Description: 高斯脉冲及高斯一阶导、高斯二阶导产生时域图-Gaussian pulse and a Gaussian derivative, Gaussian, when the second derivative domain plan Platform: |
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Author:谭汉洪 |
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Description: 高斯脉冲及高斯一阶导、高斯二阶导产生时域图-Gaussian pulse and a Gaussian derivative, Gaussian, when the second derivative domain plan Platform: |
Size: 2048 |
Author:谭汉洪 |
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Description: 图像高斯滤波,再边缘检测中,首先要用到高斯滤波-images Gaussian filtering, edge detection again, the first use of the Gaussian filter! ! Platform: |
Size: 176128 |
Author:柳杨 |
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Description: Models UWB TX and RX using BPSK fifth derivative.
MATLAB Release: R13
Description: This m file models a UWB system using BPSK with the fifth order derivative of the gaussian pulse with correlation receiver and intgrator.
-Models UWB TX and RX using BPSK fifth derivative. MATLAB Release : R13 Description : This file m models a UWB system using BPSK with the fifth order derivative of the Gaussian pulse with correlation receiver and intgrator. Platform: |
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Author:王琼 |
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Description: 对15阶高斯导函数给出各自的峰值功率，从图中可以看出他们的峰值功率的区别！-15-order derivative of Gaussian function are given their peak power, can be seen from the graph of their difference between peak power! Platform: |
Size: 1024 |
Author:娜娜 |
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Description: 利用小波变换检测突变点实验的实例，程序最后生成3个图像演示了该算法，分别为原数字信号、高斯函数作为基函数、高斯函数的一阶导数作为基函数的小波变换。-Mutation detection using wavelet transform examples of experimental points, the program generates the final three images to demonstrate the algorithm, namely, the original digital signal, Gaussian function as basis function, Gaussian function of the first order derivative as the base function of wavelet transform. Platform: |
Size: 1024 |
Author:ladan |
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Description: 采用低通性质的平滑函数为高斯函数，根据他的一阶导数、二阶导数作为小波基函数进行突变点分析。
-The use of low-pass nature of the smoothing function for the Gaussian function, according to his first derivative, second derivative wavelet basis function as a point mutation analysis. Platform: |
Size: 1024 |
Author:ladan |
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Description: 包括图像分析的四部分代码：matlab扩散和高斯函数，线性扩散，线性复扩散，非线性扩散。-It contains four parts：
[1]MATLAB function:diffusion.m gauss.m
[2]Linear diffusion
Applying linear diffusion to images creating linear scale-space.
MATLAB code: demo_lin.m
Image: haifa1.bmp
[3]Linear complex diffusion
Applying linear complex diffusion creating Gaussian and Laplacian scale-spaces. MATLAB code: demo_cmplin.m
[4]Nonlinear diffusions:
Perona-Malik ["Anisotropic diffusion"]
Catte et al. regularization of P-M
Complex ramp-preserving diffusion
Nonlinear edge preserving diffusions.The classical Perona-Malik process: the value of the diffusion coefficient is reduced near edges estimated by the first derivative. Best applied to step edges. A regularized version by Catte et al- the gradient estimation for controlling the process is smoothed by a Gaussian. Proved to be mathematically well-posed. Ramp preserving complex diffusion- best for ramp-type edges. Results are smoother with almost no staircasing effects.
MATLAB code: demo_nldif.m
Image: ct_scan.bmp Platform: |
Size: 239616 |
Author:吴豪科 |
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Description: 数字图像的边缘检测
本科毕业设计（边缘检测是数字图像处理中的重要内容。本文首先对图像的边缘检测的各种算法和算子做了总结和分析。Canny最早提出了边缘检测的三条连续准则:最优检测结果、最优定位和低重复响应,并在这些准则的基础上得到了“最优线性滤波器”―高斯函数的一阶导数。经过十几年的发展,目前已经有了对这个准则的很多改进,本文也对这个方面的工作做了小结。Demigny在理论分析和实践的基础上给出了边缘检测的离散准则,并且证明在离散准则中Canny提出的第三个准则可以被阀值操作所取代。本文利用了数值方法求出了Demigny离散准则下阶梯形边缘检测的最优线性滤波器和对应着它的平滑算子。利用这个算子和Canny边缘检测方法得到了一个完整的边缘检测算法并用VC++实现了这种算法.从算法对大量图像边缘检测的结果来看,这种算法虽然简单但是效果很好,是边缘检测的一种很好的实用方法。-Edge detection is important in image procession. This paper made a summary and analysis of edge detecting algorithm and edge detector. Canny has proposed three continuous criteria to compare the performance of different filters: good detection, good localization and low-responses. Based on these criteria he got optimal filter for edge detection: derivative of Gaussian function. After more than ten years research, Canny’s theory has been ameliorated in many aspects, this paper also made a review of it. Based on the practice and theory. Demigny gave three discrete criteria for edge detection like Canny’s criteria and he has proofed that the third criterion can be replaced by an appropriate thresholding operation. This paper used numerical method to get the optimal filter and smooth operator under the Demigny’s criteria. Then I combine these filters and Canny’s edge detecting technique to get an integrated edge detecting algorithm. I have implemented the algorithm using VC++. From the res Platform: |
Size: 1397760 |
Author:swx |
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Description: 高斯脉冲的导函数。产生高斯脉冲的前15阶导函数。-Derivative Gaussian pulse function. Gaussian pulses have a derivative function of the first 15. Platform: |
Size: 1024 |
Author:yeliang |
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Description: 通过高斯函数导数检测图像边缘，实质是方向可调小波变换检测图像边缘（8个方向变换）。-Gaussian function through derivative edge detection in real terms is adjustable direction edge detection wavelet transform (change the direction of 8). Platform: |
Size: 259072 |
Author:qht |
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Description: This source code simulates UWB based wireless Communication system which transmits color image over wireless medium.
Modulation type: Pulse position modulation
Pulse: second derivative of a Gaussian pulse Channel: AWGN
Image: lib.jpg-This source code simulates UWB based wireless Communication system which transmits color image over wireless medium.
Modulation type: Pulse position modulation
Pulse: second derivative of a Gaussian pulse Channel: AWGN
Image: lib.jpg Platform: |
Size: 83968 |
Author: |
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Description: 关于Steerable filtering decomposition 的matlab程序,- STEERGAUSS Implements a steerable Gaussian filter. This m-file can be used to evaluate the first directional derivative of an image, using the method outlined in:
W. T. Freeman and E. H. Adelson, "The Design and Use of Steerable Filters", IEEE PAMI, 1991. Platform: |
Size: 388096 |
Author:张宇 |
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Description: Gradient calculation using first order derivative of Gaussian. by using a 2-D Gaussian kernel, derivatives along x and y directions are calculated
Platform: |
Size: 1024 |
Author:eetuna |
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Description: 该演示中包含的代码演示如何氡相似的功能，可以用来提高（以及部分）在Connectome电磁图像单元格边界。
请举出下列文件如果您发现此代码有用：
Ritwik库马尔，阿梅里奥五雷纳和Hanspeter Pfister说“氡样的特点及其应用Connectomics”，接受，IEEE计算机学会研讨会在生物医学图像分析（MMBIA）2010年数学方法
http://seas.harvard.edu/〜 rkkumar
radonLikeFeaturesDemo.m：演示边缘增强用氡相似的功能，
getEdgeFeatures.m：助手为高斯函数的二阶导数滤波器
makeBarFilters.m：助手为高斯函数的二阶导数滤波器
sample.png：示例图像在此演示使用-The demo included in the code demonstrates how Radon-Like Features can be used to enhance (and segment) cell boundaries in Connectome EM images.
Please cite the following paper if you find this code useful:
Ritwik Kumar, Amelio V. Reina & Hanspeter Pfister, “Radon-Like Features and their Application to Connectomics”, accepted, IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) 2010
http://seas.harvard.edu/~rkkumar
radonLikeFeaturesDemo.m : Demonstrates edge enhancement using Radon-Like Features
getEdgeFeatures.m : Helper function for Gaussian Second Derivative Filters
makeBarFilters.m : Helper function for Gaussian Second Derivative Filters
sample.png : Sample image for use in this demo
Platform: |
Size: 12288 |
Author:zhou |
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Description: 用于Harris角点检测，可以修改滤波窗口，也可以选择改进的高斯函数一阶微分作为滤波窗口-Harris corner detection is used, you can modify the filter window, you can also choose the improved first-order derivative of Gaussian function as the filtering window Platform: |
Size: 1024 |
Author:echo |
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Description: 对三维图像进行求导的一个程序，利用高斯卷积后对图像求导相当于对高斯函数求导的性质。是用itk库写的一个通用模板类，里面还有例子。-Computation of local image derivatives is an important operation in many image processing tasks that
involve feature detection and extraction, such as edges, corners or more complicated features. How-
ever, derivative computation in discrete images is an ill-posed problem and derivative operators without
any prior smoothing are known to enhance noise. Here we present a new convolution operator, the
GaussianDerivativeOperator, that allows to calculate locally Gaussian derivatives of N order. Fur-
thermore, we present some useful classes and examples that make use of this new operator. Platform: |
Size: 866304 |
Author:xrx |
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Description: This m file plots the time and frequency waveforms for BPSK fifth derivative equation of gaussian pulse used in UWB system analysis.
Platform: |
Size: 83968 |
Author:daly |
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Search - derivative gaussian