TY - JOUR
T1 - Convex Hull Aided Registration Method (CHARM)
AU - Fan, Jingfan
AU - Yang, Jian
AU - Zhao, Yitian
AU - Ai, Danni
AU - Liu, Yonghuai
AU - Wang, Ge
AU - Wang, Yongtian
PY - 2017/9/1
Y1 - 2017/9/1
N2 - Non-rigid registration finds many applications such as photogrammetry, motion tracking, model retrieval, object recognition. In this paper we propose a novel convex hull aided registration method (CHARM) to match two point sets subject to a non-rigid transformation. First, two convex hulls are extracted from the source and target respectively. Then, all points of the point sets are projected onto the reference plane through each triangular facet of the hulls. From these projections, invariant features are extracted and matched optimally. The matched feature point pairs are mapped back onto the triangular facets of the convex hulls to remove outliers that are outside any relevant triangular facet. The rigid transformation from the source to the target is robustly estimated by the random sample consensus (RANSAC) scheme through minimizing the distance between the matched feature point pairs. Finally, these feature points are utilized as the control points to achieve non-rigid deformation in the form of thin-plate spline of the entire source point set towards the target one. The experimental results based on both synthetic and real data show that the proposed algorithm outperforms several state-of-the-art ones with respect to sampling, rotational angle, data noise. In addition, the proposed CHARM algorithm also shows higher computational efficiency compared to these methods.
AB - Non-rigid registration finds many applications such as photogrammetry, motion tracking, model retrieval, object recognition. In this paper we propose a novel convex hull aided registration method (CHARM) to match two point sets subject to a non-rigid transformation. First, two convex hulls are extracted from the source and target respectively. Then, all points of the point sets are projected onto the reference plane through each triangular facet of the hulls. From these projections, invariant features are extracted and matched optimally. The matched feature point pairs are mapped back onto the triangular facets of the convex hulls to remove outliers that are outside any relevant triangular facet. The rigid transformation from the source to the target is robustly estimated by the random sample consensus (RANSAC) scheme through minimizing the distance between the matched feature point pairs. Finally, these feature points are utilized as the control points to achieve non-rigid deformation in the form of thin-plate spline of the entire source point set towards the target one. The experimental results based on both synthetic and real data show that the proposed algorithm outperforms several state-of-the-art ones with respect to sampling, rotational angle, data noise. In addition, the proposed CHARM algorithm also shows higher computational efficiency compared to these methods.
KW - convex hull
KW - invariant feature
KW - non-rigid registration
KW - parallel projection
KW - Point set
UR - http://www.scopus.com/inward/record.url?scp=85029334090&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2016.2602858
DO - 10.1109/TVCG.2016.2602858
M3 - Article
C2 - 28113589
AN - SCOPUS:85029334090
SN - 1077-2626
VL - 23
SP - 2042
EP - 2055
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 9
M1 - 7557083
ER -