Talk:Histogram of oriented gradients
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The paper by Dalal and Triggs must be cited in the introduction, not just somewhere below! — Preceding unsigned comment added by 80.156.43.137 (talk) 09:29, 9 December 2013 (UTC)
Introductory comment
editThis article is rather inaccessible to beginners. I would have done the changes myself, but I came to this article precisely because I was not sure I understand HOG features. For one, it doesn't say, in simple words, a HOG descriptor is so-and-so, and this is how they can be computed. The rest of the theory and motivation behind the HOG descriptor is quite useful, but without a concise description of what they are, this page sort of misses its purpose.
Orientation binning gradient image selection criteria for counting
editAs a matter of practical detail the computation of a histogram is easy--counting of things in range oriented bins, however, the text in the article does not say how the n histogram cells in 180 or 360 degrees have their gradient image points selected or found for counting?
- For what range of distances surrounding the given gradient image pixel are considered for selection?
- Do the counts get truncated as soon as a gradient image pixel is considered blank / background / too near to zero, etc.
- In the human detection system referred to, are these 9 directions for selection points the 8 surrounding pixel gradient values & the pixel itself, or more?
- Are the gradients selections compared to a given threshold prior to being counted (or not) into a bin?
The finding or selection criteria should be spelled out more clearly so that a reader does not have to guess how it is done or read & understand the scholarly paper(s) written on this topic.
Could some knowledgeable person please revise the text so as to clarify it in this regard? Oldspammer (talk) 20:42, 16 February 2012 (UTC)
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Needs an Example Image
editthere are no explanatory images in the article, which is about image processing. 135.180.116.182 (talk) 00:00, 11 February 2024 (UTC)