daisy(image, step=4, radius=15, rings=3, histograms=8, orientations=8, normalization='l1', sigmas=None, ring_radii=None, visualize=False)
DAISY is a feature descriptor similar to SIFT formulated in a way that allows for fast dense extraction. Typically, this is practical for bag-of-features image representations.
The implementation follows Tola et al. but deviate on the following points:
Histogram bin contribution are smoothed with a circular Gaussian window over the tonal range (the angular range).
The sigma values of the spatial Gaussian smoothing in this code do not match the sigma values in the original code by Tola et al. . In their code, spatial smoothing is applied to both the input image and the center histogram. However, this smoothing is not documented in and, therefore, it is omitted.
Input image (grayscale).
Distance between descriptor sampling points.
Radius (in pixels) of the outermost ring.
Number of rings.
Number of histograms sampled per ring.
Number of orientations (bins) per histogram.
How to normalize the descriptors
'l1': L1-normalization of each descriptor.
'l2': L2-normalization of each descriptor.
'daisy': L2-normalization of individual histograms.
'off': Disable normalization.
Standard deviation of spatial Gaussian smoothing for the center histogram and for each ring of histograms. The array of sigmas should be sorted from the center and out. I.e. the first sigma value defines the spatial smoothing of the center histogram and the last sigma value defines the spatial smoothing of the outermost ring. Specifying sigmas overrides the following parameter.
rings = len(sigmas) - 1
Radius (in pixels) for each ring. Specifying ring_radii overrides the following two parameters.
rings = len(ring_radii)radius = ring_radii[-1]
If both sigmas and ring_radii are given, they must satisfy the following predicate since no radius is needed for the center histogram.
len(ring_radii) == len(sigmas) + 1
Generate a visualization of the DAISY descriptors
Grid of DAISY descriptors for the given image as an array dimensionality (P, Q, R) where
P = ceil((M - radius*2) / step)Q = ceil((N - radius*2) / step)R = (rings * histograms + 1) * orientations
Visualization of the DAISY descriptors.
Extract DAISY feature descriptors densely for the given image.
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
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