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There are several kinds of detectors and descriptors, like SIFT, SURF, FAST. I wonder are they all eligible for real-time applications? Which is the best or better?
And furthermore, is Harris-Laplacian dectector still useful when we already have the above three? Is it better than them?
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Feature detectors are used to find the essential features from the given image, whereas descriptors are used to describe the extracted features. Moravec introduced an interest operator based on intensity variations in 1980 [72]. But it was not scale invariant and rotation invariant. It was sensitive to noise too.
Feature detection is a method to compute abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. Feature detection is a low-level image processing operation.
In the area of psychology, the feature detectors are neurons in the visual cortex that receive visual information and respond to certain features such as lines, angles, movements, etc. When the visual information changes, the feature detector neurons will quiet down, to be replaced with other more responsive neurons.
Examples are the Trail Making Test, Stroop Color–Word Interference Test, and Complex Figure Test.
I can advise you to use Hessian-Affine and MSER for detection, if you need invariance to different factors (e.g., viewpoint change) or FAST, if you need real time. FAST is doing similar job to the Harris, but much faster.
You can look into "Local Invariant Feature Detectors: A Survey", and "A Comparison of Affine Region Detectors" where many detectors are tested and described very well.
Update: "WxBS: Wide Baseline Stereo Generalizations" does extended benchmark of the novel and classical detectors and descriptors.
Second, the description part is usually slower than detection, so to be real-time you have to use GPU or binary descriptor like BRIEF or FREAK.
Update2: "HPatches (Homography Patches) dataset and benchmark" and corresponding workshop at ECCV 2016. http://www.iis.ee.ic.ac.uk/ComputerVision/DescrWorkshop/index.html .
Update3: "Comparative Evaluation of Hand-Crafted and Learned Local Features" Descriptors (and a bit detectors) evaluation on large-scale 3D reconstruction task CVPR 2017 .
Update4: "Interest point detectors stability evaluation on ApolloScape dataset" Detector evaluation on authonomous driving dataset, ECCVW2018 .
Update5: "From handcrafted to deep local invariant features" Huuuge survey-overview paper about handcrafted and learned features, 2018.
Update6: "Image Matching across Wide Baselines: From Paper to Practice" Large scale benchmark of the abovementioned and more recent methods for the camera pose estimation. IJCV, 2020.
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My speed test for point detectors in OpenCV 3.0 using AVT Manta G-125C (1292x964)
Unit is FPS
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