Image Mosaicing with Invariant Features detection using SIFT
Keywords:
Abstract
There are situations where it is not possible to capture larger views with the given imaging media such as still cameras or video recording machines in a single stretch because of their inherent limitations. So to avoid such conditions a term Image Mosaicing comes into play. This Paper presents a complete system for mosaicing a group of still images with some amount of overlapping between every two successive images. Mainly the idea is to wrap up the overlapping areas within the group of images. Detection for the common area is done using common features by the help of feature extraction from the images. In this paper technique used for the feature extraction is SIFT which is used to extract invariant features which are stable in nature. Invariant features are those features of an image which does not change even after the scaling, rotation, or zooming, change in illumination of the image is done. Multiple level filtering and downsampling are the key factors of the SIFT. So the steps involved are feature detection, matching of stable features, wrapping up of features around those feature locations. Mosaicing part consists of two major part and those are transformation matrix and bilinear interpolation. Mosaiced images are full length images which consist of all the group images.
Downloads
- Article PDF
- TEI XML Kaleidoscope (download in zip)* (Beta by AI)
- Lens* NISO JATS XML (Beta by AI)
- HTML Kaleidoscope* (Beta by AI)
- DBK XML Kaleidoscope (download in zip)* (Beta by AI)
- LaTeX pdf Kaleidoscope* (Beta by AI)
- EPUB Kaleidoscope* (Beta by AI)
- MD Kaleidoscope* (Beta by AI)
- FO Kaleidoscope* (Beta by AI)
- BIB Kaleidoscope* (Beta by AI)
- LaTeX Kaleidoscope* (Beta by AI)
How to Cite
Published
2013-03-15
Issue
Section
License
Copyright (c) 2013 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.