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Three-dimensional segmentation of medical volumetric image data as a basis of 3D reconstruction has important significance in biomedicine engineering. However, noises or intensity inhomogeneity in practical application often make 3D medical images segmentation become formidable. To effectively alleviate these problems, this paper presents a novel variational level set framework using neighbors statistical...
This paper presents a new approach to creating variational level set model for vegetation detection combining 3D irregular point clouds and aerial image simultaneously acquired by LiDAR light scanning and imaging device. Firstly, a fundamental statistical level set framework is built which integrates texture information to improve the quality of vegetation detection. Then, several derived products...
This paper studies an new approach to creating a variational level set model for buildings detection by combining LiDAR point clouds and Aerial image data. The level set model introduces an object-based image analysis technique. Firstly, a fundamental object-based level set framework is built by neighbor analysis of remote sensing image. Then, several derived products directly or indirectly from LiDAR...
Multispectral remotely sensing imagery with high spatial resolution, such as QuickBird, IKONOS satellite imagery or Aerial imagery, especially in urban scenes, often perform spectral variations and rich details within a category, resulting in a poor accuracy of classification. To seek an efficient solution, this paper presents a non-parametric and variational multiple level set model by a joint use...
Recently, active contour models based on local information have emerged in image segmentation. These models are more robust to local variations of region of interest. But it also brought some new problems, such as local minimum, higher computational cost. To effectively alleviate these problems, this paper presents a novel fast active contour model driven by global-local statistical energy. Firstly,...
Satellite imagery especially with high spatial resolution often shows spectral variations and details disturbances in a class. These characteristics bring difficulties to people who are working at automatic classification in the remote sensing fields. To seek more effective method, this paper presents a new multiple level set model to implement unsupervised classification for multispectral images...
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