Hierarchical Correspondence of 3D Faces Based on Thin Plate Spline Deformation (HCTD)

Aiming at establishing physiological consistent point correspondence between 3D faces, this paper proposes a new hierarchical correspondence method based on thin plate spline deformation called HCTD by introducing local geometric constraints. Firstly, mark feature points in unified Frankfurt Coordinate and the sample face deform based on thin plate spline function according to strict correspondences of feature points, so the sample face approximately coincide with the template; Secondly, build voxel models respectively and select vertexes with salient feature from the template as the current under-corresponding vertex, and the candidate set of the corresponding vertex on sample face is determined by local relative position geometric constraint and Euclidean Distance constraint. Finally, the optimal corresponding vertex is selected according to the weighted distance of local geometric features. Experimental results prove that HCTD can establish point correspondence of faces with higher precision than existing methods.

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A ROBUST METHOD FOR STEREO VISUAL ODOMETRY BASED ON MULTIPLE EUCLIDEAN DISTANCE CONSTRAINT AND RANSAC ALGORITHM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w1-219-2017 ◽

2017 ◽

Vol XLII-3/W1 ◽

pp. 219-224

Author(s):

Q. Zhou ◽

X. Tong ◽

S. Liu ◽

X. Lu ◽

S. Liu ◽

...

Keyword(s):

Euclidean Distance ◽

Visual Odometry ◽

Distance Constraint ◽

Feature Points ◽

Matching Process ◽

Robotic Motion ◽

Challenging Environment ◽

Left Image ◽

Key Steps ◽

Ransac Algorithm

Visual Odometry (VO) is a critical component for planetary robot navigation and safety. It estimates the ego-motion using stereo images frame by frame. Feature points extraction and matching is one of the key steps for robotic motion estimation which largely influences the precision and robustness. In this work, we choose the Oriented FAST and Rotated BRIEF (ORB) features by considering both accuracy and speed issues. For more robustness in challenging environment e.g., rough terrain or planetary surface, this paper presents a robust outliers elimination method based on Euclidean Distance Constraint (EDC) and Random Sample Consensus (RANSAC) algorithm. In the matching process, a set of ORB feature points are extracted from the current left and right synchronous images and the Brute Force (BF) matcher is used to find the correspondences between the two images for the Space Intersection. Then the EDC and RANSAC algorithms are carried out to eliminate mismatches whose distances are beyond a predefined threshold. Similarly, when the left image of the next time matches the feature points with the current left images, the EDC and RANSAC are iteratively performed. After the above mentioned, there are exceptional remaining mismatched points in some cases, for which the third time RANSAC is applied to eliminate the effects of those outliers in the estimation of the ego-motion parameters (Interior Orientation and Exterior Orientation). The proposed approach has been tested on a real-world vehicle dataset and the result benefits from its high robustness.

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Research of Medical Image Non-Rigid Registration Based on TPS-SEMISURF Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.791-793.2112 ◽

2013 ◽

Vol 791-793 ◽

pp. 2112-2117 ◽

Cited By ~ 1

Author(s):

Ao Shuang Dong ◽

Ben Qiang Yang ◽

Dan Yang Zhao ◽

Bao Chun He ◽

Li Bo Zhang ◽

...

Keyword(s):

Thin Plate ◽

Medical Image ◽

Thin Plate Spline ◽

Human Interaction ◽

Feature Points ◽

Interaction Method ◽

Rigid Registration ◽

Traditional Algorithm ◽

Speed Up ◽

Novel Algorithm

Aiming at avoiding misregistration in complicated medical image registration based on SURF (Speed-Up Robust Features)-TPS (Thin-Plate Spline), we propose a novel algorithm. This method is based on SURF and human interaction method for feature extraction. Then we improve SURF-TPS and propose an algorithm named TPS-SEMISURF which obtains the deformation field by calculating the Thin-plate spline of the feature points, and finally does the medical image non-rigid registration according to the parameters. Experimental results showed that the proposed method can register medical images effectively. It has a good robustness and owns better precision and rate than traditional algorithm.

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3D Mesh Pre-processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

Remote Sensing ◽

10.3390/rs13112145 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2145

Author(s):

Yawen Liu ◽

Bingxuan Guo ◽

Xiongwu Xiao ◽

Wei Qiu

Keyword(s):

Real Data ◽

Geometric Constraints ◽

Feature Point ◽

Feature Points ◽

Denoising Method ◽

3D Mesh ◽

Mesh Denoising ◽

Scene Structure ◽

Sharp Edges ◽

Update Strategy

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

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