lushaolin (lushaolin-PhDing)

lushaolin-PhDing

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Company:Harbin Institute of Technology, Shenzhen

Location:Shenzhen, China

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PSO-EM

PSO-EM algorithm for 3D-to-2D registration (pose estimation)

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2D-3D-Point-Set-Registration-Based-on-Global-Rotation-Search

# 2D-3D Point Set Registration Based on Global Rotation Search # Copyright (C) 2018 Yinlong Liu@outlook.com # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # Any publications resulting from the use of this code should cite the # following paper: # Yinlong Liu, Yuan Dong, Zhijian Song and Manning Wang, "2D 3D Point Set Registration Based on Global Rotation_Search", IEEE Transactions on Image Processing (TIP) # # #==================Note================= # #1. First step->open demo_rot.m. It is a demo of Rotation Search in SO(3) for 2D-3D point set registration. # #2. The input data is set to a real circumstance that 3D point set is far away for projection plane, and in front of camera. Our method also can be applied to unusual condition that camera is surrounded by 3D point set, only if you make some fix. # #3. RotaionSearch.m is the kernel of algorithm. You can easily extend it to SE(3) searching by grid-search, while tuning parameters depends on your tasks. # #4. Fast and Global 2D-3D point set registration without correspondence is still an open problem and need further explorations. I am very happy that if you could benefit from our code. # # # Author: Yinlong Liu # Date: 20181218 # Revision: 1.0

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Fully_Unsupervised_CNN_Registration_Keras

Fully unsupervised 2D/3D image registration with ConvNet.

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Opt-GO-3D-2D-Registration

Optimization-GradientOrientation based 3D/2D registration algorithm of medical imaging

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image_registration

Python implementation of 2d and 3d image registration

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airlab

Image registration laboratory for 2D and 3D image data

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xreg

Library and executables for modeling and registration applications in medical image analysis. Particular emphasis on intraoperative fluoroscopic (X-ray) navigation via 2D/3D registration.

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ShapeWorks

ShapeWorks

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RobotForceControl

admittance control导纳与拖动

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CUDA_DRR

GPU accelerated python library for generation of DRR in linux

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HipHop_2D3Dregistration

2D/3D registration between CT/MRI or STL models and X-ray images (November 2018)

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CUDA_DigitallyReconstructedRadiographs

GPU accelerated python library for generation of Digitally Reconstructed Radiographs (March 2018)

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ActiveShapeModel

Implementation of Active Shape Model in Python (OpenCV).

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ActiveShapeModels

A python implementation of the active shape models by Cootes (http://www2.compute.dtu.dk/courses/02511/docs/asm_overview.pdf)

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dentalvision

Active Shape Model implementation for computer vision in dental radiographs

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shapewarp

Contains tools for generalized Procrustes analysis, active shape models and shape-based image warping

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asm

Active Shape Model for Facial Keypoint Detection

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active_shape_models

Implementing active shape models for a machine learning class

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SSM-Creation

Femoral Statistical Shape Model Creation

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machinelearning

My blogs and code for machine learning. http://cnblogs.com/pinard

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CTPelvic1K

Resources of the paper “Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models”.

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100-Days-Of-ML-Code

100-Days-Of-ML-Code中文版

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MRNET-For-Knee-Diagnosis

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

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easy-rl

强化学习中文教程,在线阅读地址:https://datawhalechina.github.io/easy-rl/

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