Real Time Non-rigid Surface Detection Based on Binary Robust Independent Elementary Features

The Feature Descriptor sometimes called descriptors has been a hot research topic and is a difficult challenge, a common feature descriptor algorithm is used to detect target, and further do other application cases automatically control, product inspection, face recognition and object tracking. For subsequent applications will therefore need to address issues related to extending such an object view to change, scale, rotation and multi-target detection, etc., so they need for effective algorithms to solve the above problems. In this paper we proposed based BRIEF (Binary Robust Independent Elementary Features)[1] binary feature descriptor to improve the non-rotational invariance of the shortcomings.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[3]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[6]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[9]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[10]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[12]  Ethan Rublee,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[13]  Michael S. Brown,et al.  In Defence of RANSAC for Outlier Rejection in Deformable Registration , 2012, ECCV.

[14]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Isma Irum,et al.  Powerful Descriptor for Image Retrieval Based on Angle Edge and Histograms , 2013 .

[17]  Muhammad Sharif,et al.  Intelligent Image Retrieval Techniques: A Survey , 2014 .