A sectional curve feature extraction algorithm based on multi-scale analysis is proposed for reverse engineering. The algorithm consists of two parts: feature segmentation and feature classification. In the first part...A sectional curve feature extraction algorithm based on multi-scale analysis is proposed for reverse engineering. The algorithm consists of two parts: feature segmentation and feature classification. In the first part,curvature scale space is applied to multi-scale analysis and original feature detection. To obtain the primary and secondary curve primitives,feature fusion is realized by multi-scale feature detection information transmission. In the second part: projection height function is presented based on the area of quadrilateral,which improved criterions of sectional curve feature classification. Results of synthetic curves and practical scanned sectional curves are given to illustrate the efficiency of the proposed algorithm on feature extraction. The consistence between feature extraction based on multi-scale curvature analysis and curve primitives is verified.展开更多
We propose a framework of hand articulation detection from a monocular depth image using curvature scale space(CSS) descriptors. We extract the hand contour from an input depth image, and obtain the fingertips and fin...We propose a framework of hand articulation detection from a monocular depth image using curvature scale space(CSS) descriptors. We extract the hand contour from an input depth image, and obtain the fingertips and finger-valleys of the contour using the local extrema of a modified CSS map of the contour. Then we recover the undetected fingertips according to the local change of depths of points in the interior of the contour. Compared with traditional appearance-based approaches using either angle detectors or convex hull detectors, the modified CSS descriptor extracts the fingertips and finger-valleys more precisely since it is more robust to noisy or corrupted data;moreover, the local extrema of depths recover the fingertips of bending fingers well while traditional appearance-based approaches hardly work without matching models of hands. Experimental results show that our method captures the hand articulations more precisely compared with three state-of-the-art appearance-based approaches.展开更多
基金Supported by the Natural Science Foundation of China (50175063)
文摘A sectional curve feature extraction algorithm based on multi-scale analysis is proposed for reverse engineering. The algorithm consists of two parts: feature segmentation and feature classification. In the first part,curvature scale space is applied to multi-scale analysis and original feature detection. To obtain the primary and secondary curve primitives,feature fusion is realized by multi-scale feature detection information transmission. In the second part: projection height function is presented based on the area of quadrilateral,which improved criterions of sectional curve feature classification. Results of synthetic curves and practical scanned sectional curves are given to illustrate the efficiency of the proposed algorithm on feature extraction. The consistence between feature extraction based on multi-scale curvature analysis and curve primitives is verified.
基金supported by the National Natural Science Foundation of China(Nos.6122700461370120+5 种基金6139051061300065and 61402024)Beijing Municipal Natural Science Foundation,China(No.4142010)Beijing Municipal Commission of Education,China(No.km201410005013)the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality,China
文摘We propose a framework of hand articulation detection from a monocular depth image using curvature scale space(CSS) descriptors. We extract the hand contour from an input depth image, and obtain the fingertips and finger-valleys of the contour using the local extrema of a modified CSS map of the contour. Then we recover the undetected fingertips according to the local change of depths of points in the interior of the contour. Compared with traditional appearance-based approaches using either angle detectors or convex hull detectors, the modified CSS descriptor extracts the fingertips and finger-valleys more precisely since it is more robust to noisy or corrupted data;moreover, the local extrema of depths recover the fingertips of bending fingers well while traditional appearance-based approaches hardly work without matching models of hands. Experimental results show that our method captures the hand articulations more precisely compared with three state-of-the-art appearance-based approaches.
