![]() I suspect the issue is that the gradients produced by each positive image do not produce very consistent results when saved into the Histogram. So far I have done some in depth research on using HOG descriptors to solve this problem, but I am finding that the variety of poses produced by forearms in my positives training set is producing very low detection scores in actual images. ![]() It is possible to have images of forearms that are pointing in any direction in an image, thus the complexity. ![]() A forearm can have multiple orientations, the primary distinct features probably being its contour edges. Lets focus on forearms for this discussion. I used a 32x32 window with a variety of different input parameters but was never able to to retrieve accurate detection in images. One solution I have tried so far to no avail is HOG detection for forearm identification. The only problem being that I can't seem to find a reasonable feature detector or classifier to detect this in a rotation and scale invariant way (as is needed by objects such as forearms). ![]() I would like to find a way to identify individual body part limbs in an image (ie such as Forearm or lower leg). ![]()
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