Image Stitching System Based on ORB Feature-Based Technique and Compensation Blending

The construction of a high-resolution panoramic image from a sequence of input overlapping images of the same scene is called image stitching/mosaicing. It is considered as an important, challenging topic in computer vision, multimedia, and computer graphics. The quality of the mosaic image and the time cost are the two primary parameters for measuring the stitching performance. Therefore, the main objective of this paper is to introduce a high-quality image stitching system with least computation time. First, we compare many different features detectors. We test Harris corner detector, SIFT, SURF, FAST, GoodFeaturesToTrack, MSER, and ORB techniques to measure the detection rate of the corrected keypoints and processing time. Second, we manipulate the implementation of different common categories of image blending methods to increase the quality of the stitching process. From experimental results, we conclude that ORB algorithm is the fastest, more accurate, and with higher performance. In addition, Exposure Compensation is the highest stitching quality blending method. Finally, we have generated an image stitching system based on ORB using Exposure Compensation blending method.

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

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

[3]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[4]  Mohammed Elmogy,et al.  Real time image mosaicing system based on feature extraction techniques , 2014, 2014 9th International Conference on Computer Engineering & Systems (ICCES).

[5]  Richard Szeliski,et al.  Direct methods for visual scene reconstruction , 1995, Proceedings IEEE Workshop on Representation of Visual Scenes (In Conjunction with ICCV'95).

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

[7]  Umesh C. Pati,et al.  A Robust Technique for Feature-based Image Mosaicing using Image Fusion , 2013 .

[8]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

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

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

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

[13]  Borivoj Vojnovic,et al.  An Algorithm for image stitching and blending , 2005, SPIE BiOS.

[14]  Richard Szeliski,et al.  Eliminating ghosting and exposure artifacts in image mosaics , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Shmuel Peleg,et al.  Seamless image stitching by minimizing false edges , 2006, IEEE Transactions on Image Processing.

[16]  Subu Surendran,et al.  SATELLITE IMAGE REGISTRATION AND IMAGE STITCHING , 2013 .

[17]  Richard Szeliski,et al.  Image Alignment and Stitching , 2006, Handbook of Mathematical Models in Computer Vision.

[18]  Bruce A. Draper,et al.  Introduction to the Bag of Features Paradigm for Image Classification and Retrieval , 2011, ArXiv.

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

[20]  Matthew A. Brown,et al.  Automatic Panoramic Image Stitching using Invariant Features , 2007, International Journal of Computer Vision.

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