资源列表
endpoints
- 骨骼化的结果中会有一些毛刺,我们可以对起进行修剪,使用endpoints函数实现-Bones of the results there will be some glitches, we can start pruning, using endpoints function implementation
Pattern-Recognition
- 用于模式识别，一维和二维的数据分类及提取特征方程和分类边界。-For pattern recognition, one-dimensional and two-dimensional data classification and feature extraction and classification boundary equation.
frdescp
- 傅立叶描述子(frdescp()函数)来表示各个数字包络-Fourier descr iptors (frdescp () function) to represent each digital envelope
bayesgauss
- 图像处理MATLAB源代码，这个是贝叶斯估计-bayes gue-Image Processing MATLAB source code, this is the Bayesian estimation-bayes guess
adpmedian
- matlab数字图像处理中的自适应中值滤波函数-digital image processing matlab adaptive median filter function
Draw
- 实现了图形绘制功能和文字处理功能，图形绘制功能包括绘制直线，绘制矩形，绘制圆形，绘制Bezier曲线。文字处理功能包括保存和加载。-Achieve a graphical rendering capabilities and word processing functions, graphics rendering features include draw lines, draw a rectangle, draw a circle, draw Bezier curves. Word proc
HW4
- 基于C++MFC下的，已知起始角度与终止角度，圆心，半径下画任意圆弧的算法实现-Based C++MFC under, known starting point and ending point, circle, arc radius painting arbitrary algorithm
NSCT
- 该文基于非下采样Contourlet变换(NSCT)和SAR图像的统计特性，提出一种SAR图像增强方法，给出一种基于非下采样塔型分解的斑点噪声方差估计算法和一种基于方向邻域模型的弱边缘增强算法-Based on nonsubsampled Contourlet transform and SAR image statistical property, a SAR image enhancement method is proposed. A speckle noise variance esti
houghcircles_vs10
- 图形检测，hough变换检测直线、圆、椭圆。-Pattern detection, hough transform to detect straight line, circle, ellipse.
HMT
- 针对含噪图像增强问题, 提出一种基于小波域三状态隐马尔可夫树模型的方法 -For noise image enhancement problems, and puts forward three state based on wavelet domain hidden markov tree model method
NSCTPCNN
- 提出了一种新的基于非下采样轮廓波ＮＳＣＴ和脉冲耦合神经网络ＰＣＮＮ相结合的自适应图像融合方法．对已经配准的源图像进行ＮＳＣＴ分解得到低频子带系数和不同方向的高频子带系数．对ＮＳＣＴ分解的低频部分采用简单的加权平均融合规则-Proposed a new profile based on non-downsampling wave NSCT and PCNN PCNN combining adaptive image fusion method. Which has been the source
Contourlet-HMT
- 基于 系数分布统计特性，结合隐马尔可夫树模型和贝叶斯准则提出一种新的图像分割算法 -Based on statistical properties of the distribution coefficient combined with hidden Markov tree model and Bayesian guidelines propose a new image segmentation algorithm