2522c6b7d
Detailed description of operation with codes
unet3D_v1.py
Feb 7, 2023 - Image enhancement code: time-resolved tomograms of EICP application using 3D U-net
Python Source Code - 3.8 KB - MD5: c84b9fd76ff97e75a91ccde5cc1395e2
3D U-net model
Chan_vese.m
Jul 7, 2021 - Fracture network segmentation
Plain Text - 5.3 KB - MD5: 42ba2403995ffbc255edfddfb575efa6
This file contains the workflow of Chan-Vese method
classifier.model
Jul 7, 2021 - Fracture network segmentation
Unknown - 1.9 GB - MD5: 9c0aaff5be02d37f4dd76756a142da01
This is the trained model with the Random forest method. This file could be loaded in the open source program ImageJ with TrainableWeka library. The predictions can be generated with processing.bsh code.
Frac_Unet_postprocess.py
Jul 7, 2021 - Fracture network segmentation
Plain Text - 3.0 KB - MD5: 8af741da7af5db62a1855a4c4eed56d0
With the code, the predictions of trained model for given input files can be generated and saved.
Frac_Unet_preprocess.py
Jul 7, 2021 - Fracture network segmentation
Plain Text - 2.2 KB - MD5: a269c380d683920ac900d3539a4fdc6a
Preparation of training data can be done with the code
Frac_Unet_training.py
Jul 7, 2021 - Fracture network segmentation
Plain Text - 5.6 KB - MD5: 0d2949b2c023b9f5b67ccefb8487d803
With the pre-processed training data, the code performs training of U-net model and saves a trained model after training.
img_seg.py
Jul 7, 2021 - Fracture network segmentation
Plain Text - 15.4 KB - MD5: 9a20bdaef7a1f87ecfdd053873a642bf
This file contains the load/save functions and core functions of the Sato and the Local threshold methods.
Local_threshold.py
Jul 7, 2021 - Fracture network segmentation
Plain Text - 1.5 KB - MD5: 68ae24ecafdaa06a04bf09504eb44fd2
This file contains the workflow of the local threshold method
post_random_forest.py
Jul 7, 2021 - Fracture network segmentation
Plain Text - 1.5 KB - MD5: 2c64aaca9f227470a4c9602988e8f9e8
The predictions of the Random forest method can be merged into the original size of image with the code.
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