{'fraction_non_alphanumeric': 0.02298136645962733, 'fraction_numerical': 0.0, 'mean_word_length': 4.87956204379562, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Affine invariant and robust image registration/conflation algorithm

Robust imagery conflation, co-registration and geo-referencing are critical in many applications such as fusion of multispectral data from multiple sensors. An algorithm that matches linear features from two images can be very accurate because it produces many matched points, but the selection of robust and invariant points is a long-standing challenge. This paper defines several concepts of invariance and robustness of image matching algorithms relative to pairs of transformations. A new affine invariant and noise robust registration/conflation algorithm (EAD algorithm) based on algebraic structures of linear and area features is proposed. A class of Equal Area Divider (EAD) points is a major new component of the EAD-based registration/conflation algorithm. These points are both affine invariant and robust to noise. EAD points augment several known invariant or robust points such as Ramer point (R-point, the curve point with max distance from its chord), curve middle (CM) point and equal shoulders (ES) points that we have used in our structural algorithms previously. R point is affine invariant but is not noise robust, CM and ES are noise robust but not affine invariant. It is shown in this paper that if CM and ES points are computed after affine transform of the first image to the second one using EAD points, then CM and ES points are the same (or in the T-robust vicinity) of correct CM and ES points found in the matched feature in the second image. This statement is formalized and is used in EAD algorithm design.