搜索资源列表
2D-LDA
- 2维线性判别进行人脸识别的程序，很不错！采用ORL人脸库，取每人的1、3、5、7、9五幅图像作为训练图像，其余作为测试图像，进行二维线性判别。计算出特征向量矩阵，降序排列后，取前d（d=2,4,6，……，20）个特征向量组成的矩阵作为变换矩阵，对训练集合测试集进行特征重建，最后采用最近邻分类器。附有实验的结果。-code for face recognition based 2D-LDA，the performance is nice！
Otsu-method
- 申铉京等提出三维直方图重建和降维的Otsu阈值分割算法。通过重建三维直方图，减弱了噪声干扰，将三维直方图区域划分由八分法改为二分法，使得阈值搜索的空间维度从三维降低到一维。-Shin Hyun Beijing and proposed a three-dimensional histogram reconstruction and dimensionality reduction of the Otsu threshold segmentation algorithm. Weakened by
Mnf
- MNF变换，能够实现高光谱遥感图像的MNF正逆变换，进行图像降维与重建-MNF transform, to achieve high spectral remote sensing images MNF inverse transform, image dimensionality reduction and reconstruction
MyPca
- matlab 运用PCA算法对图像降维级图像重建代码-matlab image using PCA dimensionality reduction algorithm for image reconstruction code level
testMNF
- 最小噪声分离，能够实现高光谱遥感图像的MNF正逆变换，进行图像降维与重建(Minimum noise separation can realize the MNF positive and inverse transformation of hyperspectral remote sensing images, and reduce the image dimension and reconstruct the image)
python计算机视觉.pdf
- 本书是计算机视觉编程的权威实践指南,依赖 Python 语言讲解了基础理论与算法,并通过 大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、 立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。(This book is an authoritative guide to computer vision programming, which explains basic theories and algorithms based on
KLT
- 本程序实现了利用KL变换进行特征分解，并进行降维重建，示例图片在文件中给出。(This program realizes feature decomposition using KL transform and dimensionality reduction reconstruction. The example pictures are given in the file.)
Python机器视觉编程
- 《Python计算机视觉编程》是计算机视觉编程的实践指南，依赖Python语言讲解了基础理论与算法，并通过大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。另外，书中附带的练习还能让读者巩固并学会应用编程知识。("Python Computer Vision Programming" is a practical guide to computer vision pro