头肩的定位检测采用了Haar特征和hog特征的层级分类方法，并根据头肩的对称性特点，提出了一种称为Joint hog的组合型特征。通过Haar分类器滤除大部分负样本后，接着用hog进行精细的验证从而得到头肩目标框。实验表明，本文的方法取得了80%~90%的准确率，并且完全可以用于实时处理。
上传时间： 2013-11-13
上传用户：weareno2
基于hog人体识别的很好的文章，既有基于adaboost的又有svm的分类器。
上传时间： 2013-12-24
上传用户：253189838
利用hog特征进行匹配，简单易懂，大家可以下载查看，图像处理图像匹配
标签： hog特征匹配
上传时间： 2017-11-15
上传用户：15637470600
The object detector described below has been initially proposed by P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a Dalal-Triggs detector that uses a single filter on histogram of oriented gradients (hog) features to represent an object category. This detector uses a sliding window approach, where a filter is applied at all positions and scales of an image. The first innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a “root” filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated deformation models. The score of one of star models at a particular position and scale within an image is the score of the root filter at the given location plus the sum over parts of the maximum, over placements of that part, of the part filter score on its location minus a deformation cost easuring the deviation of the part from its ideal location relative to the root. Both root and part filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of a feature pyramid computed from the input image. Another improvement is a representation of the class of models by a mixture of star models. The score of a mixture model at a particular position and scale is the maximum over components, of the score of that component model at the given location.
标签： 计算机视觉
上传时间： 2015-03-15
上传用户：sb_zhang
