This paper discribes the importance and necessity of the study on the data structures for displaying the mining field using the interactive technology in open pit design and planning,based upon the grid block model. T...This paper discribes the importance and necessity of the study on the data structures for displaying the mining field using the interactive technology in open pit design and planning,based upon the grid block model. The commonly used data structures--rectangular array structure and quadtree structure ,are analyzed. Two compressed data structures--compressed circular link array structure and compressed doubly-linked circular array structure,are proposed,which are much more suitable for displaying the regularly gridded block model. When the two compressed data structures are adopted,the storage space can be tremendously saved and the algorithms are simple,while the requirements of the accuracy and the manipulating speed will be both satisfied for the interactive open pit short range plan formulation.展开更多
Image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping...Image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between highand low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.展开更多
文摘This paper discribes the importance and necessity of the study on the data structures for displaying the mining field using the interactive technology in open pit design and planning,based upon the grid block model. The commonly used data structures--rectangular array structure and quadtree structure ,are analyzed. Two compressed data structures--compressed circular link array structure and compressed doubly-linked circular array structure,are proposed,which are much more suitable for displaying the regularly gridded block model. When the two compressed data structures are adopted,the storage space can be tremendously saved and the algorithms are simple,while the requirements of the accuracy and the manipulating speed will be both satisfied for the interactive open pit short range plan formulation.
基金Project (No 2008AA01Z132) supported by the National High-Tech Research and Development Program of China
文摘Image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between highand low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.
