实现图像变换与目标跟踪应用OPENCV的源程序

能实现多种边缘检测方法，包括Prewitt、Roberts、Sobel和高斯拉普拉斯-To achieve a variety of edge detection methods, including Prewitt, Roberts, Sobel and Laplacian of Gaussian

实现了1D,2D,3D空间自由度下的运动参数的扩展Kalman滤波算法，文章2008 IROS Visual SLAM for 3D Large-Scale Seabed Acquisition Employing Underwater Vehicles的具体实现。-a novel technique to align partial 3D reconstructions of the seabed acquired by a stereo camera mounted on an autonomous underwater vehicle. Vehicle localization and seabed mapping is performed simultaneously by means of an Extended Kalman Filter. Passive landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot is navigating and data association becomes easier but robust. Once the survey is completed, vehicle trajectory is smoothed by a Rauch-Tung-Striebel filter obtaining an even better alignment of the 3D views and yet a large-scale acquisition of the seabed.

线特征的hough提取程序，很不错的，自己试过，绝对可用-Line Features hough the process of extraction, very good that he tried to absolutely available

自己编的图像水平集分割matlab方法，已经成功运行，中间辅以形态学算子-Own level set image segmentation matlab methods, have successfully run the middle accompanied by morphological operators

基于Hough圆变换算法，在图像中寻找圆形-Based on the circular Hough transform algorithm to find the image circle

区域生长: 标注详细,参数可在程序中调整-Regional Growth: tagging detailed parameters can be adjusted in the procedure

视觉神经科学中一个重要的研究方法就是建立初级视觉系统功能与自然图像的统计特性之间的关系，通过对自然图像的有效编码（稀疏性）可以模拟初级视觉皮层的一些基本属性，包括简单、复杂细胞的感受野，细胞的空间组织结构等。本课题旨在研究如何建立一个高阶神经网络结构，通过学习过程稀疏的表示复杂细胞对自然图像块的响应，进而实现对图像轮廓的编码以及模型自上而下的推理。-use ica and gabor filter to simulate the visula system of human

BlurTemplate.m为模糊模板函数子程序 DIPexp2.m为用逆滤波和维纳滤波进行恢复的源程序-Template function for the fuzzy BlurTemplate.m Subroutine DIPexp2.m using inverse filtering and Wiener filtering for the restoration of the source

256色转灰度图 256色转灰度图-256-color gray level 256 to switch to 256 colors change colors change grayscale grayscale

一种基于相位的光流计算方法，该方法不同于以往基于微分的计算方法，而是采用空间滤波器，取得非常好的效果。该结果发表于IEEE Trans. Neural Networks,13(5), 1127--1136. -Gautama, T. and Van Hulle, M.M. (2002). A Phase-based Approach to the Estimation of the Optical Flow Field Using Spatial Filtering, IEEE Trans. Neural Networks, 13(5), 1127--1136.