10  Feature detection, description and matching

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10.1 Interest-point detection

10.1.1 The Moravec detector and the corner concept

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10.1.2 Harris and Shi–Tomasi corners

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10.1.3 Scale-invariant detection: difference of Gaussians

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10.1.4 Hessian-based detectors

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10.1.5 FAST and AGAST corners for real-time use

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10.2 Feature description

10.2.1 SIFT descriptor

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10.2.2 SURF descriptor

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10.2.3 Binary descriptors: BRIEF, ORB, BRISK, FREAK

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10.2.4 Learned descriptors: SuperPoint and successors

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10.3 Feature matching

10.3.1 Distance metrics: L2, Hamming

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10.3.2 Brute-force and FLANN matching

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10.3.3 The Lowe ratio test and cross-check

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10.3.4 Robust matching with RANSAC and MSAC

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10.4 Template and pattern matching

10.4.1 Sum of squared and absolute differences

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10.4.2 Normalised cross-correlation

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10.4.3 Rotation- and scale-invariant template matching

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10.4.4 Geometric pattern matching (edge-model methods)

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10.5 Line, circle and shape detection

10.5.1 The Hough transform for lines

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10.5.2 The Hough transform for circles

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10.5.3 The generalised Hough transform

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10.5.4 Direct fitting: least-squares lines, circles, ellipses

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10.5.5 Robust fitting with RANSAC

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10.6 Choosing a feature method

10.6.1 Repeatability, invariance and speed trade-offs

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10.6.2 Hand-crafted versus learned features

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10.6.3 Matching the detector–descriptor pair to the task

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