SIFT image alignment tutorial

SIFT (Scale-Invariant Feature Transform) is an algorithm developped by David Lowe in 1999. It is a worldwide reference for image alignment and object recognition. The robustness of this method enables to detect features at different scales, angles and illumination of a scene.

Silx provides an implementation of SIFT in OpenCL, meaning that it can run on Graphics Processing Units and Central Processing Units as well. Interest points are detected in the image, then data structures called descriptors are built to be characteristic of the scene, so that two different images of the same scene have similar descriptors. They are robust to transformations like translation, rotation, rescaling and illumination change, which make SIFT interesting for image stitching.

In the fist stage, descriptors are computed from the input images. Then, they are compared to determine the geometric transformation to apply in order to align the images. This implementation can run on most graphic cards and CPU, making it usable on many setups. OpenCL processes are handled from Python with PyOpenCL, a module to access OpenCL parallel computation API.

This tutuorial explains the three subsequent steps:

• keypoint extraction

• Keypoint matching

• image alignment

All the tutorial has been made using the Jupyter notebook.

import time
start_time = time.time()
%pylab nbagg

Populating the interactive namespace from numpy and matplotlib

# display test image
import silx
print("Silx version %s"%silx.version)

from PIL import Image
from silx.test.utils import utilstest
path = utilstest.getfile("lena.png")
image = numpy.asarray(Image.open(path))
fig, ax = subplots()
ax.imshow(image)

Silx version 0.7.0-dev0