Automatic Panoramic Image Stitching using Invariant Features

Matthew Brown, David G Lowe

Research output: Contribution to journalArticlepeer-review

2200 Citations (SciVal)


This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.
Original languageEnglish
Pages (from-to)59-73
Number of pages15
JournalInternational Journal of Computer Vision
Issue number1
Publication statusPublished - 2007


Dive into the research topics of 'Automatic Panoramic Image Stitching using Invariant Features'. Together they form a unique fingerprint.

Cite this