snake

图像边缘检测，实现了几种边缘检测算法，像Roberts，Sobel，Prewitt，Canny等(Edge detection, edge detection algorithm to achieve several, such as Roberts, Sobel, Prewitt, Canny, etc.)

基于序贯蒙特卡罗方法的人体跟踪程序，对于学习蒙特卡罗粒子滤波器的人有重要参考价值。(Sequential Monte Carlo method based on human tracking procedures, Monte Carlo particle filter for learning a person has an important reference value.)

marburg.c 用Burg算法求AR模型的参数。 mar1psd.c 由AR模型参数得到功率谱。 maryuwa.c 用Levinson算法求解Yule-Walker方程以得到 阶AR模型的参数 。 (marburg.c using Burg algorithm for AR model parameters. mar1psd.c by the AR model parameters obtained power spectrum. Levinson algorithm maryuwa.c with Yule-Walker equation in order to get rank AR model parameters.)

人耳识别，包含图像预处理，图像特征点的提取，比较实用(Ear recognition)

基于蚁群算法的图像边缘检测，将蚁群算法应用于边缘检测,提出了一种图像边缘检测的新方法，具有很大的创新性，最主要是可以仿真，供本研究方向的人参考( Image Edge Detection based on ant colony algorithm)

说明：  联合特征选择和子空间相关性学习的跨模态检索，map优于典型相关性分析的跨模态检索(Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval)

数字图像插值增减算法。数字图像插值增减算法。数字图像插值增减算法 (reduce)

说明：  基于Haar+AdaBoost+bdd100k数据集做车辆识别(Vehicle recognition based on Haar + AdaBoost + bdd100k dataset)

绣花问题是连续型的填充，在不同区域的缝制过程中，缝针不能提针，因此不能用一般的离散型区 域填充方法处理。本文运用几何计算和图论理论提出了一个绣花缝针轨迹自动生成算法：先对轮廓走向进行 定义，通过轮廓铅垂方向的局部极值点的分割线将图案从上向下进行分割，采用交点的特征值比较彻底的解 决了分割时的重点问题，将图案区域准确的划分成缝针能一次完成的若干个节点。在此基础上根据节点的邻 接关系建立节点的邻接“图”，通过图论中半哈密尔顿路径或深度遍历方法找到节点的遍历（缝制）序列和每 个节点的缝制方向，最后可对设定的起点、终点和缝针间隔的条件自动提供缝针的走向轨迹。(In the process of embroidering, needle can not hang up for changing to a different area. Therefore, Embroider problem could be abstract as a continuous area-filling work. Considering disperse area-filling methods can not process this problem well. Based on geometry computing and graph theory, an algorithm of finding path of embroidering needle are proposed in this paper. First, outline orientations are defined, and finding all local extreme points of inner outline on their gravitational orientation to build sectioning lines so as to divide the picture. By using intersection point character the overlap point problem was solved perfectly. Therefore, the picture is divided into some nodes which can be finished alone. Then, based on connections of these nodes, an adjacency graph of nodes was built. Using half-Hamilton path or depth-first search method, both embroidering sequence and direction of these nodes could be got from the graph. Finally, from a defined start-point, end-point)

本篇论文主要介绍地基云的自动检测！采用阈值分割！(This paper describes the automatic detection of the foundation of cloud! Using thresholding!)