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Advances in Image Registration: Techniques and Applications in Computer Vision

Image registration is a crucial process in computer vision, transforming datasets into a unified coordinate system for accurate comparison and integration. This process is vital for analyzing images collected from different times or perspectives, such as in multi-cellular biological studies. Key registration techniques include feature-based methods that map landmarks and area-based methods that analyze image structures using correlation metrics. Our current work focuses on a 2D image registration algorithm using Fast Fourier Transformation, with plans to extend to 3D images and further validate the code through testing.

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Advances in Image Registration: Techniques and Applications in Computer Vision

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Presentation Transcript

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

  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

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