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Object-Recognition-via-Sparse-PCA 利用稀疏主分量分析实现目标识别中的特征提取
- 利用稀疏主分量分析实现目标识别中的特征提取，包括论文和仿真代码。-Informative Feature Selection for Object Recognition via Sparse PCA
6480
- 有信道编码，调制，信道估计等，是学习PCA特征提取的很好的学习资料，包括最后计算压缩图像的峰值信噪比和压缩效果的源码。- Channel coding, modulation, channel estimation, Is a good learning materials to learn PCA feature extraction, Including the final calculation of the compressed image peak signal to noise ra
bwnfn
- 分形维数计算的毯子算法matlab代码,是学习PCA特征提取的很好的学习资料，可以提取一幅图中想要的目标。- Fractal dimension calculation algorithm matlab code blankets, Is a good learning materials to learn PCA feature extraction, Target can be extracted in a picture you want.
vempa
- 结合PCA的尺度不变特征变换(SIFT)算法，实现了对10个数字音的识别程序LZ复杂度反映的是一个时间序列中。- Combined with PCA scale invariant feature transform (SIFT) algorithm, Realization of 10 digital audio recognition program LZ complexity is reflected in a time sequence.
langnang_v13
- 包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法，结合PCA的尺度不变特征变换(SIFT)算法，用于时频分析算法。- Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm, Combined with PCA scale invariant feature transform (SIFT) algorithm, For time-frequency analysis algorithm.
ue533
- 采用热核构造权重，结合PCA的尺度不变特征变换(SIFT)算法，对于初学者具有参考意义。- Thermonuclear using weighting factors Combined with PCA scale invariant feature transform (SIFT) algorithm, For beginners with a reference value.
svnic
- PLS部分最小二乘工具箱,结合PCA的尺度不变特征变换(SIFT)算法，是路径规划的实用方法。- PLS PLS toolbox, Combined with PCA scale invariant feature transform (SIFT) algorithm, Is a practical method of path planning.
uu580
- 人脸识别中的光照处理方法，是学习PCA特征提取的很好的学习资料，可以提取一幅图中想要的目标。- Face Recognition light treatment method, Is a good learning materials to learn PCA feature extraction, Target can be extracted in a picture you want.
cu430
- 进行逐步线性回归，用平面波展开法计算二维声子晶体带隙,是学习PCA特征提取的很好的学习资料。- Stepwise linear regression, Computation Method D phononic bandgap plane wave, Is a good learning materials to learn PCA feature extraction.
ikvei
- 随机调制信号下的模拟ppm，结合PCA的尺度不变特征变换(SIFT)算法，包括主成分分析、因子分析、贝叶斯分析。- Random ppm modulated analog signal under Combined with PCA scale invariant feature transform (SIFT) algorithm, Including principal component analysis, factor analysis, Bayesian analysis.
sisws
- 均值便宜跟踪的示例，是学习PCA特征提取的很好的学习资料，实现六自由度运动学逆解算法。- Example tracking mean cheap, Is a good learning materials to learn PCA feature extraction, Six degrees of freedom to achieve inverse kinematics algorithm.