搜索资源列表
heiv_src
- C++ code implementing the estimation of errors-in-variables models under point dependent noise. It includes examples for linear, ellipse, fundamental matrix and trifocal tensor estimation. The theory is described in A general method for errors-in-var
spotbox-v1.0
- 压缩感知理论，系数学习工具箱，多个信号算子，观测矩阵算子-Compressed sensing theory, the coefficient of learning toolbox, multiple signal operator, the observation matrix operator
Search_matrix_based_on_the_adaptive_anti_collision
- 提出一种基于搜索矩阵的自适应防碰撞算法．该算法有效利用碰撞信息，通过构造搜索矩阵，克服自适应 二又树搜索算法逐位搜索效率低的缺点．同时提出碰撞堆栈的概念，根据时隙状态，来自适应调整搜索路径，从而 减少碰撞和空闲时隙数以及传输的比特量．理论和仿真实验表明，该算法有效实用，可有效减少识别时间，提高搜 索效率-Presents a matrix of adaptive search-based anti-collision algorithm. The algorithm is the e
linear-algebra
- 图形图像处理需要强大的线性代数以及概率论的数学基础，此资源包括重要的线性代数及矩阵论的知识，对于从事图像处理专业的学生有重大帮助-Graphical image processing requires a strong linear algebra and mathematical foundations of probability theory, this resource includes important linear algebra and matrix theory of know
the-Renyi-entropy-theory
- 建武和申铉京等人充分利用图像空间邻域信息，引入均值-中值-梯度共生矩阵模型，并结合Renyi熵相关理论, 提出一种结合纹理信息的三维Renyi熵阈值分割算法. 同时给出了该方法的快速递推公式。-Jianwu and Shin Hyun Beijing, who make full use of the image spatial information, the introduction of the mean- in value- gradient co-occurrence matrix m
2d-dct
- 多维矢量矩阵理论中，2维dct能量集中性测试c++代码-Multi-dimensional vector matrix theory, the two-dimensional DCT energy the centralized testing c++ code
Recovery-from-compression
- 从压缩传感,秩最小化到低秩矩阵恢复_理论与应用-Recovery from compression feel low rank matrix _ theory and application
image-fusion-based-on-cs-measurement
- 基于CS测量矩阵优化的图像融合，测量矩阵是压缩感知理论的三大核心部分之一，它直接影响着压缩感知理论在图像融合领域的应用-Image Fusion CS measurement matrix optimization, measurement matrix is compressed sensing theory of one of the three core parts, which directly affects the compressive sensing
ellipsefit
- 基于矩阵理论，根据最小二乘法进行的椭圆曲线拟合的matlab程序-ellipse fit theory of matrix least square
NMF
- 基于非负矩阵分解理论，实现多源的图像融合-Based on non-negative matrix decomposition theory, to achieve image fusion of multi-source
灰度共生矩阵
- 图像处理，灰度共生矩阵。已知被理论证明并且实验显示它在纹理分析中是一个很好的方法,广泛用于将灰度值转化为纹理信息(Image processing, gray level co-occurrence matrix. Known to have been proved by theory, and experiments show that it is a good method in texture analysis, and is widely used to transform gray v