The DAISY local image descriptor is based on gradient orientation histograms similar to the SIFT descriptor. It is formulated in a way that allows for fast dense extraction which is useful for e.g. bag-of-features image representations.
In this example a limited number of DAISY descriptors are extracted at a large scale for illustrative purposes.
from skimage.feature import daisy from skimage import data import matplotlib.pyplot as plt img = data.camera() descs, descs_img = daisy(img, step=180, radius=58, rings=2, histograms=6, orientations=8, visualize=True) fig, ax = plt.subplots() ax.axis('off') ax.imshow(descs_img) descs_num = descs.shape * descs.shape ax.set_title('%i DAISY descriptors extracted:' % descs_num) plt.show()
Python source code: download (generated using skimage 0.10.0)
IPython Notebook: download (generated using skimage 0.10.0)