Deformation‐Driven Shape Correspondence

Zhang, H.; Sheffer, Alla; Cohen–Or, Daniel; Zhou, Qun; Kaick, Oliver van; Tagliasacchi, Andrea

doi:10.1111/j.1467-8659.2008.01283.x

Cited by 150 publications

(198 citation statements)

References 41 publications

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“…The main drawback of this method is that it relies heavily on nonintuitive user-defined parameters, which makes the method hard to use. Zhang et al [27] propose a technique that solves the correspondence problem by finding a small set of features and by choosing the best feature correspondence as the one that minimizes a deformation energy. To improve the efficiency of the algorithm, the tree of all matching features is pruned if the features are too dissimilar.…”

Section: Related Workmentioning

confidence: 99%

Landmark-free posture invariant human shape correspondence

Wuhrer

Shu

2011

Vis Comput

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We consider the problem of computing accurate point-to-point correspondences among a set of human bodies in varying postures using a landmark-free approach. The approach learns the locations of the anthropometric landmarks present in a database of human models in strongly varying postures and uses this knowledge to automatically predict the locations of these anthropometric landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a rigged template model and the newly available scan.

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Section: Related Workmentioning

confidence: 99%

Landmark-free posture invariant human shape correspondence

Wuhrer

Shu

2011

Vis Comput

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“…e.g. (Bronstein et al, 2011;Funkhouser and Shilane, 2006;Gelfand et al, 2005;Lipman and Funkhouser, 2009;Wang et al, 2010;Windheuser et al, 2011;Zeng et al, 2010;Zhang et al, 2008), the only fully-automatic non-rigid approaches to intra-operative registration of range data in abdominal procedures have been applied in open surgery (dos and do not provide real-time performance. To avoid the computational demands of repeating the registration process over time, an alternative registration approach involves continuously updating an initially performed registration via tissue tracking using the endoscopic image information acquired during surgery.…”

Section: Discussionmentioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

242

130

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One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilites by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

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“…Zhang et al . [38] performed the matching using extremal curvature feature points and a combinatorial tree traversal algorithm, but its high complexity allowed them to match only a small number of points.Lipman and Funkhouser [20] used the fact that isometric transformation between two shapes is equivalent to a Möbius transformation between their conformal mappings, and obtained this transformation by comparing the respective conformal factors. However, there is no guarantee that this result minimizes the difference between pairwise geodesic distances of matched points.…”

Section: Non-rigid Correspondence In a Briefmentioning

confidence: 99%

Hierarchical Matching of Non-rigid Shapes

Raviv

Dubrovina

Kimmel

2012

Lecture Notes in Computer Science

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Abstract. Detecting similarity between non-rigid shapes is one of the fundamental problems in computer vision. While rigid alignment can be parameterized using a small number of unknowns representing rotations, reflections and translations, non-rigid alignment does not have this advantage. The majority of the methods addressing this problem boil down to a minimization of a distortion measure. The complexity of a matching process is exponential by nature, but it can be heuristically reduced to a quadratic or even linear for shapes which are smooth two-manifolds. Here we model shapes using both local and global structures, and provide a hierarchical framework for the quadratic matching problem.

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Deformation‐Driven Shape Correspondence

Cited by 150 publications

References 41 publications

Landmark-free posture invariant human shape correspondence

Landmark-free posture invariant human shape correspondence

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Hierarchical Matching of Non-rigid Shapes

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