A SURF keypoint, represented as a tuple ((x, y), laplacian, size, dir, hessian) .
 x¶
xcoordinate of the feature within the image
 y¶
ycoordinate of the feature within the image
 laplacian¶
1, 0 or +1. sign of the laplacian at the point. Can be used to speedup feature comparison since features with laplacians of different signs can not match
 size¶
size of the feature
 dir¶
orientation of the feature: 0..360 degrees
 hessian¶
value of the hessian (can be used to approximately estimate the feature strengths; see also params.hessianThreshold)
Extracts Speeded Up Robust Features from an image.
Parameters: 


The function cvExtractSURF finds robust features in the image, as described in Bay06 . For each feature it returns its location, size, orientation and optionally the descriptor, basic or extended. The function can be used for object tracking and localization, image stitching etc.
To extract strong SURF features from an image
>>> import cv
>>> im = cv.LoadImageM("building.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE)
>>> (keypoints, descriptors) = cv.ExtractSURF(im, None, cv.CreateMemStorage(), (0, 30000, 3, 1))
>>> print len(keypoints), len(descriptors)
6 6
>>> for ((x, y), laplacian, size, dir, hessian) in keypoints:
... print "x=%d y=%d laplacian=%d size=%d dir=%f hessian=%f" % (x, y, laplacian, size, dir, hessian)
x=30 y=27 laplacian=1 size=31 dir=69.778503 hessian=36979.789062
x=296 y=197 laplacian=1 size=33 dir=111.081039 hessian=31514.349609
x=296 y=266 laplacian=1 size=32 dir=107.092300 hessian=31477.908203
x=254 y=284 laplacian=1 size=31 dir=279.137360 hessian=34169.800781
x=498 y=525 laplacian=1 size=33 dir=278.006592 hessian=31002.759766
x=777 y=281 laplacian=1 size=70 dir=167.940964 hessian=35538.363281
Retrieves keypoints using the StarDetector algorithm.
Parameters: 


The function GetStarKeypoints extracts keypoints that are local scalespace extremas. The scalespace is constructed by computing approximate values of laplacians with different sigma’s at each pixel. Instead of using pyramids, a popular approach to save computing time, all of the laplacians are computed at each pixel of the original highresolution image. But each approximate laplacian value is computed in O(1) time regardless of the sigma, thanks to the use of integral images. The algorithm is based on the paper Agrawal08 , but instead of a square, hexagon or octagon it uses an 8end star shape, hence the name, consisting of overlapping upright and tilted squares.
Each keypoint is represented by a tuple ((x, y), size, response) :
 x, y Screen coordinates of the keypoint
 size feature size, up to maxSize
 response approximated laplacian value for the keypoint