0% Use Case Dependent – Further information here.
Since this model is a preprocessing model for imagery, it does not have any associated and relevant performance. The model tends to perform better on images with scenes of flat grounds (e.g., fields, tops of buildings) instead of scenes with significant contour (e.g., hills, areas with many trees, riverbanks).
This model was not trained on any dataset as it is a preprocessing tool that uses a transformation methodology build built into the OpenCV library. The algorithm first finds key-points and descriptors from each image using the ORB OpenCV function. It then matches the key features from both images using the brute-force matcher function in OpenCV (BFMatcher). Next, it finds the homography matrix and uses it to create a new image that represents the unaligned image being aligned to the reference image.
This model is a deterministic computer vision algorithm implementation and has no associated training.
Validation of the model output was done empirically, using several diverse test scenes.
The input(s) to this model must adhere to the following specifications:
This model will output the following:
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