Ask Your Question

Buck's profile - activity

2017-03-03 05:51:34 -0500 received badge  Famous Question (source)
2016-05-26 01:30:18 -0500 received badge  Notable Question (source)
2015-10-23 07:40:08 -0500 received badge  Popular Question (source)
2014-07-01 23:03:24 -0500 commented answer How to detect individual body parts in an image

Another area I have recently been researching is using a Feature Detection & Description though the likes of KAZE, BRISK, &/or FREAK (among other options). Since the areas you mentioned above aren't directly available in OpenCV, will utilizing say Feature Detection/Description + BOW be a viable alternative?

2014-07-01 23:01:43 -0500 received badge  Scholar (source)
2014-06-30 09:59:55 -0500 received badge  Student (source)
2014-06-29 14:28:16 -0500 received badge  Supporter (source)
2014-06-29 14:24:11 -0500 asked a question How to detect individual body parts in an image


I would like to find a way to identify individual body part limbs in an image (ie such as Forearm or lower leg). 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). One solution I have tried so far to no avail is HOG detection for forearm identification. I used a 32x32 window with a variety of different input parameters but was never able to to retrieve accurate detection in images.


Lets focus on forearms for this discussion. A forearm can have multiple orientations, the primary distinct features probably being its contour edges. It is possible to have images of forearms that are pointing in any direction in an image, thus the complexity. 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. I suspect the issue is that the gradients produced by each positive image do not produce very consistent results when saved into the Histogram. I have reviewed many research papers on the topic trying to resolve or improve this, including the original from Dalal & Triggs [Link]: It also seems that the assumptions made for detecting whole humans do not necessary apply to detecting individual features (particularly the assumption that all humans are standing up seems to suggest HOG is not a good route for rotation invariant detection like that of forearms).


If possible, I would like to steer clear of any non-free solutions such as those pertaining to Sift, Surf, or Haar. I have also asked the same question on Stack Overflow:


What is a good solution to detecting rotation and scale invariant objects such as human limbs in an image? Particularly for this example, what would be a good solution to detecting all orientations of forearms in an image?