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Mittali et al., International Journal of Computer & IT [ISSN No.(Print):2320-8074]
A Detailed Survey on Various Image Stitching Techniques
Mittali,
M-tech student ,
Department of CSE,
GZSPTU Campus Bathinda
Abstract:-In this research paper, we have solicited
high quality impact research papers. This
systematic review reveals many aspects of image
stitching. The first section of this paper is dedicated
to the basic concepts in image stitching and
discusses its applications. These sections in this
paper discusses the algorithms involved in image
stitching. The main algorithm discussed here are
harris based,local symmetry based, chaos inspired
dissimilarity features based and surf based. We
have also discussed their merits and demerits and
have identified four research gaps which would help
future researchers in the area to develop next
generation of image stitching algorithms and
applications.
Keywords:-Sift, Surf, Harriscorner, Ransac, Multi
model , Feature extraction and cross correlation.
1.Introduction
Image stitching is a process in which various
images are stitched together after establishing
geometric relationship between these images. The
geometric
relationships
are
coordinate
transformations that usually relates the various
coordinate system .By applying appropriate
transformations via a merging operation and combine
the overlapping region of images it is possible to
create a noteworthy form of mosaic. A noteworthy
form of image mosaicing known as image stitching
has become growingly common in the making of
panoramic image Connected sets of image matches
will later become panorama.
Registration and
mosaicing of images have been in practice since long
before the age of digital computers. Shortly after the
photographic process was developed in 1839, the use
of photographs was demonstrated on topographical
mapping .In past to capture a panoramic view one
basic requirement was the n different cameras in
different location and at different angles .Still it was
not possible to obtain a perfect panorama due to lack
of coordination between the shots taken by camera due
to time lag. The possible reasons behind it were
uncoordinated time frames and angle setups. To
overcome this problem wireless sensor networks were
introduced which use an array of sensors to capture
different angles and different perspectives. To
simplify the process of capturing a perfect panorama
© 2015, IJCIT All Rights Reserved
Jyoti Rani
Assistant Professor,
Department of CSE,
GZSPTU Campus Bathinda
drones may be used to capture vast view using
automatic timer.
The two main expectations from the image stitching
process are: The Stitched image should be nearly close as
possible to input images
 In Stitched images the seams should be
invisible
1.1 Image stitching procedure:In the first step in generation of a panoramic image is
to select the positions for acquisition of image. In this
step, a decision needs to be made on the type of
resultant panoramic images. According to the required
panoramic images, different image acquisition
methods[1] may be used to acquire the series of images
.After the images have been acquired, some processing
might need to be applied to the images before they can
be stitched. For example, the images might need to be
projected onto a surface, which can be a mathematical
surface model such as a cylindrical, spherical, or planar
surface. Distortions caused by the camera lenses also
need to be corrected before the images are processed
further. In this work, the process of image stitching can
be divided into two steps as shown in fig(1):1.
2.
Image registration
Image merging.
During image registration, portions of adjacent images
are compared to find the translations which align the
images. Image registration includes following
processes:1. Feature extraction
2. Feature description
3. Feature Matching
These processes play an essential role in image
stitching.Once, the overlapping images have been
registered, they need to be merged together to form a
single panoramic image view . The process of image
merging is performed to make the transition between
adjacent images visually undetectable .A panoramic
image is generated after the images have been stitched.
Image stitching has practical importance in many
fields, including remote sensing, medical imaging,
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Mittali et al., International Journal of Computer & IT [ISSN No.(Print):2320-8074]
computer vision, automated navigation and multi node
movies
2.3.Taeyup Song; Changwon Jeon; Hanseok Ko et al.
proposed "Image stitching using chaos-inspired
dissimilarity measure"
This method overcomes the problem of illumination
changes stemming from different exposures. In this
method feature points are extracted by sift and by using
k-d search tree algorithm and k nearest neighbor
algorithm feature points are matched after that the
outliers are removed by using the novel feature
matching algorithm.
2.4. Fei Lei; Wenxue Wang,et al. proposed "A fast
method for image mosaic based on SURF,"
In this method in the first step feature points are
extracted by surf operator[7] and feature points are
matched by best bin first algorithm[8]. Image
registration is completed by estimating the converting
relationship between images by using ransac and least
squares method .In the last step the image is merged by
using in and out amalgamation algorithm which
produces a final stitched image.
Figure 1 Typically Flow of Image Stitching
2.Related Work
2.1. Chen Kaili Wang Meiling et al. proposed "Image
stitching algorithm research based on Open CV". In
this stitching method first of all feature extraction takes
place through harris corner detection[2] and feature is
matched by finding the normalized cross correlation
between them. After that ransac is used to remove the
outliers and to eliminate the error matching. Finally the
weighting average method[3] is used to merge the
image .As per the claim of this paper this algorithm
reduces: Computational complexity of image merging
 Overlapping rate of images
2.2 Yang Di; Bo Yu-ming; Zhao Gao-peng, et al.
proposed “Image stitching based on local symmetry
features: This method overcome the limitation of sift
i.e. sensitive to non linear illumination changes[4]. In
this method initially feature points are extracted by
SYFM(a local symmetry based descriptor)and
SIFT(gradient based descriptor)[5].Then SIFT
descriptor and local symmetry are combined to
characterize those feature point. After that feature
matching is carried out by “randomized kd trees” and
transform parameters are calculated by correct inner
points after ransac was used to eliminate wrong
matches. In the last image stitching is completed with
smoothing algorithm .this method has higher matching
precision than SIFT(scale invariant feature
transformation) and SURF(speeded up robust features)
under the non linear illumination change scenarios and
can achieve better performance in image stitching.
© 2015, IJCIT All Rights Reserved
3.Research Gap:After Conducting this survey ,few gaps have been
found In previous approaches ,RANSAC can only
estimate one model for a particular data set, that means
it works only with particular limited set of image set.
As for any one model approach when two (or more)
model instances exist, RANSAC may fail to find either
one .the main idea proposed here is using ransac with
multi model fitting , which combines model sampling
from data points as in RANSAC with iterative re
estimation of inliers and the multimodel fitting being
formulated as an optimization problem with a global
energy functional describing the quality of the overall
solution . Firstly the features are extracted by using
hybrid of Sift and Surf and then features are matched
by finding the normalized cross correlation between
them and then outliers are removed by using ransac
with multimodel fitting..
 This will calculate recall and precision with
better accuracy
 No. of features will also be increased. this will
increase the reliability and accuracy of
stitched image.
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Mittali et al., International Journal of Computer & IT [ISSN No.(Print):2320-8074]
4. Literature survey:
Various image
stitching techniques
Feature extraction
Feature matching
Outliers
elimination
Image
merging
Image stitching
based on open cv
Harris corner
detection
Normalized cross
correlation
Ransac
Weighting average
method
Image stitching
based on local
symmetry features
Image stitching
based on chaos
inspired
dissimilarity
measures
Image stitching
based on fast
method of surf
Proposed image
stitching method
SYFM*SIFT
Randomized k-d
trees
Ransac
Smoothing algorithm
SIFT
K nearest neighbor
and k-d trees
Novel
feature
matching
algorithm
Direct
average
method
SURF
Best bin first
algorithm
Ransac and
least square
method
Hybrid of Sift and
surf
Normalized cross
correlation
Ransac with
multi model
fitting
Gradating
in and
out amalgamation
n algorithm
Blending process
(using
multiplication)
5. Discussion and Conclusion:All the techniques discussed previously in section 2
conclude that every method has its own pros and cons.
The summary it may conclude that there is ample scope
to improve these methods. As research gap present over
here. a proposed algorithm was discussed in section 3
based on this gap.
6. Future Scope:It is clear from the above study that the future research
in this area can be enhanced based on using hybrid
technique which use multi model approach in matching
feature points of images.
7. REFRENCES:[1]Patil, Tejasha, et al. "Image stitching using matlab."
International Journal of Engineering Trends and TechnologyVolume4Issue3-2013 (2013).
[2]Chen Kaili; Wang Meiling, "Image stitching algorithm
research based on Open CV," Control Conference (CCC), 2014
33rd Chinese , vol., no., pp.7292,7297, 28-30 July 2014
doi: 10.1109/ChiCC.2014.6896208
© 2015, IJCIT All Rights Reserved
[3] Mahesh; Subramanyam, M.V., "Automatic image mosaic
system using steerable Harris corner detector," Machine Vision
and Image Processing (MVIP), 2012 International Conference
on , vol., no., pp.87,91, 14-15 Dec. 2012
[4] Yang Di; Bo Yu-ming; Zhao Gao-peng, "Image stitching
based on local symmetry features," Control Conference (CCC),
2014 33rd Chinese , vol., no., pp.4641,4646, 28-30 July 2014
doi: 10.1109/ChiCC.2014.6895721
[5] Yanfang Li; Yaming Wang; Wenqing Huang; Zuoli Zhang,
"Automatic image stitching using SIFT," Audio, Language and
Image Processing, 2008. ICALIP 2008. International
Conference on , vol., no., pp.568,571, 7-9 July 2008
[6] Taeyup Song; Changwon Jeon; Hanseok Ko, "Image
stitching using chaos-inspired dissimilarity measure,"
Electronics Letters , vol.51, no.3, pp.232,234, 2 5 2015
doi: 10.1049/el.2014.0981
[7] Niu Jing; Yang Fan; Shi Lingyi, "Improved method of
automatic image stitching based on SURF," Future Information
and Communication Technologies for Ubiquitous HealthCare
(Ubi-HealthTech), 2013 First International Symposium on , vol.,
no., pp.1,5, 1-3 July 2013
doi: 10.1109/Ubi-HealthTech.2013.6708059
[8] Fei Lei; Wenxue Wang, "A fast method for image mosaic
based on SURF," Industrial Electronics and Applications
(ICIEA), 2014 IEEE 9th Conference on , vol., no., pp.79,82, 911 June 2014 doi: 10.1109/ICIEA.2014.6931135
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