This paper presents a modified Speeded Up Robust Features (SURF) with feature point detector based on scale space saddle points. Most of the feature detectors like Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)-SIFT and SURF are based on extrema points i.e. local maxima and minima. This work aims at utilizing the saddle points for panorama stitching which is a common and direct application for feature matching. Here Euclidean distance of descriptor is used to find the correct matches. Experiments to test the performance are done on Oxford affine covariant dataset and compared the performance with that of SURF. © 2012 Springer-Verlag.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@21b747ee ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@7d36b8d Through org.apache.xalan.xsltc.dom.DOMAdapter@4939f9fe; Conference Code:88918
S. S. Kecheril, Issac, A., and C. Velayutham, S., “SaddleSURF: A saddle based interest point detector”, Communications in Computer and Information Science, vol. 283 CCIS, pp. 413-420, 2012.