Image mosaicing

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.

1. Image Mosaicing SADHANA SINGH M.Tech 164709

2. Content W h a t i s M os ai c and M o s a i ci ng ? I ma g e M os a i c i n g W h y w e n ee d i m a g e M o s a i ci n g I ma g e M os a i c i n g M od el Basi c A l g o r i t h m s F o r I ma g e M o sai ci n g U n i d i r e c ti on a l A lg o ri t h m Bi - di rect i o na l A l g o r i t h m Res ul t s L i m i t at ion A p p l ic a t io n s Re f er e nc es

3. Wh a t i s Mo sai c and M o sa i c i n g 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. I m a g e M o sa i cing M o del I n pu t I m a g es F ea t u re E x t r a c t i o n Im a g e R e g i s t ra ti o n 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 ) I m a g e W a rp i n g 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).

12. Bi d i rect i o na l Al gor i t h m 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.

13. Results

14. Results

15. L i mi t a t ion s 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. I t m a y f a il i f t h e s eq u e n c e i s mis se d .

16. Ap p l i cat io n 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.

17. Refe r e n ces 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 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 Hartley, R. & Zisserman , A. (2000) Multiple View Geometry{ Cambridge University Press, UK . https://in.mathworks.com / https://courses.engr.illinois.edu/cs498dwh/fa2010/lectures/Lecture%2017%20-%20Photo%20Stitching.pdf

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