Description: 一个很好的fast marching算法的工具箱，是用matlab语言编写的-a good fast marching algorithm toolbox is the use of the Matlab language Platform: |
Size: 172179 |
Author:潘晓花 |
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Description: The goals of this tool is to manipulate the fast marching algorithm in 2D and 3D. Application to shortest path extraction (e.g. road tracking and tubular structure extraction in medical images), shape statistics and geodesic remeshing are presented. Platform: |
Size: 1349330 |
Author:费仙凤 |
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Description: toolbox_fast_marching
A toolbox for the computation of the Fast Marching algorithm in 2D and 3D. Platform: |
Size: 2957223 |
Author:dangle |
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Description: 一个很好的fast marching算法的工具箱，是用matlab语言编写的-a good fast marching algorithm toolbox is the use of the Matlab language Platform: |
Size: 172032 |
Author:潘晓花 |
Hits:
Description: The goals of this tool is to manipulate the fast marching algorithm in 2D and 3D. Application to shortest path extraction (e.g. road tracking and tubular structure extraction in medical images), shape statistics and geodesic remeshing are presented.-The goals of this tool is to manipulate the fast marching algorithm in 2D and 3D. Application to shortest path extraction (eg road tracking and tubular structure extraction in medical images), shape statistics and geodesic remeshing are presented. Platform: |
Size: 1349632 |
Author:费仙凤 |
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Description: 在MATLAB环境下的level set方法的实现，可以应用在多种领域中-In the MATLAB environment to achieve the level set method, can be applied in a variety of field Platform: |
Size: 1435648 |
Author:hawk |
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Description: 水平集快速步进算法的源码，参考书籍为：Level sets and fast matching algorim.-Level set fast marching algorithm source code, reference books as: Level sets and fast matching algorim. Platform: |
Size: 6080512 |
Author:刘娜娜 |
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Description: fastmarching算法的matlab实现源码，对学习偏微分方程图像处理算法有很大帮助-Fastmarching algorithm to learn matlab source, partial differential equations are of great help image processing algorithm
Platform: |
Size: 1460224 |
Author:ylx |
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Description: The method mainly consists in considering a reference normal ECG segment and input ECG (unknown behavior) segment.
Each segment of the input ECG T is compared to the reference segment using SEA.
The SEA method outputs a measure in the range [0..1] in terms of the correlation factor that reflects the degree of similarity between the reference and the segment under analysis. Based on this measure, the whole segment is classified as normal or abnormal.
A user specified threshold value on the correlation factor c is used for decision -The method mainly consists in considering a reference normal ECG segment and input ECG (unknown behavior) segment.
Each segment of the input ECG T is compared to the reference segment using SEA.
The SEA method outputs a measure in the range [0..1] in terms of the correlation factor that reflects the degree of similarity between the reference and the segment under analysis. Based on this measure, the whole segment is classified as normal or abnormal.
A user specified threshold value on the correlation factor c is used for decision
Platform: |
Size: 1366016 |
Author:keerthi |
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