Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images
Abstract
General cameras, which have low FOV can’t
generate images with higher FOV while stitching
can help us achieve it. It is a special case of scene
reconstruction through which images are related by
planar homography. Two or more images can be
stitched with each other uniquely without loss of
information in any images with a greater
FOV.Numerous stitching algorithms have been
proposed. Applications of the algorithms proposed
is based on the quality of the results we obtain. It
depends upon human perception (how much
aesthetic the generated picture is) as well as
machine perception (these can be used for other
processing where some data extraction is required
from the image.This paper proposes a unique
algorithm for stitching two or a number of images.
Input images are taken and features are detected
using Harris-corner detection method. RANSAC is
applied to find feature correspondences between
images. Images are then projected in a plane and
blended together. The whole method is implemented
using MATLAB software.Work by Samy Ait-Aoudia
was focused on stitching of satellite images or
aerialimages.
It
was using SIFT future
correspondence for feature detection. Thus finding
the relevant ones and stitching the images.
Debabrata Ghose worked on quantitative evaluation
of image stitching methods . An algorithm was
developed to determine the performance matrix for
different methods i.e. RANSAC, SIFT etc., thus
determining the correlation and errors between the
outputs and taking the best of the results among
those from the created performance matrix. Richard
Szeliski has done an extended research on the topics
and had found many novel algorithms for
registration and stitching. There are unique methods
developed for extracting large 2-D textures from
image sequences based on image registration and
compositing techniques. After a review of related
work and of the basic image formation equations led
to the development of method for registering pieces
of a flat (planar) scene, which is the simplest
interesting image stitching problem. Then it was
seen how the same method can be used to mosaic
panoramic scenes attained by rotating the camera
around its centre of projection. Finally, we conclude
with a discussion of the importance of our
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