搜索资源列表
gkp
- 这是用c#实现的david low的sift（尺度不变特征点变换算法）特征检测，面向对象的实现代码写的十分的好，是图形图象学习的好资料 -This is the realization of c # david low sift the (scale-invariant feature points transform algorithm) Feature detection, object-oriented code written in achieving very good, is s
MotionDetection.rar
- 利用emgucv 所開發的移動物體偵測可以偵測移動物體之方向即位移,Emgucv developed by use of a moving object detection can detect moving objects that is the direction of displacement
code.rar
- 视频运动物体检测，采用混合高斯分布建立背景模型及差分方法对背景模型进行更新,Sports video object detection, adopt a mixed Gaussian distribution model and set up the background difference method to update the background model
VideosTargetDetection.rar
- 1. 静态背景下的背景预测法目标检测2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法4. 重心多目标跟踪方法 ,1. Static background prediction in the context of target detection method 2. Static background frame difference method for target detection 3. Mean Shift Object Tracking Method 4.
capture.rar
- 运动物体检测与跟踪，绝对值得下载的程序，已经调试过的,Moving object detection and tracking, definitely worth downloading the program, has been testing
opticalflow.rar
- 用光流法进行运动物体的检测和跟踪，并返回运动方向,Light flow method of moving object detection and tracking, and return to the direction of movement
Addrectanglemotion.rar
- 利用质心法的OPenCV实现运动物体跟踪检测。非常实用。,OPenCV use of quality-force to achieve the detection of moving object tracking. Very useful.
multiple-object-detection
- 多目标检测，可用于视频监控，视觉目标跟踪，运动检测，及其它图像处理中的相关应用。-This code contains multiple object detection adaptable to visual surveillance, visual object tracking, motion estimation and other image processing applications.
Cascade-Object-Detection
- 人脸检测的最新论文-CVPR-2010,Cascade-Object-Detection-with-Deformable-Part-Models--Felzenszwalb-Girshick-McAllester-Face detection of the latest paper-CVPR-2010
MoveDetection
- 用VC编写的运动检测程序，实现运动物体的跟踪与检测，包括了数字图像处理的一些操作方法-VC prepared with motion detection process, the achievement of moving object tracking and detection, including a number of digital image processing method of operation
yundongshibie
- 用于运动物体的检测 原理是对图像的连续几帧进行差分，这样就可以实现运动的检测-For moving object detection principle is the image of several frame difference, so that movement can be detected
detection
- 背景减除法 Opencv 运动物体检测 ROI区域检测-Background subtraction method for moving object detection Opencv ROI region detection
detection-match
- 用visual c++开发的利用中心匹配算法实现的运动物体检测系统-Using visual c++ Development centers and use the matching algorithm to achieve the moving object detection system
BackgroundSubtractionLibrary
- 基于混合高斯模型的背景消除 利用混合高斯背景建模进行运动物体检测, 同时引入共轭先验以改进权值更新方程-Gaussian mixture model based on the background to eliminate the use of Gaussian mixture background modeling for moving object detection, while the introduction of conjugate a priori weights to imp
cell-get
- 边缘提取算法提取细胞轮廓 适用于前景背景反差巨大-An object can be easily detected in an image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a prostate cancer cell.
Cross-Correlation-Target-Image-Detection
- 正交相关目标检测，采用正交相关方法检测指定目标在图像中的位置。适用于计算机视觉中的视频目标检测、视觉目标检测、目标定位、视觉目标跟踪、视频目标跟踪、图像匹配、图像配准等工作。-cross relation detection is used to detect object in image for in the field computer vision such as visual object detection, motion detection, object localization
saliency-object-or-region-detection
- 该文件夹包括代码及其对应的论文。其作用在于模拟人类视觉系统的生理特性--视觉注意机制，按照人眼观察外界的方式，检测视觉显著性物体和区域，并阐述了显著性区域的显著性密度和尺度之间的关系，可应用于生物视觉模拟、视觉目标检测、视觉目标跟踪、视觉智能监控，以及视觉生理学和视觉心理学等的研究中。-This document contains codes and the corresponding paper. The aim is to simulate a physiological character
Object-based-image-analysis
- 面向对象的遥感图像变化监测算法-英文参考文献，对专业人士一定有帮助-Object-oriented remote sensing image change detection algorithm- English references, will certainly be helpful for professionals
Edge-Detection
- An Augmented Reality app that demonstrates basic computer vision concepts such as greyscaling, thresholding, edge detection, homography, corner detection...its a long list. It paints a 3D image on any detected markers. Here is a crude video that
OpenCV_By_Example(中文版)
- 该资料中包含了《OpenCV By Example》中文版以及例程程序，该书的目录如下所示： 第1章 OpenCV的探险之旅; 第2章 OpenCV基础知识介绍; 第3章 图形用户界面和基本滤波; 第4章 深入研究直方图和滤波器; 第5章 自动光学检测、目标分割和检测; 第6章 学习目标分类; 第7章 识别人脸部分并覆盖面具; 第8章 视频监控、背景建模和形态学操作; 第9章 学习对象跟踪; 第10章 文本识别中的分割算法; 第11章 使用Tessera