Effects of noise and relative overlap on image mosaicing using SURF features

Performance of image mosaicing depends on overlap between the images to be joined and the percentage of noise present. An algorithm is used for joining images based on image matching by comparing the descriptors for different images. This paper is concerned with the analysis of effect of variations in noise and degree of relative overlap on the algorithm and obtaining their limits. Speeded Up Robust Features (SURF) have been used for key point detection. Percentage overlap between the images to be joined is varied to find out the minimum value required for mosaicing. Further, relative overlap is kept constant and noise is increasingly added to the input images to find out the maximum amount of noise the algorithm can sustain. The aforementioned experiments were performed over a set of images. Ultimately, a range for maximum permissible noise and minimum overlap required is defined for acceptable panorama generation.

[1]  Richard Szeliski,et al.  Construction of Panoramic Image Mosaics with Global and Local Alignment , 2001 .

[2]  Harpreet S. Sawhney,et al.  True Multi-Image Alignment and Its Application to Mosaicing and Lens Distortion Correction , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

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

[4]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[6]  Rachid Deriche,et al.  Using geometric corners to build a 2D mosaic from a set of images , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Andrew Zisserman,et al.  Automated mosaicing with super-resolution zoom , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[8]  Richard Szeliski,et al.  Systems and Experiment Paper: Construction of Panoramic Image Mosaics with Global and Local Alignment , 2000, International Journal of Computer Vision.

[9]  Matthew A. Brown,et al.  Invariant Features from Interest Point Groups , 2002, BMVC.

[10]  Richard Szeliski,et al.  Construction of panoramic mosaics with global and lo-cal alignment , 2020 .

[11]  Philip F. McLauchlan,et al.  Image mosaicing using sequential bundle adjustment , 2002, Image Vis. Comput..

[12]  Matthew A. Brown,et al.  Recognising panoramas , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  Harpreet S. Sawhney,et al.  True multi-image alignment and its application to mosaicing and lens distortion correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[16]  P. Anandan,et al.  About Direct Methods , 1999, Workshop on Vision Algorithms.

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

[18]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[19]  Wen-Liang Hwang,et al.  Variational calculus approach to multiresolution image mosaic , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[20]  Richard Szeliski,et al.  Creating full view panoramic image mosaics and environment maps , 1997, SIGGRAPH.

[21]  James J. Little,et al.  Global localization using distinctive visual features , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[23]  L. Gool,et al.  Interactive museum guide : fast and robust recognition of museum objects , 2006 .