Segmenting a complex 3D surface model into some visually meaningful sub-parts is one of the fundamental problems in digital geometry processing. In this paper, a novel segmentation approach of point-sampled surfaces i...Segmenting a complex 3D surface model into some visually meaningful sub-parts is one of the fundamental problems in digital geometry processing. In this paper, a novel segmentation approach of point-sampled surfaces is proposed, which is based on the level set evolution scheme. To segment the model so as to align the patch boundaries with high curvature zones, the driven speed function for the zero level set inside narrow band is defined by the extended curvature field, which approaches zero speed as the propagating front approaches high curvature zone. The effectiveness of the proposed approach is demonstrated by our ex- perimental results. Furthermore, two applications of model segmentation are illustrated, such as piecewise parameterization and local editing for point-sampled geometry.展开更多
Three extraction methods, ultrasonic assisted extraction (USE), microwave assisted extraction (MSE), and conventional single extraction (CSE), in conjunction with the modified three-stage BCR sequential extraction pro...Three extraction methods, ultrasonic assisted extraction (USE), microwave assisted extraction (MSE), and conventional single extraction (CSE), in conjunction with the modified three-stage BCR sequential extraction procedure (SEP) were applied to examine the contents of Cd, Cu, Cr, Ni, Pb and Zn from lake sediment samples, to know whether these techniques can reduce extraction time and improve reproducibility. The SEP and developed alternative single extrac- tion methods were validated by the analysis of certified reference material BCR 601. By the use of optimized sonication and microwave conditions, steps 1, 2 and 3 of the BCR sequential extraction methods (excluding the hydrogen peroxide digestion in step 3, which was not performed with sonication and microwave) could be completed in 15-30 min and 60- 150 s, respectively. The recoveries of total extractable metal contents in BCR 601, obtained by three single extractions ranged from 93.3%-102%, 88.9%-104% and 81.2%-96.2% for CSE, USE and MSE, respectively. The precision of the single extraction methods was found in the range of 3.7%-9.4% for all metals (n = 6).展开更多
To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. Howeve...To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase o f manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines （MARS） and smooth thin plate spline regression （TPSR）. Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression （SVR） based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com- parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in- teractive natural image segmentation.展开更多
基金Project supported by the National Basic Research Program (973) of China (No. 2002CB312101)the National Natural Science Foundation of China (Nos. 60503056, 60373036, 60333010)the Education Department of Zhejiang Province, China (No. 20060797)
文摘Segmenting a complex 3D surface model into some visually meaningful sub-parts is one of the fundamental problems in digital geometry processing. In this paper, a novel segmentation approach of point-sampled surfaces is proposed, which is based on the level set evolution scheme. To segment the model so as to align the patch boundaries with high curvature zones, the driven speed function for the zero level set inside narrow band is defined by the extended curvature field, which approaches zero speed as the propagating front approaches high curvature zone. The effectiveness of the proposed approach is demonstrated by our ex- perimental results. Furthermore, two applications of model segmentation are illustrated, such as piecewise parameterization and local editing for point-sampled geometry.
基金Project supported by the Higher Education Commission of Pakistan (No.Ac-I/GS/963)
文摘Three extraction methods, ultrasonic assisted extraction (USE), microwave assisted extraction (MSE), and conventional single extraction (CSE), in conjunction with the modified three-stage BCR sequential extraction procedure (SEP) were applied to examine the contents of Cd, Cu, Cr, Ni, Pb and Zn from lake sediment samples, to know whether these techniques can reduce extraction time and improve reproducibility. The SEP and developed alternative single extrac- tion methods were validated by the analysis of certified reference material BCR 601. By the use of optimized sonication and microwave conditions, steps 1, 2 and 3 of the BCR sequential extraction methods (excluding the hydrogen peroxide digestion in step 3, which was not performed with sonication and microwave) could be completed in 15-30 min and 60- 150 s, respectively. The recoveries of total extractable metal contents in BCR 601, obtained by three single extractions ranged from 93.3%-102%, 88.9%-104% and 81.2%-96.2% for CSE, USE and MSE, respectively. The precision of the single extraction methods was found in the range of 3.7%-9.4% for all metals (n = 6).
基金the National Natural Science Foundation of China （Nos. 61071176, 61171192, and 61272337） and the Doctoral
文摘To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase o f manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines （MARS） and smooth thin plate spline regression （TPSR）. Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression （SVR） based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com- parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in- teractive natural image segmentation.
