But, it is very useful for tasks like image recognition and object detection. Let us calculateThe HOG descriptor of an image patch is usually visualized by plotting the 9×1 normalized histograms in the 8×8 cells. For the patch above, it looks like thisIn our representation, the y-axis is 0 degrees. 最近要做图像特征提取，可能要用下HOG特征，所以研究了下OpenCV的HOG描述子。OpenCV中的HOG特征提取功能使用了HOGDescriptor这个类来进行封装，其中也有现成的行人检测的接口。然而，无论是OpenCV官方说明文档还是各个中英文网站目前都没有这个类的使用说明，所以在这里把研究的部分心得分 … Hog特征提取 openCV. Let’s first focus on the pixel encircled in blue. See image on the side. The histogram contains 9 bins corresponding to angles 0, 20, 40 … 160.The following figure illustrates the process.
If you continue to use this site we will assume that you are happy with it. A bin is selected based on the direction, and the vote ( the value that goes into the bin ) is selected based on the magnitude. By the end of this section we will see how these 128 numbers are represented using a 9-bin histogram which can be stored as an array of 9 numbers. Typically patches at multiple scales are analyzed at many image locations. I have deliberately left out the image showing the direction of gradient because direction shown as an image does not convey much.The gradient image removed a lot of non-essential information ( e.g. In other words, you can look at the gradient image and still easily say there is a person in the picture.At every pixel, the gradient has a magnitude and a direction. For example, they can be 100×200, 128×256, or 1000×2000 but not 101×205.To illustrate this point I have shown a large image of size 720×475. But once you take the time to deconstruct them, the mystery is replaced by mastery and that is what we are after.
I've partnered with OpenCV.org to bring you official courses in We use cookies to ensure that we give you the best experience on our website. The length of this vector is Now that we know how to normalize a vector, you may be tempted to think that while calculating HOG you can simply normalize the 9×1 histogram the same way we normalized the 3×1 vector above. In this case, edge information is “useful” and color information is not. One of the important reasons to use a feature descriptor to describe a patch of an image is that it provides a compact representation. 第四个是 胞元cell大小 8 x 8. We can also achieve the same results, by using Sobel operator in OpenCV with kernel size 1. In addition, the features also need to have discriminative power. You will notice that dominant direction of the histogram captures the shape of the person, especially around the torso and legs.Unfortunately, there is no easy way to visualize the HOG descriptor in OpenCV.I am a co-founder of TAAZ Inc where the scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products. Gradients ( x and y derivatives ) of an image are useful because the magnitude of gradients is large around edges and corners ( regions of abrupt intensity changes ) and we know that edges and corners pack in a lot more information about object shape than flat regions.In this section, we will go into the details of calculating the HOG feature descriptor. The feature vector produced by these algorithms when fed into an image classification algorithms like Support Vector Machine (SVM) produce good results.But, what kinds of “features” are useful for classification tasks ? In our case, the patches need to have an aspect ratio of 1:2. HOG was used for pedestrian detection initially. the face, the top of the head etc. This post is part of a series I am writing on Image Recognition and Object Detection. 目标检测的图像特征提取之（一）HOG特征 这篇文章讲的比较透彻，可以参考。需要注意的是，这篇文章最后的例子计算结果是错误的，正确结果是2340。 OpenCV HOGDescriptor 参数图解 这篇文章通过图解的方式详细解释了一下window、block、cell的关系。 constant colored background ), but highlighted outlines. The magnitude of gradient fires where ever there is a sharp change in intensity. We are looking at magnitude and direction of the gradient of the same 8×8 patch as in the previous figure. Why not 32×32 ? OpenCV, PyTorch, Keras, Tensorflow examples and tutorialsIn this post, we will learn the details of the Histogram of Oriented Gradients (HOG) feature descriptor.