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Extension of M-VOTE: Improving Feature Detection

Extension of M-VOTE: Improving Feature Detection. Presented by Wyman Group Meeting 24 Oct 2005. Outline. Feature Detection KLT detector SUSAN detector Rotational motion detection How zooming of camera enhances interaction with user. Feature Detection.

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Extension of M-VOTE: Improving Feature Detection

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  1. Extension of M-VOTE: Improving Feature Detection Presented by Wyman Group Meeting 24 Oct 2005

  2. Outline • Feature Detection • KLT detector • SUSAN detector • Rotational motion detection • How zooming of camera enhances interaction with user

  3. Feature Detection • To detect points of interest (feature) out of an image • Requirement on points of interest • High contrast and different from nearby blocks • Corners are used in motion analysis • Motion is ambiguous at an edge but unambiguous at a corner • Corner detectors • Plessey detector • KLT detector • Susan detector

  4. What Is a Corner? • Corners are local image features characterised by locations where variations of intensity function f(x, y) in both X and Y directions are high • Intersection points between two or more edge segments

  5. Corner Detectors • Corner detection should satisfy a number of important criteria: • All the true corners should be detected • No false corners should be detected • Corner points should be well localized • Corner detector should be robust with respect to noise • Corner detector should be efficient

  6. Families of Corner Detectors • Work directly with the values of brightness of images • Usually based on the study of derivatives (orientation, magnitude) of grey-level or color image • Extract object boundaries first then analyze its shape • Use edge detectors first then analyze the curvature of boundaries • Less reliable

  7. KLT Corner Detector • Search for points where variations in two orthogonal directions are large • Based on local structure matrix • Derivatives of the intensity function f(x, y) are calculated in each point • Then, the entries of the matrix are obtained • Each of the entries is smoothed by Gaussian filter

  8. KLT Corner Detector • The local structure matrix Cstr can be diagonalised by rotation of the coordinate axes. The diagonal entries will be the two eigenvalues • The eignevalues are nonnegative • Assume λ1>=λ2>=0 • For a perfectly uniform image: • Cstr = 0 and λ1=λ2=0 • For a perfectly black-and-white step edge: • λ1>0, λ2=0, where the eigenvector associated with λ1 is orthogonal to the edge • For a corner of black square against a white background: • λ1>=λ2>0. The higher contrast in that direction, the larger the eigenvalue • A corner is identified by two strong edges. That is, a corner is a location where the smaller eigenvalue,λ2 , is large enough

  9. KLT Corner Detector • The KLT corner detector has two parameters: the threshold on λ2 denoted by λthr, and the linear size of a square window D • The algorithm • Compute fx and fy over the entire image f(x, y) • For each image point p: • form the matrix Cstr over a DxD neighbourhood of p; • compute λ2, the smaller eigenvalue of Cstr; • if λ 2 >λthr, save the p into a list, L. • Sort L in decreasing order of λ2. • Scan the sorted list from top to bottom. For each current point, p, delete all points appearing further in the list, which belong to the neighbourhood of p

  10. KLT Corner Detector • The threshold λthr can be estimated from the histogram of λ2 • There is no simple criterion for the window size D. Values between 2 and 10 are adequate in most practical cases • But some corners which are close to each other may be lost for larger D

  11. Example of corner detection

  12. The SUSAN Detector • Proposed by Smith and Brady in 1995 • It doesn’t use any derivatives • SUSAN stands for Smallest “Univalue Segment Assimilating Nucleus” • It is based on the fact that each point within an image has associated with it a local area of comparable brightness • The Principle • It generates a circular mask around a given point in an image • It compares the intensity of neightbouring pixels with that of the centre pixel (nucleus of the mask), the area with similar intensity to the nucleus is called USAN area • Repeat the procedure for each pixel within the image

  13. The SUSAN Detector • USAN area varies within the image depending on its location with respect to special features of the image • The USAN area is maximum within the rectangular area but falls to a minimum at an edge and to an even smaller value corresponding to a local minimum at a corner • This is the property upon which the corner finder algorithm is based, hence the name SUSAN

  14. The SUSAN Detector • The algorithm • Determine a circular mask, typically of 37 pixels around a nucleus for each point within the image • Calculate the difference in brightness between each pixel of the mask and that of its nucleus and • Sum the number of pixels within the circular mask which have similar intensity levels to that of the nucleus

  15. The SUSAN Detector • Compare n with g, the geometric threshold which is set to half of the maximum value that n can be (nmax/2) • At a perfect corner (where two straight edges intersect) the USAN area will always be less than half the size of the mask area, and will be a local minimum

  16. The SUSAN Detector (Improved) • However, the above process may generate “false” corners • There are a couple of experiments to remove false positives arising from strong edges and noise • The first test consists in finding the centre of gravity of the USAN area • Only those pixels falling on a straight line from the nucleus of the mask to its centre of gravity and forming part of the USAN area will constitute a proper corner

  17. The SUSAN Detector • The initial corner response image is subjected to non-maximal suppression • Local maxima are searched for in smaller five-by-five pixel regions • Find a pixel which has a response greater than a given threshold is also greater than all its immediate neighbors • In other words, if at least one of those neighboring pixels has a magnitude higher than that of the examined pixel then the latter is assigned a zero value

  18. The SUSAN Detector • SUSAN has better accuracy, reliability and speed than Plessey corner detector Output of the Plessey corner detector (o =2.0) given the test image. (3.5 sec) Output of the SUSAN corner detector (t=10) given the test image. (0.3 sec)

  19. SUSAN Corner Detection Result

  20. Advantages of SUSAN Detector • It performs well even in the presence of noise • No image derivatives are used • Integrating effect and non-linear response give strong noise rejection • The use of controlling parameters is much simpler and less arbitrary

  21. Problems • Are these corner detection algorithms fast enough for mobile devices? • Floating point operation consideration • How to extend the algorithms to detect corners in color image? • Robust feature matching across widely separated color images. Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04)

  22. Reference • S.M. Smith. SUSAN - a new approach to low level image processing. Internal Technical Report TR95SMS1, Defence Research Agency, Chobham Lane, Chertsey, Surrey, UK, 1995. • J. Shi and C. Tomasi. Good features to track. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR94), Seattle, June 1994.

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