1. Progress In Image Registration

2. Why Registration In computer vision, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. Image registration is the process of transforming the different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different measurements

3. Example : Two Images From a Mojave Desert Sequence

4. Types of Registration Feature Based : Identifies some landmarks, lines, curves, points of high/low intensities and maps them. Area Based : looks at the structure of the image as a whole using correlation metrics, Fourier transforms etc.

5. We Use Area Based Reg. “In multi-cellular biological images, there are several many different points with similar values of intensity at different cells” R. Araiza et al. 3-D Image Registration Using Fast Fourier Transformation: Potential Applications to Geoinformatics and Bioinformatics.

6. The Algorithm

7. R. Araiza et al. 3-D Image Registration Using Fast Fourier Transformation: Potential Applications to Geoinformatics and Bioinformatics. Determining Shift

8. Determining Rotation Compute the second order moments of the images : Compare the orientations of the largest eigenvectors of the matrices formed by the second order moments.

9. Determining Scale Just divide the magnitudes of the Fourier transforms.

10. Current Status A working Code for determining the shift, rotation and scale in 2D images. (Courtesy : Prof. Bajaj) We have assembled an experiment on AVS to check the quality of output of this code.

11. Things To Do Subject the 2D code to more tests. Extending the code to cater to 3D Images. Receive datasets from MDA and run the 3D code on them.

12. Thank You