The way we interact with spatial data has been changed from 2D map to 3D Virtual Geographic Environment(VGE).Three-dimensional representations of geographic information on a computer are known as VGE,and in particular...The way we interact with spatial data has been changed from 2D map to 3D Virtual Geographic Environment(VGE).Three-dimensional representations of geographic information on a computer are known as VGE,and in particular 3D city models provide an efficient way to integrate massive,heterogenous geospatial information and georeferenced information in urban areas.3D city modeling(3DCM)is an active research and practice topic in distinct application areas.This paper intro-duces different modeling paradigms employed in 3D GIS,virtual environment,and AEC/FM.Up-to-date 3DCM technologies are evolving into a data integration and collaborative approach to represent the full spatial coverage of a city,to model both aboveground and underground,outdoor and indoor environments including man-made objects and natural features with 3D geometry,appearance,topology and semantics.展开更多
A new method of contrast enhancement is proposed in the paper using multiscale edge representation of images, and is applied to the field of CT medical image processing. Comparing to the traditional Window technique, ...A new method of contrast enhancement is proposed in the paper using multiscale edge representation of images, and is applied to the field of CT medical image processing. Comparing to the traditional Window technique, our method is adaptive and meets the demand of radiology clinics more better. The clinical experiment results show the practicality and the potential applied value of our method in the field of CT medical images contrast enhancement.展开更多
U-Net has been widely applied in semantic segmentation tasks,but it faces challenges in the semantic segmentation of high-resolution remote sensing images due to the loss of boundary information during the downsamplin...U-Net has been widely applied in semantic segmentation tasks,but it faces challenges in the semantic segmentation of high-resolution remote sensing images due to the loss of boundary information during the downsampling process and the inherent blurriness of object boundaries in remote sensing images.We propose an advanced U-Net variant model that addresses these issues.By introducing the CBAM attention mechanism,we enhance the extraction of boundary information during the downsampling process,and by incorporating a cascaded edge detection module,we significantly improve the model’s boundary segmentation performance.As a result,the model demonstrates excellent performance in the segmentation of high-resolution remote sensing images.The results indicate that our proposed model outperforms other baseline models and exhibits superior performance.展开更多
基金Supported by the National Natural Science Foundation of China ( No. 40871212, No. 40671158), the Leading Academic Discipline Project of Shang- hai Educational Committee(No.J50104).
文摘The way we interact with spatial data has been changed from 2D map to 3D Virtual Geographic Environment(VGE).Three-dimensional representations of geographic information on a computer are known as VGE,and in particular 3D city models provide an efficient way to integrate massive,heterogenous geospatial information and georeferenced information in urban areas.3D city modeling(3DCM)is an active research and practice topic in distinct application areas.This paper intro-duces different modeling paradigms employed in 3D GIS,virtual environment,and AEC/FM.Up-to-date 3DCM technologies are evolving into a data integration and collaborative approach to represent the full spatial coverage of a city,to model both aboveground and underground,outdoor and indoor environments including man-made objects and natural features with 3D geometry,appearance,topology and semantics.
基金Supported by National Natural Science Foundation of China,under Grant No.6 0 2 710 15
文摘A new method of contrast enhancement is proposed in the paper using multiscale edge representation of images, and is applied to the field of CT medical image processing. Comparing to the traditional Window technique, our method is adaptive and meets the demand of radiology clinics more better. The clinical experiment results show the practicality and the potential applied value of our method in the field of CT medical images contrast enhancement.
文摘U-Net has been widely applied in semantic segmentation tasks,but it faces challenges in the semantic segmentation of high-resolution remote sensing images due to the loss of boundary information during the downsampling process and the inherent blurriness of object boundaries in remote sensing images.We propose an advanced U-Net variant model that addresses these issues.By introducing the CBAM attention mechanism,we enhance the extraction of boundary information during the downsampling process,and by incorporating a cascaded edge detection module,we significantly improve the model’s boundary segmentation performance.As a result,the model demonstrates excellent performance in the segmentation of high-resolution remote sensing images.The results indicate that our proposed model outperforms other baseline models and exhibits superior performance.
