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Scale & Affine Invariant Interest Point Detectors

Scale & Affine Invariant Interest Point Detectors,10.1023/B:VISI.0000027790.02288.f2,International Journal of Computer Vision,Krystian Mikolajczyk,Cor

Scale & Affine Invariant Interest Point Detectors   (Citations: 1091)
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In this paper we propose a novel approach for detecting interest points invariant to scale and affine transformations. Our scale and affine invariant detectors are based on the following recent results : (1) Interest points extracted with the Harris detector can be adapted to affine transformations and give repeatable results (geometrically stable). (2) The characteristic scale of a local structure is indicated by a local extremum over scale of normalized derivatives (the Laplacian). (3) The affine shape of a point neighborhood is estimated based on the second moment matrix. Our scale invariant detector computes a multi-scale representation for the Harris interest point detector and then selects points at which a local measure (the Laplacian) is maximal over scales. This provides a set of distinctive points which are invariant to scale, rotation and translation as well as robust to illumination changes and limited changes of viewpoint. The characteristic scale determines a scale invariant region for each point. We extend the scale invariant detector to affine invariance by estimating the affine shape of a point neighborhood. An iterative algorithm modifies location, scale and neighborhood of each point and converges to affine invariant points. This method can deal with significant affine transformations including large scale changes. The characteristic scale and the affine shape of neighborhood determine an affine invariant region for each point. We present a comparative evaluation of different detectors and show that our approach provides better results than existing methods. The performance of our detector is also confirmed by excellent matching results; the image is described by a set of scale/affine invariant descriptors computed on the regions associated with our points.
Journal: International Journal of Computer Vision - IJCV , vol. 60, no. 1, pp. 63-86, 2004
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    • ...We begin by overviewing the Harris-Affine and the Hessian-Affine region detectors [34]...
    • ...Weexaminetheperformanceofsevendifferentinterest-point and saliency detectors (the Harris-Affine and Hessian-Affine detectors [34], Kadir and Brady’s detector [35], the MSER detector [36], SURF’s detector [37], the Itti-Koch-Niebur saliency algorithm [5], and the AIM saliency algorithm [6]), under variable illumination and simultaneous variations in the camera’s shutter/gain values, using their publicly available implementations...

    Alexander Andreopouloset al. On Sensor Bias in Experimental Methods for Comparing Interest-Point, S...

    • ...In order to compensate for local perspective distortions, ane adaptation of features is used in Hessian-Ane and Harris-Ane detectors [21].,To determine the best feature detector for our application, we evaluated a variety of popular feature detectors (MSER, Laplace, Harris, FAST-ER, Hessian, Hessian-Laplace, Hessian-Ane, and Edge Foci) [21, 28, 29]...

    Sudipta N. Sinhaet al. Detecting and Reconstructing 3D Mirror Symmetric Objects

    • ...Mikolajczyk and Schmid (2004, 2005) used a multiscale version of the Harris interest point detector to localize interest points in space...

    Li Wanget al. A robust multisource image automatic registration system based on the ...

    • ...The Harris-Laplace interest region detector [20] uses a...

    C. G. M. Snoeket al. The MediaMill TRECVID 2011 semantic video search engine

    • ...Mikolajczyk and Schmid (2004) is theoretically related to LOWE, as it is also based on the theory of Lindeberg (1998b) and relies on the second derivatives of the image function over scale space...
    • ...The Harris affine (HARAF) detector (Mikolajczyk and Schmid 2004) computes the structure tensor on multiple scales to detect 2D extrema within each scale...
    • ...LAP) of Mikolajczyk and Schmid (2004) as well as their affine covariant extensions (HARAF, HESAF), – the Maximally Stable Extremal Regions detector (MSER) by Matas et al. (2004), – the intensity-based region detector (IBR) by Tuytelaars and Van Gool (2004), – the Edge-Laplace detector (EDGELAP) by Mikolajczyk et al. (2003), and – the salient regions detector (SALIENT) of Kadir and Brady (2001)...

    Timo Dickscheidet al. Coding Images with Local Features

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