Feature detection and description


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class CvSURFPoint

A SURF keypoint, represented as a tuple ((x, y), laplacian, size, dir, hessian) .


x-coordinate of the feature within the image


y-coordinate of the feature within the image


-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 of the feature


orientation of the feature: 0..360 degrees


value of the hessian (can be used to approximately estimate the feature strengths; see also params.hessianThreshold)


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ExtractSURF(image, mask, storage, params)-> (keypoints, descriptors)

Extracts Speeded Up Robust Features from an image.

  • image (CvArr) – The input 8-bit grayscale image
  • mask (CvArr) – The optional input 8-bit mask. The features are only found in the areas that contain more than 50 % of non-zero mask pixels
  • keypoints (CvSeq of CvSURFPoint) – sequence of keypoints.
  • descriptors (CvSeq of list of float) – sequence of descriptors. Each SURF descriptor is a list of floats, of length 64 or 128.
  • storage (CvMemStorage) – Memory storage where keypoints and descriptors will be stored
  • params (CvSURFParams) –

    Various algorithm parameters in a tuple (extended, hessianThreshold, nOctaves, nOctaveLayers) :

    • extended 0 means basic descriptors (64 elements each), 1 means extended descriptors (128 elements each)
    • hessianThreshold only features with hessian larger than that are extracted. good default value is ~300-500 (can depend on the average local contrast and sharpness of the image). user can further filter out some features based on their hessian values and other characteristics.
    • nOctaves the number of octaves to be used for extraction. With each next octave the feature size is doubled (3 by default)
    • nOctaveLayers The number of layers within each octave (4 by default)

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


Comments from the Wiki

GetStarKeypoints(image, storage, params) → keypoints

Retrieves keypoints using the StarDetector algorithm.

  • image (CvArr) – The input 8-bit grayscale image
  • storage (CvMemStorage) – Memory storage where the keypoints will be stored
  • params (CvStarDetectorParams) –

    Various algorithm parameters in a tuple (maxSize, responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize) :

    • maxSize maximal size of the features detected. The following values of the parameter are supported: 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128
    • responseThreshold threshold for the approximatd laplacian, used to eliminate weak features
    • lineThresholdProjected another threshold for laplacian to eliminate edges
    • lineThresholdBinarized another threshold for the feature scale to eliminate edges
    • suppressNonmaxSize linear size of a pixel neighborhood for non-maxima suppression

The function GetStarKeypoints extracts keypoints that are local scale-space extremas. The scale-space 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 high-resolution 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 8-end 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