Posture invariant surface description and feature extraction

Wuhrer, Stefanie; Azouz, Zouhour Ben; Shu, Chang

doi:10.1109/cvpr.2010.5540188

Cited by 9 publications

(5 citation statements)

References 25 publications

(25 reference statements)

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“…An overview of several methods to analyze and model the human body is presented in [6]. Recent works on automatic landmarking try to make the automatic analysis more robust, for example against pose variation [7] and different body types [8]. Accuracy in automatic landmarking with methods non-optimized for scanner and poses seems, however, still an open problem.…”

Section: Related Workmentioning

confidence: 99%

Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

Giachetti

Lovato

Piscitelli

et al. 2015

IEEE J. Biomed. Health Inform.

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In this paper, we present an automatic tool for estimating geometrical parameters from 3-D human scans independent on pose and robustly against the topological noise. It is based on an automatic segmentation of body parts exploiting curve skeleton processing and ad hoc heuristics able to remove problems due to different acquisition poses and body types. The software is able to locate body trunk and limbs, detect their directions, and compute parameters like volumes, areas, girths, and lengths. Experimental results demonstrate that measurements provided by our system on 3-D body scans of normal and overweight subjects acquired in different poses are highly correlated with the body fat estimates obtained on the same subjects with dual-energy X-rays absorptiometry (DXA) scanning. In particular, maximal lengths and girths, not requiring precise localization of anatomical landmarks, demonstrate a good correlation (up to 96%) with the body fat and trunk fat. Regression models based on our automatic measurements can be used to predict body fat values reasonably well.

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Section: Related Workmentioning

confidence: 99%

Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

Giachetti

Lovato

Piscitelli

et al. 2015

IEEE J. Biomed. Health Inform.

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“…In this subsection, we introduce a correspondence field d : S →ℝ 3 which defines a pointwise correspondence between the source shape S and the target shape T . As already discussed in Section 2, there is a substantial amount of work in the field of non‐rigid correspondence estimation [BK10, RBBK10, WZL*10, WAS10, ZWW*10]. These methods solve the problem without making any assumptions about the initial alignment of the shapes but, unfortunately, tend to be costly.…”

Section: Methods Descriptionmentioning

confidence: 99%

“…One class of deformable registration approaches consists of the feature‐based methods. Several papers [WAS10, WZL*10, BK10, RBBK10] proposed to use local invariant geometric descriptors to compute a one‐to‐one mapping between corresponding features on the input shapes. However, detecting feature points and establishing the correspondence can be problematic especially in the presence of noise and missing data.…”

Section: Related Workmentioning

confidence: 99%

Deformable 3D Shape Registration Based on Local Similarity Transforms

Papazov

Burschka

2011

Computer Graphics Forum

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In this paper, a new method for deformable 3D shape registration is proposed. The algorithm computes shape transitions based on local similarity transforms which allows to model not only as-rigid-as-possible deformations but also local and global scale. We formulate an ordinary differential equation (ODE) which describes the transition of a source shape towards a target shape. We assume that both shapes are roughly pre-aligned (e.g., frames of a motion sequence). The ODE consists of two terms. The first one causes the deformation by pulling the source shape points towards corresponding points on the target shape. Initial correspondences are estimated by closestpoint search and then refined by an efficient smoothing scheme. The second term regularizes the deformation by drawing the points towards locally defined rest positions. These are given by the optimal similarity transform which matches the initial (undeformed) neighborhood of a source point to its current (deformed) neighborhood. The proposed ODE allows for a very efficient explicit numerical integration. This avoids the repeated solution of large linear systems usually done when solving the registration problem within general-purpose non-linear optimization frameworks. We experimentally validate the proposed method on a variety of real data and perform a comparison with several state-of-the-art approaches.

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“…Correspondence computing is the key challenge in those methods, and Thin-plate spline (TPS) [1], [2] or radial basis function (RBF) [3] interpolation are commonly used. Several papers [4], [5], [6], [7] proposed to use local invariant shape descriptors to compute a one-to-one mapping.…”

Section: Related Workmentioning

confidence: 99%

Topology-Adaptive Non-Rigid Registration for 3D Facial Scans

Gao¹,

Li²,

Hao³

et al. 2012

IJFCC

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3D scans are usually stored as range images or point clouds, which are inconvenient to be utilized in common graphics pipelines. Therefore non-rigid registration is required to convert the scans into triangulated 3D models. Traditional non-rigid registration methods deform and register a topologystable template to approximate the point cloud without considering the complexity of the scanned object, this will lead to bad registration or redundancies in the results, especially for 3D dynamic, densely-sampled facial scans with a large number of degrees of freedom. To address this problem, in this paper we propose a new topology-adaptive non-rigid registration method for 3D facial scans. Our method avoids the drawback of traditional methods by combining the advantages of nonrigid registration and mesh subdivision/simplification. It reduces the redundancy of registered results effectively and guarantees the accuracy of registration by adding or removing triangles of template models. We introduce of the working principle of our method, then evaluate our technique with a variety of models, and finally give a quantitative analysis of the registration results.

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Posture invariant surface description and feature extraction

Cited by 9 publications

References 25 publications

Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

Deformable 3D Shape Registration Based on Local Similarity Transforms

Topology-Adaptive Non-Rigid Registration for 3D Facial Scans

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