In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and...In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.展开更多
Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient.The representation of hand gestures is critical for recognition.In this paper,we propose...Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient.The representation of hand gestures is critical for recognition.In this paper,we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition.The depth maps of hand gestures captured via the Kinect sensors are used in our method,where the 3D hand shapes can be segmented from the cluttered backgrounds.To extract the pattern of salient 3D shape features,we propose a new descriptor-3D Shape Context,for 3D hand gesture representation.The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition.The description of all the 3D points constructs the hand gesture representation,and hand gesture recognition is explored via dynamic time warping algorithm.Extensive experiments are conducted on multiple benchmark datasets.The experimental results verify that the proposed method is robust to noise,articulated variations,and rigid transformations.Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.展开更多
The southern Ferkessédougou batholith in the center-west of Côte d’Ivoire is the study area. The geology of this area includes granitoids (granodiorite, two-mica granite, biotite granite and muscovite g...The southern Ferkessédougou batholith in the center-west of Côte d’Ivoire is the study area. The geology of this area includes granitoids (granodiorite, two-mica granite, biotite granite and muscovite granite) and metasediment panels. Petrographic studies were coupled with geochemical analyzes on the whole rock in order to provide new elements in the structural evolution of this portion of the West African craton. Petrographic data show that the basement of the Bonon area is partly identical to that of the northern part of the batholith. The structural data reveal three major phases of deformation that structured the study area. As for the geochemical data carried essentially on samples of granitoids, they indicated a high-k affinity the I type granite characteristics. The spectra of the REE normalized to chondrites, have moderate slopes with a fractionation highlighted by the ratios (La/Sm)N = 1.93 - 4.56 and (La/Yb)N = 7.69 - 32.28. The multi-element diagrams revealed negative anomalies in Ta-Nb implying the partial melting of a crust of TTG composition. Studies for the geotectonic environment have shown that the granitoids of the Bouaflé and Bonon region were emplaced in an arc environment associated with a subduction zone.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant Nos.U20A20197,62306187the Foundation of Ministry of Industry and Information Technology TC220H05X-04.
文摘In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.
基金supported by the National Natural Science Foundation of China(61773272,61976191)the Six Talent Peaks Project of Jiangsu Province,China(XYDXX-053)Suzhou Research Project of Technical Innovation,Jiangsu,China(SYG201711)。
文摘Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient.The representation of hand gestures is critical for recognition.In this paper,we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition.The depth maps of hand gestures captured via the Kinect sensors are used in our method,where the 3D hand shapes can be segmented from the cluttered backgrounds.To extract the pattern of salient 3D shape features,we propose a new descriptor-3D Shape Context,for 3D hand gesture representation.The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition.The description of all the 3D points constructs the hand gesture representation,and hand gesture recognition is explored via dynamic time warping algorithm.Extensive experiments are conducted on multiple benchmark datasets.The experimental results verify that the proposed method is robust to noise,articulated variations,and rigid transformations.Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.
基金Supported by National Natural Science Foundation of China （60503024, 60375038, 60374032） and 0pen Fund of Key Laboratory of Industrial Controlling Technology, Zhejiang Universlty （060004）
文摘The southern Ferkessédougou batholith in the center-west of Côte d’Ivoire is the study area. The geology of this area includes granitoids (granodiorite, two-mica granite, biotite granite and muscovite granite) and metasediment panels. Petrographic studies were coupled with geochemical analyzes on the whole rock in order to provide new elements in the structural evolution of this portion of the West African craton. Petrographic data show that the basement of the Bonon area is partly identical to that of the northern part of the batholith. The structural data reveal three major phases of deformation that structured the study area. As for the geochemical data carried essentially on samples of granitoids, they indicated a high-k affinity the I type granite characteristics. The spectra of the REE normalized to chondrites, have moderate slopes with a fractionation highlighted by the ratios (La/Sm)N = 1.93 - 4.56 and (La/Yb)N = 7.69 - 32.28. The multi-element diagrams revealed negative anomalies in Ta-Nb implying the partial melting of a crust of TTG composition. Studies for the geotectonic environment have shown that the granitoids of the Bouaflé and Bonon region were emplaced in an arc environment associated with a subduction zone.
