资源列表
Application-SURF
- 基于surf的图像特征的提取，采集，配准已经图像融合拼接技术的研究-Image feature extraction surf-based acquisition, registration has been studied image fusion splicing technology
Static-image
- 静态图像的拼接技术的研究，以及最先图像拼接方面的技术、理论成果。-Static image stitching technology research, as well as technical, theoretical aspects of the results of the first image stitching.
Infrared-image
- 图像拼接 在红外在线监测系统中的应用,如何实现红外线的检测控制-Infrared image stitching in-line monitoring system, how to control the infrared detection
common-image
- 常用图像 拼接算法综述概括，讲述图像拼接的相关算法及应用-Summary of common image stitching algorithm generalization algorithms and applications about image stitching
ImageProcess
- 对图像通过Hough变换提取直线算法,采用VC环境编写。-Linear algorithm for image extraction by Hough transform.
Untitled
- 指纹图像的归一化处理程序，仅供参考，谢谢！-Fingerprint image normalization process for reference only. . . . . . . . . . . . . . . . . . . . . . . . . .
6645LBP
- LBP已经成功应用于人脸检测，唇语识别，表情检测，动态纹理等等领域。其算法复杂度低，消耗内存小，原理简单.-LBP has been successfully applied to face detection, lip recognition, face detection, dynamic texture and so on. Its low algorithm complexity and memory consumption is small, simple principle.
SVM
- 支持向量机(Support Vector Machine，SVM)是Corinna Cortes和Vapnik8等于1995年首先提出的，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。-SVM (Support Vector Machine, SVM) was Corinna ms Cortes and Vapnik8 is first proposed in 1995, it is to solve the small sample,
(SVM)-regression-algorithm
- 对比这么复杂的推导过程，SVM的思想确实那么简单。它不再像logistic回归一样企图去拟合样本点（中间加了一层sigmoid函数变换），而是就在样本中去找分隔线，为了评判哪条分界线更好，引入了几何间隔最大化的目标。-The derivation process of the contrast is so complex, the idea of SVM is so simple. It is no longer tried to like logistic regression fitting
Angle-cosine--diffusion-distance
- 详细讲解了夹角余弦，扩散距离，KD-tree，他们的原理以及优势-In detail the included Angle cosine, diffusion distance, KD- tree, their principles and advantages
SVM-reviewed
- 支持向量机方法中也存在着一些亟待解决的问题，主要包括：如何用支持向量机更有效的解决多类分类问题，如何解决支持向量机二次规划过程中存在的瓶颈问题、如何确定核函数以及最优的核参数以保证算法的有效性等。-Support vector machine (SVM) method also exist some problems to be solved, mainly includes: how to use support vector machine (SVM) is more effective t
SVM-regression-theory-and-control-
- 支持向量机回归理论与神经网络等非线性回归理论相比具有许多独特的优点有线性回归和非线性回归,其模型的选 择包括核的选择、容量控制以及损失函数的选择.在控制方面的研究包括非线性 时间序列 的预测及应用、系统辨识以及优化控制和学习控制等方面的研究-Support vector machine (SVM) regression theory and neural network has many unique advantages such as nonlinear regression theory