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Figure 13 (right) shows the 53rd stereo image with the matched feature points.
Camera poses are estimated from matched feature points between map points and the input image.
It is then assumed that the more the matched feature points, the better the image dehazing algorithm.
Although it increased matched feature points using color information in detection and distinctive description, it is inapplicable to multimodal images.
The pose estimation process estimates the camera poses based on the matched feature points from previous procedures.
Moreover, when matched feature points are near to each other, small location errors may lead to large scale and rotation estimation errors [20].
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This algorithm is based on a sparse set of matched feature point pairs and the elastic registration is performed by means of multilevel B-spline image warping.
Figure 3 demonstrates an example for matching feature points in two images with remarkable scale changes.
In general, the value of residual error is determined as an indicator of the quality of photogrammetry modeling which entails manually matching feature points on the object.
Once object images taken from different perspectives are imported to PhotoModeler, building the model involves human interaction, to pick and match feature points in different photos.
In this paper, image registration is boil down to a formula discovery problem to match feature points, we develop a new feature-based algorithm for contour registration automatically based on a hybrid approach combining Multi Expression Programming (MEP) with Clonal Selection Principle (CSP).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com