Real time image mosaicing system based on feature extraction techniques

Image mosaicing/stitching is considered as an active research area in computer vision and computer graphics. Image mosaicing is concerned with combining two or more images of the same scene into one panoramic image with high resolution. There are two main types of techniques used for creating image stitching: direct methods and feature-based methods. The greatest advantages of feature-based methods over the other methods are their speed, robustness, and the availability of creating panoramic image of a non-planar scene with unrestricted camera motion. In this paper, we propose a real time image stitching system based on ORB feature-based technique. We compared the performance of our proposed system with SIFT and SURF feature-based techniques. The experiment results show that the ORB algorithm is the fastest, the highest performance, and it needs very low memory requirements. In addition, we make a comparison between different feature-based detectors. The experimental result shows that SIFT is a robust algorithm but it takes more time for computations. MSER and FAST techniques have better performance with respect to speed and accuracy.

[1]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[2]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[3]  A. V. Kulkarni,et al.  Object recognition with ORB and its Implementation on FPGA , 2013 .

[4]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[5]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[6]  Dilipsinh Bheda,et al.  A Study on Features Extraction Techniquesfor Image Mosaicing , 2014 .

[7]  Edward H. Adelson,et al.  PYRAMID METHODS IN IMAGE PROCESSING. , 1984 .

[8]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[9]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[10]  A. Koschan,et al.  COMPARISON AND EVALUATION OF FEATURE POINT DETECTORS , 2006 .

[11]  K. P. Soman,et al.  Implementation and Comparative Study of Image Fusion Algorithms , 2010 .

[12]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

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

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

[15]  Zhenjiang Miao,et al.  A Robust Image Mosaic Algorithm , 2008, 2008 Congress on Image and Signal Processing.

[16]  Robert Laganiere,et al.  OpenCV 2 Computer Vision Application Programming Cookbook , 2011 .

[17]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[18]  Dushyant Vaghela,et al.  A Review of Image Mosaicing Techniques , 2014, ArXiv.

[19]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[20]  M. Tech,et al.  A Survey on Image Mosaicing Techniques , 2013 .

[21]  H. K. Abhyankar,et al.  Image Registration Techniques: An overview , 2009 .

[22]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[23]  Rupali Chandratre,et al.  Image Stitching using Harris and RANSAC , 2014 .

[24]  Mark Hedley,et al.  Fast corner detection , 1998, Image Vis. Comput..

[25]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Hetal M. Patel,et al.  Comprehensive Study And Review Of Image Mosaicing Methods , 2012 .