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- 基于内容的图像识别特征提取部分——k-均值聚类分割获取形状等信息-Content-based image recognition, feature extraction part of- k-means clustering segmentation information for shape
JAVA
- 基于JAVA实现的图像特征提取源代码 图像特征提取的源代码，使用sobel算子提取图像边缘，使用hu矩提取图像形状特征，使用颜色中心矩提取图像颜色特征，请使用eclipes导入该工程并运行test文件（可将test文件中去掉一些注释部分运行更多功能）。-JAVA based on the realization of the image feature extraction image feature extraction source code source code, use the sob
shape_feature
- 一个简单的傅立叶带亮度描绘子提取图像的形状特征程序-A simple Fourier descr iptor with the brightness of the image shape feature extraction procedures
LKTK
- Recovering 3-D structure from motion in noisy 2-D images is a problem addressed by many vision system researchers. By consistently tracking feature points of interest across multiple images using a methodology first described by Lucas-Kanade, a 3-D s
det_code-1.0beta
- The main part of the code uses improved Shape Context as feature descr iptor and fit into a Hough Voting framework to detect objects. It can be used for initial hypothesis proposal. I hope you find this code helpful!
tuxianpipei-dabao
- MATLAB图像配准源程序,初学者一起研究.图像配准的程序，在网上流传很多，本人把能找到的都打包.-MATLAB source image registration, novices together. Step one, calculated to be scale-space image registration and to obtain scale-space Harris corner step 2, using affine shape modification technolog
morp
- 提取图像的形状特征的一个matlab程序-A code of matlab to extract a ROI s shape feature
HOG
- shape feature extraction of Histogram of Gradient
featureextraction
- 特征提取提取了图像中各个标记区域的纹理特征及形状特征-Feature extraction to extract texture features and shape features of each labeled region in the image
hog1
- Histogram of Oriented Gradients (HOG) are feature descr iptors used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method i
mn4op8.ZIP
- 商标检索中形状特征描述的研究Trademark retrieval of shape feature descr iption research-Trademark retrieval of shape feature descr iption research
kPCA_v2.0
- Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensiona
P_zernike_demos
- 利用HU不变矩提取图像的特征，主要是形状特征，HU不变矩具有旋转缩放不变性-HU invariant moments using the image feature extraction, mainly shape characteristics, HU invariant moments with rotation scaling invariance
MUSIC
- MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原
PSO
- Rosenbrock函数优化属于无约束函数优化问题，其全局极小值位于一条平滑而狭长的抛物线形状的山谷底部，且为优化算法提供的信息很少，因此找到其全局极小值就显得很困难。根据Rosenbrock函数的这种特性，专门提出了一种改进的PSO算法，该算法引入三角函数因子，利用三角函数具有的周期振荡性，使每个粒子获得较强的振荡性，扩大每个粒子的搜索空间，引导粒子向全局极小值附近靠近，避免算法过早地收敛，陷入局部最优，从而找到Rosenbrock函数的全局极小值。大量实验结果表明，该算法具有很好的优化性能，
pujulei
- 谱聚类算法建立在谱图理论基础上，与传统的聚类算法相比，它具有能在任意形状的样本空间上聚类且收敛于全局最优解的优点。 该算法首先根据给定的样本数据集定义一个描述成对数据点相似度的亲合矩阵,并且计算矩阵的特征值和特征向量 ， 然后选择合适 的特征向量聚类不同的数据点。谱聚类算法最初用于计算机视觉 、VLS I 设计等领域， 最近才开始用于机器学习中，并迅速成为国际上机器学习领域的研究热点。-Spectral clustering algorithm based on the spectrum b
RBF
- Radial Basis Function interpolation-RBF was originally developed for scattered multivariate data interpolation (Hardy 1971). It uses a series of basis functions that are symmetric and centred at each sampling point. Radial basis functions are a s
multi-feature-for-texture-and-shape
- MULTI FEATURE IMAGE RETREIVAL MATLAB CODE. COLOR TEXTURE AND SHAPE IMAGE SEARCH
RBF
- 径向基函数插值使用一系列基函数，它们在每个采样点对称且居中。径向基函数是一类特殊的函数，其主要特征是它们的响应与中心点的距离单调地减小（或增加）。中心、距离刻度和精确的形状是模型的参数。(Radial Basis Function interpolation with biharmonic, multiquadric, inverse multiquadric, thin plate spline, and Gaussian basis functions for Matlab/Octave.
Shape feature extraction
- 图像的形状特征一般是在物体从图像中分割出来以后进行分析，形状特征描述与尺寸测量结合起来可以作为区分不同物体的依据。形状特征有两类表示方法，一类是边界特征，另一类是区域特征。图像的轮廓特征主要针对物体的外边界，而图像的区域特征则关系到整个形状区域。(The shape feature of an image is generally analyzed after the object is segmented from the image. The combination of shape fea