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Hybrid Approach of Image Stitching using Normalized Gradient Correlation and Harris Corner Detector

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Paresh M. Patel, Hitesh A. Ravani

Paresh M Patel and Hitesh A Ravani. Hybrid Approach of Image Stitching using Normalized Gradient Correlation and Harris Corner Detector. International Journal of Computer Applications 155(8):30-35, December 2016. BibTeX

	author = {Paresh M. Patel and Hitesh A. Ravani},
	title = {Hybrid Approach of Image Stitching using Normalized Gradient Correlation and Harris Corner Detector},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {155},
	number = {8},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {30-35},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016912396},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Image Stitching is application of image registration in which images with few overlapping are aligned and stitch together to form wide angle images. Image registration is the fundamental task used to match two or more partially overlapping images taken, for example, at different times, from different sensors, or from different viewpoints and stitch these images into one panoramic image comprising the whole scene. It is a fundamental image processing technique and is very useful in integrating information from different sensors, finding changes in images taken at different times, inferring three-dimensional information from stereo images, and recognizing model-based objects. Some techniques are proposed to find a geometrical transformation that relates the points of an image to their corresponding points of another image. To register two images, the coordinate transformation between a pair of images must be found. In this paper, proposed algorithm is based on Log-Polar Transform and first roughly estimate the angle, scale and translation between two images. The proposed algorithm can recover scale value up to 5.85. In order to improve the insufficiency of Harris corner, proposed method present an auto-adjusted algorithm of image size based on NGC. The robustness of this algorithm is verified on different images with similarity transformation and in the presence of noise and finally by using RANSAC algorithm smooth stitching can be obtained than any other methods.


  1. B.S. Reddy and B.N. Chatterji. "An FFT-based technique for translation, rotation and scale invariant image registration." IEEE Trans. Image Processing, 5(8):1266–1271, [1996].
  2. B. Sun, D. Zhou A, “Rotated Image Matching Method Based on CISD”, ISNN 2007, Part I, LNCS 4491, pp.1346-1352, [2007].
  3. C.D. Kuglin and D.C. Hines. "The phase -correlation image alignment method." In Proc. IEEE Conf. Cybernetics and Society, pages 163–165, [1975].
  4. Fan YANG, Linlin WEI, Zhiwei ZHANG, Hongmei TANG, ”Image Mosaic Based on Phase Correlation and Harris Operator”, Journal of Computational Information Systems,
  5. Vol 8:6(2012) 2647–2655.
  6. Jignesh N Sarvaiya, Dr. Suprava Patnaik, Salman Bombaywala, “Image registration using Log polar transform and phase correlation”, TENCON 2009 - 2009 IEEE Region 10 Conference, pp.1-5, [2009].
  7. Pengrui Qiu, Ying Liang and Hui Rong, “Image Mosaics Algorithm Based on SIFT Feature Point Matching and Transformation Parameters Automatically Recognizing” 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013).
  8. Qin-sheng Chen, Michel Defrise, F. Deconinck, “Symmetric phase-only matched filtering of Fourier Mellin transform for image registration and recognition”, IEEE Trans. on Pattern Analysis and Machine Intelligence 16, pp.1156-1168, [1994].
  9. S. Zokai and G.Wolberg. "Image registration using log-polar mappings for recovery of large-scale imilarity and projective transformations". IEEE Trans. Image Processing.
  10. 14(10):1422–1434, [2005].
  11. Tzimiropoulos G. "Robust FFT-based scale invariant image registration with image gradients." IEEE (2010).
  12. Xiaoxin Guo, Zhiwen Xu, Yinan Lu, and Yunjie Pang, “An Application of Fourier-Mellin Transform in Image Registration”, Proceedings of The Fifth International Conference on Computer and Information Technology, pp.619-623 , [2005].
  13. Xin Xie and Yin Xu “A study on Fast SIFT Image Mosaic Algorithm Based on compressed Sensing and wavelet Transform” In Journal of ambient Intelligence and Humanized computing(Springer), Vol 6, Issue 6, Pages 835-843,[2015].
  14. Zhicheng Wang and Yufei Chen “An Automatic Panoramic Image Mosaic method based on Graph Model” In Springer Link. Vol 75, Issue 5, pages 2725-2740, [2016].


Log-Polar Transform (LPT), Fast Fourier Transform (FFT), Normalized Gradient Correlation (NGC), Random Sample Consensus (RANSAC), Direct Linear Transform (DLT), Scale Invariant Feature Transform (SIFT).