This document discusses image mosaicing, which is the process of assembling multiple images into a single image with a larger field of view. It describes image mosaicing models and basic algorithms, including unidirectional and bidirectional scanning. The bidirectional algorithm improves upon unidirectional by using block matching to more efficiently find overlapping regions between images. Example results are shown and limitations discussed, such as reduced accuracy when mosaicing many images to one reference. Applications include creating panoramic or immersive environments from multiple photos.

1. Image Mosaicing
SADHANA SINGH
M.Tech
164709

2. Content
1) What is Mosaic and Mosaicing?
2) Image Mosaicing
3) Why we need image Mosaicing
4) Image Mosaicing Model
5) Basic Algorithms For Image Mosaicing
6) Unidirectional Algorithm
7) Bi-directional Algorithm
8) Results
9) Limitation
10)Applications
11)References

3. What is Mosaic and Mosaicing
• Mosaic“ originates from an old Italian word “mosaico” which
means a picture or pattern produced by arranging together
small pieces of stone, tile, glass, etc.
• Mosaicing is the process of assembling a series of images
and joining them together to form a continuous seamless
photographic representation of the image surface.
• The result is an image with a field of view greater than that of a
single image.

4. Image Mosaicing
• Many a time, it may not be possible to capture the complete image of a
large document in a single exposure as most image-capturing media work
with documents of definite size.
• In such cases, the document has to be scanned part by part producing split
images. Thus, document image analysis and processing require Mosaicing
of the split images to obtain a complete final image of the document.
• Hence, document image mosaicing is the process of merging split images
that are obtained by scanning different parts of single large document image
with some sort of overlapping region (OR) to produce a single and complete
image of the document.

5. Need of Image Mosaicing?
• There are situations where it is not possible to capture large
documents with the given imaging media such as scanners or
copying machines in a single stretch because of their inherent
limitations.
• This results in capture of a large document in terms of split
components of a document image. Hence, the need is to mosaic the
split components into the original and put together the document
image.
• Image mosaicing not only allow you to create a large field of view
using normal camera, the result image can also be used for texture
mapping of a 3D environment such that users can view the
surrounding scene with real images.

6. Image Mosaicing Model
Input Images
Feature Extraction
Image Registration
Homographic
Refinement
Image Warping and
Blending
Output Mosaic
image

7. • 1) Feature Extraction
The first step in image mosaic process is feature detection.
Features are the elements in the two input images to be
matched. For images to be matched they are taken inside an
image patches. These image patches are groups of pixel in
images. Patch matching is done for the
• 2) Image Registration
Image registration is the process of aligning two or more
images of the same scene taken at different times. It
geometrically aligns two images—the reference and
sensed images. This process is needed in various
computer vision applications like motion analysis,
change detection, image fusion etc.

8. • 3) Homographic Refinement
Homography is mapping between two spaces which often
used to represent the correspondence between two images of
the same scene. It’s widely useful for images where multiple
images are taken from a rotating camera having a fixed
camera centre ultimately warped together to produce a
panoramic view.
• 4) Image Warping
Image Warping is the process of digitally manipulating
an image such that any shapes portrayed in the image
have been significantly distorted. Warping may be used
for correcting image distortion as well as for creative
purposes (e.g., morphing).

9. • 5) Image Blending
The final step is to blend the pixels colours in the
overlapped region to avoid the seams. Simplest
available form is to use feathering ,which uses
weighted averaging colour values to blend the
overlapping pixels.

10. Algorithms for Image Mosaicing
• Basically there are two main algorithms of
image mosaicing:
• 1)Unidirectional Scanning 2)Bi-directional
Scanning

11. Unidirectional Algorithm
• It takes two split images as input and produce the original
mosaic image.
• The algorithm compares all pixel values of first image with all
pixel values of second image starting from top to bottom.
• If the whole row matches then the pointer i (represents the
row of two split images) incremented by one in both images.
• If the whole row does not match then the pointer i of first
image is incremented by one but the pointer i of second
image remains unchanged.
• This procedure is repeated till the overlapping region is found
in the split images. The algorithm terminates when the pointer
i of first image reach m (number of rows in the image).

14. Bidirectional Algorithm
• It is an extension of unidirectional algorithm but it uses block
matching to find out overlapping region.
• This algorithm reduced the time complexity to get a mosaic image
from split images.
• This method scans the split images from right to left as well as
left to right, whereas in previous Algorithm scanning of the image takes
place only from left to right to identify the overlapping region in the split
images.

16. Results

17. Results

18. Limitations
• Mosaicing of multiple images cannot be achieved by
repeatedly warping new images to one reference image.
Hence, after mosaicing 4 images to the reference image,
the image alignment doesn’t look good anymore.
• The methods work fine for all types of documents but
they consume time.
• It may fail if the sequence is missed.

19. Application
• Constructing high resolution images that cover an
unlimited field of view using inexpensive equipment.
• Creating immersive environments for effective
information exchange through the internet.
• Using image mosaicing to make a significant impact
in video processing.

20. References
i. Mousumi Saha Mainak Chakraborty Tamasree Biswas ,An
Improved Approach for Document Image Mosaicing,
International Journal of Advanced Research in Computer
Science and Software Engineering , Volume 6, Issue 2,
February 2016
ii. Hemlata Joshi, KhomLal Sinha, Image Mosaicing using
Harris, SIFT Feature Detection Algorithm, International
Journal of Science, Engineering and Technology Research
(IJSETR) Volume 2, Issue 11, November 2013
iii. Hartley, R. & Zisserman, A. (2000) Multiple View
Geometry{Cambridge University Press, UK.
iv. https://in.mathworks.com/
v. https://courses.engr.illinois.edu/cs498dwh/fa2010/lectures/L
ecture%2017%20-%20Photo%20Stitching.pdf