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I see HOG is often used with SVM for target detection, can it be used in matching keypoints in two images?
and btw, where could I find OpenCV sample of using HOGDescriptor?
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The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection.
In this applica- tion keypoint matching is primarily used for finding the correct orientation and approximate position of aerial images that have been “geolocated” with a single GPS position but that do not have a known orientation.
This is done by extracting the gradient and orientation (or you can say magnitude and direction) of the edges. Additionally, these orientations are calculated in 'localized' portions. This means that the complete image is broken down into smaller regions and for each region, the gradients and orientation are calculated ...
What is it? Histogram of Oriented Gradients, also known as HOG, is a feature descriptor like the Canny Edge Detector, SIFT (Scale Invariant and Feature Transform) . It is used in computer vision and image processing for the purpose of object detection.
HOG can be used without SVM for feature matching.
just choose some points ( edge, for example ) and calculate the feature of HOG inside ROI with those points centered.
HOGDescriptor seems only for GPU programming.
I created Descriptor for HOG as a Mat in openCV and it also works for OpenCV matching functions.
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If you are working with images you can use SIFT/SURF with SVM. There is nothing that stops you from using HOG for keypoint matching, but bear in mind that the effectiveness depends on discrimination power and robustness of the descriptor.
Edit: My bad in understanding when I originally mentioned HOG being for video only. Somehow I was thinking about histogram of optical flow vectors which is very effective for video activity description.
Edit 2 [Oct '12]: I now suggest people to try ORB or BRISK for those looking for license friendly descriptors that are fast and quite effective for keypoint matching.
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