多视K-means聚类（MKC）聂飞平

阵列信号处理的理论和应用的pdf以及书中的代码 对于研究阵列信号的同学很有用

【实例简介】

D-S 多传感器信息融合 matlab实现.rar

帧差分法（matlab实现）

二维码图像定位、摆正算法。可以用于摄像头捕捉的二维码而不仅仅是原图保存的二维码。算法找到二维码的3个Finder Points后进一步用向量算法找到二维码正方形的4个顶点，再由投影变换矫正图形，并用Zxing解码。

【实例简介】免疫粒子群算法优化多个参数，速度快，精度高

【实例简介】

用粒子群算法对RBF网络参数初值的估计

【实例简介】以单关节机械手为例，实现阻抗控制

论文《As-Projective-As-Possible Image Stitching with Moving DLT》中的拼接算法，对于视差图像拼接具有一定的鲁棒性，但是对特征点数量及其分布均匀性有较高的要求。 The success of commercial image stitching tools often leads to the impression that image stitching is a “solved problem”.The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumptions;the main two being that the photos correspond to views that differ purely by rotation, or that the imaged scene is effectively planar.Such assumptions underpin the usage of 2D projective transforms or homographies to align photos. In the hands of the casual user,such conditions are often violated, yielding misalignment artifacts or “ghosting” in the results. Accordingly, many existing imagestitching tools depend critically on post-processing routines to conceal ghosting. In this paper, we propose a novel estimationtechnique called Moving Direct Linear Transformation (Moving DLT) that is able to tweak or fine-tune the projective warp toaccommodate the deviations of the input data from the idealized conditions. This produces as-projective-as-possible image alignmentthat significantly reduces ghosting without compromising the geometric realism of perspective image stitching. Our technique thuslessens the dependency on potentially expensive postprocessing algorithms. In addition, we describe how multipleas-projective-as-possible warps can be simultaneously refined via bundle adjustment to accurately align multiple images for largepanorama creation.