speck

方向检测的粒子滤波程序，内含重采样算法。(The direction of the particle filter detection procedures, including re-sampling algorithm.)

能完成钢筋数数，图像分割，直线检测，边缘检测，很好用。(Can finish the reinforcing bar counting, image segmentation, straight line detection, edge detection and works well.)

自己用MATLAB写的一个基于多项式插值的亚像素处理算法，处理速度快(sub-pixel edge detect)

形态学成分分析，该方法的基本思想是利用信号组成成分的形态差异，将图像分割为纹理和分片光滑部分。(Morphological Component Analysis，The basic point is that an image can be divided into texture and piecewise smooth parts by the morphological diversity among signal components.)

高分辨率遥感影像的支持张量机分类方法，此方法能够利用少量的训练样本实现更优的分类精度。(Support tensor machines resolution remote sensing image classification method, and this method can use a small amount of training samples to achieve better classification accuracy.)

图片极坐标转换,可实现极坐标转换，以及求傅里叶变换(Picture polar coordinate conversion, convert polar coordinates can be realized, as well as seeking the Fourier transform)

说明：  视觉slam十四讲，程序资料：g2o优化、cere拟合、特征点提取与匹配、点云拼接、opencv图像读取(Visual slam 14, program data: g2o optimization, cere fitting, feature point extraction and matching, point cloud mosaic, OpenCV image reading)

发表于ECCV上的一篇用于人脸识别的算法，Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary (At ECCV on an algorithm for face recognition, Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary)

最小二乘图像影像匹配方法，一个很详细的源码例程，有过程有结果，希望对初学者有帮助。高斯解方程，以及最小二乘匹配，里面都很详细。(Squares image image matching method, a very detailed source code routines, the outcome of the process, I hope to help beginners. Gaussian solution of equations, and least squares matching, which is very detailed.)

Zernike矩是一种具有尺度、移位和旋转不变性的正交不变矩，本设计的目的就是利用Zernike不变矩设计一种图像检索系统，该系统能够充分验证Zerinike矩的不变性及其在图像检索中的优良性能。具体内容包括： (1) 图像特征提取、统计特征提取； (2) Zernike不变矩及其应用方法； (3) 基于Zernike不变矩的图像检索系统。 (Zernike moments is a scale, shift and rotation invariant orthogonal invariant moments, the purpose of this design is the use of Zernike invariant moments design an image retrieval system, the system can fully verify Zerinike moments invariance and itsexcellent performance in image retrieval. Specific content includes: (1) image feature extraction, statistical feature extraction (2) Zernike invariant moments and its application method (3) based on Zernike Moment Invariant image retrieval system)