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红外和可见光的匹配跟踪在军事、遥感等领域有着广泛的应用。针对灰度和图像特征存在比较大差异的红外和可见光图像，本文采用了最大互信息算法，结合形态学梯度和小波分解。互信息算法优点在于不需要对多模图像灰度间的关系做任何假设，不足之处在于它对图像空间信息的忽略而且计算时间较长。本文互信息结合多结构元的形态学梯度检测的图像边缘，可以使得图像匹配精度提高，还能改善局部极值的问题，再利用小波分解对图像进行压缩降低分辨率，可以减少互信息计算量。最后的实验数据表明在配准过程中互信息的计算速度得到了优化，匹配精度得
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用MATLAB实现的压缩传感，用于特征降维，特征融合，相关分析等,雅克比迭代求解线性方程组课设。- Using MATLAB compressed sensing, For feature reduction, feature fusion, correlation analysis, Jacobi iteration for solving linear equations class-based.
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最小均方误差等算法的MSE的计算,用MATLAB实现的压缩传感，旋转机械二维全息谱计算。- Minimum mean square error MSE calculation algorithm, Using MATLAB compressed sensing, Rotating machinery 2-d holographic spectrum calculation.
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Relief计算分类权重，基于K均值的PSO聚类算法,用MATLAB实现的压缩传感。- Relief computing classification weight, K-means clustering algorithm based on the PSO, Using MATLAB compressed sensing.
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基于matlab平台实现,信号处理中的旋转不变子空间法，用MATLAB实现的压缩传感。- Based on matlab platform, Signal Processing ESPRIT method, Using MATLAB compressed sensing.
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用MATLAB实现的压缩传感，基于多相结构的信道化接收机，用于信号特征提取、信号消噪。- Using MATLAB compressed sensing, Channelized receiver based on multi-phase structure, For feature extraction, signal de-noising.
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是小学期课程设计的题目，有循环检测，周期性检测，用MATLAB实现的压缩传感。- Is the topic of the elementary school stage curriculum design, There are cycle detection, periodic testing, Using MATLAB compressed sensing.
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对球谐函数图形进行仿真,用MATLAB实现的压缩传感，wolf 方法计算李雅普诺夫指数。- Of spherical harmonics graphic simulation, Using MATLAB compressed sensing, wolf calculated Lyapunov exponent.
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利用最小二乘法进行拟合多元非线性方程，比较了软阈值，硬阈值及当今各种阈值计算方法,用MATLAB实现的压缩传感。- Multivariate least squares fitting method of nonlinear equations, Comparison of soft threshold and hard threshold and today various threshold calculation method, Using MATLAB compressed sensin
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用MATLAB实现的压缩传感，matlab编写的元胞自动机，包括邓氏关联度、绝对关联度、斜率关联度、改进绝对关联度。- Using MATLAB compressed sensing, matlab prepared cellular automata, Including Deng s correlation, absolute correlation, correlation of slope, improved absolute correlation.
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Compressed sensing on the classic sp matching tracking algorithm that can run, useful
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压缩感知英文入门书籍 详细介绍关于压缩感知的基本理论与前沿发展(Introduction to Compression Percepts in English: a brief introduction to the basic Theory and Frontier Development of Compression perception)
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实现压缩感知的稀疏信号恢复，采用L1范数约束最小化策略(Sparse signal recovery with compressed sensing, by using the L1 norm constraint minimization strategy)
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本程序的性能已经超过其他算法，有CDF三角函数曲线/三维曲线图，用MATLAB实现的压缩传感。( This program has exceeded the performance of other algorithms, There CDF trigonometric curve/3D graphs, Using MATLAB compressed sensing.)
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基于压缩感知的稀疏脉冲反射系数谱反演方法研究(Research on sparse pulse reflection coefficient spectrum inversion method based on compressed sensing)
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本文分别以稀疏基有离散余弦变换基（DCT）和快速傅立叶变换基（FFT）做为稀疏基，高斯随机矩阵、部分哈达玛矩阵为测量矩阵，L1范数、正交匹配追踪算法（OMP）为重建算法进行压缩感知算法实现。(In this paper, DCT and FFT are used as sparse basis, Gauss random matrix and partial Hadamard matrix are used as measurement matrix, L1 norm and OMP are u
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