@article{
 title = {Affine object recognition and affine parameters estimation based on covariant matrix},
 type = {article},
 year = {2008},
 keywords = {Affine parameters estimation,Covariant matrix,Object recognition},
 pages = {14-18},
 volume = {1},
 id = {f314ae04-3356-3a51-a7c1-7b5113eb6162},
 created = {2024-03-04T08:01:34.471Z},
 accessed = {2024-03-04},
 file_attached = {true},
 profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2024-03-14T10:29:17.634Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 citation_key = {Ji2008},
 folder_uuids = {df28411a-ed7f-4991-8358-d39685eb4bf0},
 private_publication = {false},
 abstract = {A new method of affine object recognition and affine parameters estimation is presented. For a real-time image and a group of templates, firstly, we segment the object regions in them and compute their covariant matrices. Secondly, normalize the ellipse regions defined by covariant matrices to circle regions to get rotational invariants, and compute the similarity function value between rotational invariants of real-time image and every template respectively. Then compare the values with threshold set in advance, if more than one value is larger than threshold, take the corresponding templates as candidates, and compute affine matrix between real-time image and every candidate. Finally, transform the realtime image with every affine matrix and match the result with corresponding candidate by classical matching methods. Experimental results show that the presented method is robust to illumination, with low computational complexity, and it can realize recognition of different affine objects; in addition, on the basis of correct recognition, it can estimate affine parameters exactly, and the estimated error is within 3%. © 2008 IEEE.},
 bibtype = {article},
 author = {Ji, Hua and Li, Guiju and Wang, Yanjie},
 doi = {10.1109/ISISE.2008.282},
 journal = {2008 International Symposium on Information Science and Engineering, ISISE 2008}
}

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For a real-time image and a group of templates, firstly, we segment the object regions in them and compute their covariant matrices. Secondly, normalize the ellipse regions defined by covariant matrices to circle regions to get rotational invariants, and compute the similarity function value between rotational invariants of real-time image and every template respectively. Then compare the values with threshold set in advance, if more than one value is larger than threshold, take the corresponding templates as candidates, and compute affine matrix between real-time image and every candidate. Finally, transform the realtime image with every affine matrix and match the result with corresponding candidate by classical matching methods. Experimental results show that the presented method is robust to illumination, with low computational complexity, and it can realize recognition of different affine objects; in addition, on the basis of correct recognition, it can estimate affine parameters exactly, and the estimated error is within 3%. © 2008 IEEE.","bibtype":"article","author":"Ji, Hua and Li, Guiju and Wang, Yanjie","doi":"10.1109/ISISE.2008.282","journal":"2008 International Symposium on Information Science and Engineering, ISISE 2008","bibtex":"@article{\n title = {Affine object recognition and affine parameters estimation based on covariant matrix},\n type = {article},\n year = {2008},\n keywords = {Affine parameters estimation,Covariant matrix,Object recognition},\n pages = {14-18},\n volume = {1},\n id = {f314ae04-3356-3a51-a7c1-7b5113eb6162},\n created = {2024-03-04T08:01:34.471Z},\n accessed = {2024-03-04},\n file_attached = {true},\n profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},\n group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},\n last_modified = {2024-03-14T10:29:17.634Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n citation_key = {Ji2008},\n folder_uuids = {df28411a-ed7f-4991-8358-d39685eb4bf0},\n private_publication = {false},\n abstract = {A new method of affine object recognition and affine parameters estimation is presented. 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