Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

A Method for Generation of Panoramic View based on Images Acquired by a Moving Camera

Print
PDF
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
© 2011 by IJCA Journal
Number 3 - Article 2
Year of Publication: 2011
Authors:
Prajkta Sangle
Krishnan Kutty
Anita Patil
10.5120/2840-221

Prajkta Sangle, Krishnan Kutty and Anita Patil. A Method for Generation of Panoramic View based on Images Acquired by a Moving Camera. IJCA Special Issue on Artificial Intelligence Techniques - Novel Approaches & Practical Applications (3):24–27, 2011. Full text available. BibTeX

@article{key:article,
	author = {Prajkta Sangle and Krishnan Kutty and Anita Patil},
	title = {A Method for Generation of Panoramic View based on Images Acquired by a Moving Camera},
	journal = {IJCA Special Issue on Artificial Intelligence Techniques - Novel Approaches & Practical Applications},
	year = {2011},
	number = {3},
	pages = {24--27},
	note = {Full text available}
}

Abstract

Panoramic photo stitching is the process of combining multiple photographic images with overlapping fields of view to produce a panorama. The process to generate a panoramic view can be divided into three main components - image acquisition, image registration, and blending. In this paper, a robust algorithm called Scale Invariant Feature Transform (SIFT) used to extract the features from the images and matching them which is a part of image registration. SIFT features are invariant to rotation, translation, image scaling and partially invariant to 3D viewpoint, illumination changes and image noise. Image transformation is estimated using homography. Image blending technique is used to blend the images together to get a panoramic view. Main applications of panoramic view include creating virtual environment for virtual reality, modeling the 3D environment using images acquired from the real world.

Reference

  • M. Brown, D. Lowe, “Automatic Panoramic Image Stitching using Invariant Features”, International Journal of Computer Vision 74(1), 59–73, 2007, Springer Science + Business Media, LLC. Manufactured in the United States
  • David Lowe, “Distinctive Image Features from Scale- Invariant Keypoints”, International Journal of Computer Vision, 2004.
  • Lindeberg, T. 1993. “Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention” International Journal of Computer Vision, 11(3): 283-318.
  • Ito, Minami, Hiroshi Tamura, Ichiro Fujita, and Keiji Tanaka, “Size and position invariance of neuronal responses in monkey inferotemporal cortex,” Journal of Neurophysiology, 73, 1 (1995), pp. 218–226.
  • C. Chen and R. Klette, “An image stitcher and its application in panoramic movie making.” Proc.DICTA’97, Dec.1997, pp.101-106.
  • Szeliski, R. 2004. “Image alignment and stitching: A tutorial.” Technical Report MSR-TR-2004-92, Microsoft Research.
  • David Lowe, “Object recognition from local scale-invariant features” In International Conference on Computer Vision, Corfu, Greece, pp. 1150-1157
  • Harris and M.J. Stephens, “A combined corner and edge detector” In Alvey Vision Conference, pages 147–152, 1988.
  • Mikolajczyk and Schmid, 2005“A performance evaluation of local descriptors”, In European Conference on Computer Vision (ECCV), Copenhagen, Denmark
  • Schmid, C., and Mohr, R. 1997, “Local grayvalue invariants for image retrieval” IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(5):530-534.
  • Xi Shao, Changsheng Xu, Joo Hwee Lim, “Image Mosaics Base on Homogeneous Coordinates” Institute for Infocomm Research.
  • Anat Levin, Assaf Zomet, Shmuel Peleg, and Yair Weiss, “Seamless Image Stitching in the Gradient Domain”, research supported (in part) by the EU under the Presence Initiative through contract IST-2001-39184, Benego.
  • Yu Meng and Bernard Tiddeman, “Implementing the Scale Invariant Feature Transform (SIFT) Method”, Department of Computer Science University of St. Andrews
  • Andrea Vedaldi, “An implementation of SIFT detector and descriptor”, University of California at Los Angeles
  • Konstantinos G. Derpanis, “The Harris Corner Detector”.