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deboor-cox.rar
- 目的:运用强化学习!多分类器集成!降维方法等最新计算机技术,结合细胞病理知识,设计制作/智能化肺癌细胞病理图像诊断系统0"方法:采集细胞图像,运用基于强化学习的图像分割法将细胞区域从背景中分离出来 运用基于样条和改进2方法对重叠细胞进行分离和重构 提取40个细胞特征用于贝叶斯!支持向量机!紧邻和决策树4种分类器,集成产生肺癌细胞分类结果 建立肺癌细胞病理图库,运用基于等降维方法对细胞进行比对,给予未定型癌细胞分类"结果:/智能化肺癌细胞病理诊断系统0应用于临床随机1200例肺
compressed-sensing
- 压缩感知,稀疏表示采用小波基表示,压缩测量采用随机高斯矩阵,重构算法是omp重构-Compressed sensing, sparse representation using wavelet representation, compression measurements using random Gaussian matrix remodeling reconstruction algorithm is omp
Wavelet_OMP
- 包含压缩传感的随机矩阵程序,如小波变换和高斯随机矩阵和omp重构算法-Random matrix containing the compressed sensing programs, such as wavelet transform and Gaussian random matrices and omp reconstruction algorithm
nGpFBMP-ver-1.0
- 非高斯分布信号的快速重构算法,该代码为论文“A Fast Non-Gaussian Bayesian Matching Pursuit Method for Sparse Reconstruction”论文的源代码-Fast reconstruction algorithm of non-Gaussian signal The code for the paper "A Fast Non-Gaussian Bayesian Matching Pursuit Method for Sparse
GSxishu_samp
- 本代码使用高斯绝对稀疏信号进行重构,采用的重构算法是SAMP,重构效果好!-This code uses the absolute sparse Gaussian signal reconstruction, reconstruction algorithm uses a SAMP, good remodeling effect!
Wavelet-dec--rec
- 在理解了离散小波变换的基本原理和算法的基础上,通过设计VC程序对简单的一维信 号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波和Haar小波变换,从而得到小波分解系数;再通过改变分解得到的各层高频系数进行信号的小波重构达到消噪的目的。在这一程序实现的过程中能直观地理解信号小波分解重构的过程和在信号消噪中的重要作用,以及在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。-In understanding the basis of discrete wavelet
fbmp_v1_3.tar
- 经典的稀疏重构算法,即快速贝叶斯追踪算法,恢复出的信号精度高,恢复算法复杂度低-Classic sparse reconstruction algorithm, namely the bayesian tracking algorithm quickly, to restore the signal of high precision, low recovery algorithm complexity
BP
- 一维信号BP重构算法,先生成稀疏度为K的稀疏矩阵,再加入高斯白噪声进行算法重构以及质量衡量。-BP signal reconstruction algorithm for one dimensional, Mr. into sparse matrix sparsity of K, then the Gauss white noise and measure the quality of reconstruction algorithm.
CS--design-MATLAB
- 基于MATLAB的图像压缩感知毕业设计说明书。运用matlab软件,在离散傅里叶变换(DFT)和离散余弦变换(DCT)分块CS的基础上,采用正交匹配追踪算法(OMP)实现了对一维信号和二维图像的高概率重构。将重构结果与原始信号对比,结果表明,只要采样数M(远小于奈奎斯特定理所需要的采样率)能够包含图像所需要的有用信息时,CS算法就能精确的完成对图像的重构,并且重构效果也比较好-Compressed sensing image graduation design specification bas
Laplace-pyramid
- 拉普拉斯金字塔在图像融合中有所应用,方法是首先对两个待融合图像求拉普拉斯残差金字塔,然后用模板对每一级残差图像进行融合得到融合后图像的残差金字塔,然后对这个金字塔进行重构就能得到最终的融合图像,图像各尺度细节得到保留。(注:融合时模板一般会先用高斯函数模糊一下) 不过这里不实现融合,只实现拉普拉斯金字塔的建立。 -Laplace pyramid has applications in image fusion method is to first find two images to b
haar
- 基于haar小波的图像分解和重构,加入高斯噪声和椒盐噪声来验证小波的效果-Haar wavelet-based image decomposition and reconstruction, adding salt and pepper noise and Gaussian noise to verify the effect of wavelet
kde2d
- 二维高斯核函数重构 重构方法不依赖于参数化模型-2D Gaussian Kernel Reconstruction fast and accurate state-of-the-art bivariate kernel density estimator with diagonal bandwidth matrix. The kernel is assumed to be Gaussian. The two bandwidth parameter
jiachaung
- 令 ,w(n)为高斯窗函数。试用matlab软件,取不同长度的窗函数,分别求x(n)的离散短时傅里叶变换,并进行信号重构。试讨论窗函数长度对时频分辨率、重构精度的影响。-Order, w (n) is Gaussian window function. Try matlab software, takes a different length of the window function, respectively, find x (n) short-time discrete Fourier
Untitled90
- 计算信号信噪比和其相关的误差。一维信号BP重构算法-生成稀疏度为K的稀疏信号-添加高斯白噪声进行信号的观测-BP算法的重构-重建质量衡量-Calculate the Signal Signal to Noise Ratio, Correlation Error. Advanced One-Dimensional Signal BP Algorithm Reconstruction- Generate Sparse Signal with Sparse Degree K- Add Gaussian
Compressive_sensing
- 傅立叶变换矩阵对信号进行稀疏表示,用高斯随即观测矩阵观测,重构方法为征缴匹配追踪算法、压缩感知入门程序,经典之作- U5085 u7An2F3 u53D3 u53A2 u7R09 u09R0 U9635 u89C2 u6D4B uFF0C u91CD u678 u6B1 u6CD5 u4E3A u5F81 u7F34 u5339 u914D u8E U5E8F uFF0C u7ECF u5178 u4E4B u4F5C
compressing
- 应用傅立叶变换矩阵对信号进行稀疏,经高斯随机观测矩阵观测,经正交匹配追踪算法重构.压缩感知入门程序-The Fourier transform matrix is used to spill the signal. Observed by Gaussian random observation matrix and reconstructed by orthogonal matching tracing algorithm. Compression Sensing Getting Started
SpectrumRecongnize
- 高光谱识别程序。可以改变字典长度进行多尺度识别,识别时,利用重构误差改变字典长度,通过高斯变换得到多尺度信息进行识别-Hyperspectral recognition program. Can change the length of the dictionary for multi-scale recognition, recognition, the use of reconstruction error to change the dictionary length, through t
Untitled00
- 压缩感知用高斯随机矩阵作为测量矩阵,用BP算法重构(The Gauss random matrix is used as the measurement matrix for the compression perception, and the BP algorithm is reconstructed)