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What is the difference between a feature detector and a dense detector in computer vision? Are they the same or a subset of one another?
For the following algorithms (implemented in OpenCV), which of them are feature detectors and/or dense detectors?
For class matching, Hietanen in [29] compares several binary descriptors and SIFT with different detectors, including a dense grid. SIFT preformed better than other descriptors and dense grid responds very well.
This result leads us to test dense detector for different descriptors…
It seems that a dense detector is one that uses a dense grid on which to evaluate some feature metric.
This is opposed to "classical" feature detectors that search the image for points with features.
Frankly, spanning a grid over an image to reduce it to a lower resolution does sound like what I'd call a subsampling and image transform, but maybe I'm misunderstanding this!
