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4.1 Points and patches 191

(a) Strongest 250 (b) Strongest 500

(c) ANMS 250, r =24 (d) ANMS 500, r =16
Figure 4.9 Adaptive non-maximal suppression (ANMS) (Brown, Szeliski, and Winder 2005) c 2005 IEEE:
The upper two images show the strongest 250 and 500 interest points, while the lower two images show the
interest points selected with adaptive non-maximal suppression, along with the corresponding suppression radius
r. Note how the latter features have a much more uniform spatial distribution across the image.

Scale invariance

In many situations, detecting features at the finest stable scale possible may not be appro-
priate. For example, when matching images with little high frequency detail (e.g., clouds),
fine-scale features may not exist.
One solution to the problem is to extract features at a variety of scales, e.g., by performing
the same operations at multiple resolutions in a pyramid and then matching features at the
same level. This kind of approach is suitable when the images being matched do not undergo
large scale changes, e.g., when matching successive aerial images taken from an airplane or
stitching panoramas taken with a fixed-focal-length camera. Figure 4.10 shows the output of
one such approach, the multi-scale, oriented patch detector of Brown, Szeliski, and Winder
(2005), for which responses at five different scales are shown.
However, for most object recognition applications, the scale of the object in the image
is unknown. Instead of extracting features at many different scales and then matching all of
them, it is more efficient to extract features that are stable in both location and scale (Lowe
2004; Mikolajczyk and Schmid 2004).
Early investigations into scale selection were performed by Lindeberg (1993; 1998b),
who first proposed using extrema in the Laplacian of Gaussian (LoG) function as interest