基于C++的通过HOG+SVM训练进行行人检测算法代码实现

图像边缘检测算法 代码程序 及其结果.pdf.pdf

角点检测算法源码，包括harris，susan，hough，对于学习计算机视觉的同学很有用

图像边缘检测算法代码，非常详细的算法代码。

Hough Forest目标检测由Juergen Gall在2009的CVPR上提出。作者给出的源码是基于linux系统的，在这里做了相应的修改使其能够在win系统上能够正常工作，只上传了修改后的代码及测试数据，需要自己另外配置opencv。我的环境是64位Win7+vs2010+opencv2.4.9。

RGB图像的对称轴检测算法代码实现

代码可用

MATLAB实现的一个图像信息隐藏检测算法，可以检测jpeg图像隐藏的信息

Haar+Adaboost 车辆检测 目标检测（视频车辆检测算法代码）-附件资源

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1,000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR