Image segmentation is one of the important applications in computer vision applications. In this paper, we
present an image registration method that stiches multiple images into one complete view. Also, we demonstrate
how image segmentation is used as an error metric to evaluate image registration. This paper explains about the
error analysis using pattern recognition algorithm such as watershed algorithm for calculating the error for image
registration applications. In this paper, we compare pixel intensity-based error metric with object-based error metric
for evaluating the registration results. We explain in which situation pattern recognition algorithm is superior to
other conventional algorithm such as mean square error.
This paper discusses the error analysis and performance estimation between two different mathematical
methods for registering a sequence of images taken by an airborne sensor. Here both methods use homography
matrices to obtain the panoramic image, but they use different mathematical techniques to obtain the same result. In
Method-I, we use Discrete Linear Transform and Singular Value Decomposition to obtain the homographies and in
Method-II we use the Levenberg-Marquardt algorithm as iterative technique to re-estimate the homography in order
to obtain the same panoramic image. These two methods are analyzed, compared based on reliability and robustness
of registration. We also compare their performance using an error metric that compares their registration accuracies
with respect to ground truth. Our results demonstrate that Levenberg-Marquardt algorithm clearly outperforms
Discrete Linear Transform algorithm.
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