An Image mosaic is a composition generated from a sequence of images which can be obtained by understanding geometric relationships between images. The geometric relations are coordinate transformations that relate the different image coordinate systems. Image mosaic is a technique for creating images which cannot be created by a single frame of the camera, for example satellite imagery. The user gives a series of pictures with overlapping regions and the software returns one large image with all the pictures merged as accurately as possible. When it is impossible to capture the large image in one shot with available equipment, mosaicing process can be used to create an image montage from separately scanned pieces. This paper goal is to create a script that will stitch two images together to create one larger image. These stitched images, become panoramic views which increase the visual aesthetics of a scene, and are widely sought out for posters, postcards, and other printed materials. This stitching will be performed using point correspondences between the two images.

1. Automatic Image Mosaicing: An Approach Based on FFT

2. I.INTRODUCTION
An Image mosaic is a composition generated from a sequence of images which can be obtained
by understanding geometric relationships between images. The geometric relations are
coordinate transformations that relate the different image coordinate systems. Image mosaic is
a technique for creating images which cannot be created by a single frame of the camera, for
example satellite imagery. The user gives a series of pictures with overlapping regions and the
software returns one large image with all the pictures merged as accurately as possible. When it
is impossible to capture the large image in one shot with available equipment, mosaicing
process can be used to create an image montage from separately scanned pieces.
This paper goal is to create a script that will stitch two images together to create one larger
image. These stitched images, become panoramic views which increase the visual aesthetics of
a scene, and are widely sought out for posters, postcards, and other printed materials. This
stitching will be performed using point correspondences between the two images.

3. A.Global alignment: Global alignment involves calculation of trasform,
which alingn two images
B.Local adjustment: even after good global alignment, some pixels might not
align in the two images.
This might cause ghosting or blur in blended images.
C.Automatic selection of images to blend from a given set of images.
D.Image blending: After one of the images has been transformed using the
image homography
calculated above a decision need to be made about the colour to be assigned
to the overlapping regions.
Blending also becomes important when there exist a moving object in the
images taken.
E.Auto exposer compensation: Most cameras have an automatic exposer
control.
The images taken can therefore be of variable brightness in the overlapping
region which might cause the mosaic to look unrealistic.
II.PROBLEM ENCOUNTERED

4. 1. Image calibration (perspective correction,vignette correction, chromatic aberration correction).
Images are processed in this stage to improve results.
2. Image registration (analysis for translation, rotation, and focal length).
Direct or feature-based image alignment methods may be used.
Direct aliment methods search for image orientations that minimize the sum of absolute differences b/w overlapping pixels.
Feature-based methods determine proper image orientations by identifying features that appear in multiple images.
3. Image blending; combining the sections
A. Automated methods for image registration used in image mosaicing can be categorized as follows:
1) Feature based methods rely on accurate detection of image features.
Correspondences between features lead to computation of the camera motion which can be tested for alignment.
In the absence of distinctive features, this kind of approach is likely to fail.
2) Exhaustively searching for a best match for all possible motion parameters can be computationally extremely expensive.
Using hierarchical processing (i.e. coarse-to-fine) results in significant speed-ups.
We also use this approach having an advantage of parallel processing for additional performance improvement.
3) Frequency domain approaches for finding displacement and rotation/scale are computationally efficient but can be
sensitive to noise.
These methods also require the overlap extent to occupy a significant portion of the images (e.g. at least 50%).
4) Iteratively adjusting camera-motion parameters leads to local minimums unless a reliable initial estimation is provided.
Initial estimation can be obtained using a coarse global search or an efficiently implemented frequency domain approach.
II.STEPS OF IMAGE STITCHING

5. Suppose the two images I1 and I2 to be registered having both translation and
rotation with angle of rotation being ’theta’ between them.
When I2 is rotated by theta,there will be only translation left between the images.
So by rotating I2 by one degree each time and computing the correlation peak for
that angle,we reach a stage where there is only translation left between the images.
That angle becomes the angle of rotation.
1.Translation Transform x’= x +b
2.Affine Transform x’=ax+b
B. Transformations

6. V I I I . A P P L I C AT I O N S
There are many applications which
require high resolution
images. In bright field or
epifluorescence microscopy for
example, which are used in
biological and medical
application. It is often necessary to
analyze the complete tissue
section which has dimensions of
several tens of millimeters at
high resolution. The most common
approach is to acquire
several images of parts of the tissue
at high magnification and
assemble them into a composite
single image which preserves
the high resolution
The most common mosaicing
applications include
constructing high resolution images that cover an unlimited
field of view using inexpensive equipment, creating
immersive environments for effective information exchange
through the internet. These applications have been extended
towards the creation of completely navigatable “virtualized''
environments by creating arbitrary views from a limited
number of nodes. The reconstruction of 3D scene structure
from multiple nodes has also been another active area of
research.

7. · Ti l l n o w w e h a v e w o r k e d w i t h M o s a i c i n g t w o i m a g e s . We
c a n e x t e n d i t t o m u l t i p l e i m a g e s b y s e n d i n g t w o i m a g e s a t
a t i m e t o t h e a l g o r i t h m s p ro p o s e d a n d m e rg e t h e m
a c c o rd i n g l y.
· I m a g e p a n o r a m a g e n e r a t i o n f ro m v i d e o
· S u p e r- re s o l u t i o n i m a g i n g
· C o m b i n i n g i m a g e s t o i n c re a s e t h e re s o l u t i o n .
· C o m b i n i n g i m a g e s f ro m m u l t i p l e a n g l e s t o f o r m a 3 D
m o d e
I X . F U T U R E A S P E C T S

8. · A k e y f e a t u re o f F o u r i e r- b a s e d re g i s t r a t i o n m e t h o d s i s t h e
s p e e d o f f e re d b y t h e u s e o f F F T ro u t i n e s .
· We h a v e p re s e n t e d a s u c c e s s f u l m e t h o d o f a u t o m a t i c a l l y
m o s a i c i n g b i n a r i s e d i m a g e s t a k e n w i t h a l o w c o s t d i g i t a l
c a m e r a b y u s i n g t h e p h a s e c o r re l a t i o n m e t h o d .
· To re d u c e t h e c o m p u t a t i o n t i m e w e c o r re l a t e d o n l y t h e s u b
i m a g e o f s e c o n d i m a g e w i t h t h e f i r s t i m a g e .
· T h e p ro p o s e d a l g o r i t h m h a s g i v e n t h e e x a c t a n g l e o f
ro t a t i o n e f f i c i e n t l y a n d t h e m e rg e d i m a g e s a re s a t i s f a c t o r y.
A C K N O W L E D G E M E N T
I w o u l d l i k e t o t h a n k s D r. A j a y Ve r m a , E l e c t ro n i c s &
I n s t r u m e n t a t i o n D e p t . D AV V- I n d o re , f o r t h e i r c o n s t a n t
e n c o u r a g e m e n t a n d s u p p o r t f o r t h i s w o r k
. C O N C L U S I O N