As the number of available 3D models grows, there is an increasing need to index and retrieve them according to their contents. This paper provides a survey of the up-to-date methods for content-based 3D model retrieval. First, the new challenges encountered in 3D model retrieval are discussed. Then, the system framework and some key techniques of content-based 3D model retrieval are identified and explained, including canonical coordinate normalization and preprocessing, feature extraction, similarity match, query representation and user interface, and performance evaluation. In particular, similarity measures using semantic clues and machine learning methods, as well as retrieval approaches using nonshape features, are given adequate recognition as improvements and complements for traditional shape-matching techniques. Typical 3D model retrieval systems and search engines are also listed and compared. Finally, future research directions are indicated, and an extensive bibliography is provided. 相似文献
Scanning tunneling microscopy (STM) can be used to image individual biological molecules, such as proteins, in vacuum or air. This requires sample dehydration and thus may not reflect the native state of the molecule. Extensive efforts have been made to image single proteins in solution using STM; however, the images have revealed only round or oval shapes with no sub-molecular details. Here, we present the sub-molecular features of streptavidin proteins under physiological conditions using a homebuilt low-leakage-current and highstability liquid phase STM. The N-lobe, C-lobe, and C-terminal tail of the epidermal growth factor receptor kinase domains were also resolved in solution. Our results demonstrate that the structure, morphology, and dynamics of a protein molecule can be examined under physiological conditions by the STM.
Owing to sparseness, directly clustering high-dimensional data is still a challenge problem. Therefore, obtaining their low-dimensional compact representation by dimensional reduction is an effective method for clustering high-dimensional data. Most of existing dimensionality reduction methods, however, are developed originally for classification (such as Linear Discriminant Analysis) or recovering the geometric structure (known as manifold) of high-dimensional data (such as Locally Linear Embedding) rather than clustering purpose. Hence, a novel nonlinear discriminant clustering by dimensional reduction based on spectral regularization is proposed. The contributions of the proposed method are two folds: (1) it can obtain nonlinear low-dimensional representation that can recover the intrinsic manifold structure as well as enhance the cluster structure of the original high-dimensional data; (2) the clustering results can also be obtained in the dimensionality reduction procedure. Firstly, the desired low-dimensional coordinates are represented as linear combinations of predefined smooth vectors with respect to the data manifold, which are characterized by a weighted graph. Then, the optimal combination coefficients and the optimal cluster assignment matrix are computed by maximizing the ratio between the between-cluster scatter and the total scatter simultaneously as well as preserving the smoothness of the cluster assignment matrix with respect to the data manifold. Finally, the optimization problem is solved in an iterative procedure, which is proved to be convergent. Experiments on UCI data sets and real world data sets demonstrated the effectiveness of the proposed method for both clustering and visualization high-dimensional data set. 相似文献
A numerical model for the split Hopkinson bar fly-away technique is presented to evaluate the performance of accelerometers measuring large amplitude pulses. Simulation results based on the numerical model indicate that the rise time of the incident stress pulse in the incident bar and the disk length are of appropriate lengths for the disk response to be accurately approximated as a rigid-body motion. Strain-time histories demonstrate that the incident strain pulse is non-dispersive. The rigid-body acceleration of the disk is derived from analytical models with stress at the incident bar/disk interface, incident strain-time data, and particle velocity on the free end of the disk calculated from numerical results. Thus, accelerations measured using the accelerometer and those derived from the models can be compared. These acceleration-time pulses show good agreement. The numerical model of the split Hopkinson bar fly-away technique can be used to calibrate high g accelerometers. 相似文献