The modeling of moving objects is the basic of the information management of them. The common practice of moving objects’ modeling is to regard the objects as points which have different positions in the space at different time regardless of their structures, sizes, colors, etc. The General Motion Model (GMM) proposed here is good for representing both 2D and 3D moving objects. It combines sampling method and function method, and encapsulates all the data, including parameters and sampling data, and operations with object oriented method. GMM mixes discrete processing and continuous processing of motion data, and offers multiple functions to fulfill the needs of motion data LOD, thus gives users the option to balance processing speed and precision. Nonlinear interpolations and extrapolations could be applied to GMM as well as linear interpolations and extrapolations. GMM also gives users the flexibility of choosing interested dynamic attributes of moving objects in dynamic attributes set, which are not required to be presented explicitly, incrementally and separately as in other models. Experiments show that GMM is easy to implement and easy to operate. With proper indexing of motion data, GMM is also efficient in spatiotemporal data query.
The spatial and temporal characteristics of the data used to describe moving objects' movement make them large in quantity and complex to manage. Different queries to motion data ask for various organization methods. According to the needs of most applications, general motion model is used to represent the translation and rotation of moving objects during a period of time. Because the motion data are multidimensional in space and time dimension, 2n tree is employed to construct the main part of the index to these data. Meanwhile other kinds of index algorithms should be added to the index structure so as to meet the needs of queries other than state queries only related to a specific epoch. Thus, motion data index structure (MDIS) is constructed as a multi-entry multi-level index structure for the organization of motion data. Each index within MDIS may work alone or cooperate with each other to process different kinds of queries. The extra space needed for MDIS is only about 5%~6% of the total storage space of motion data themselves. And the respond time to each query is much decreased and acceptable to most applications dealing with moving objects.
In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed sensor with given spatial resolution are a mixture of soil and vegetation spectra, so vegetation covering on soil influences the accuracy of soils surveying by remote sensing. Mixed pixel spectra are described as a linear combination of endmember signature matrix with appropriate abundance fractions correspond to it in a linear mixture model. According to the principle of this model, abundance fractions of endmembers in every pixel were calculated using unsupervised fully constrained least squares(UFCLS) algorithm. Then the signature of vegetation correspond to its abundance fraction was eliminated, and other endmember signatures covered by vegetation were restituted by scaling their abundance fractions to sum the original pixel total and recalculating the model. After above processing, de-vegetated reflectance images were produced for soils surveying. The accuracies of paddy soils classified using these characteristic images were better than that of using the raw images, but the accuracies of zonal soils were inferior to the latter. Compared to many other image processing methods, such as K-T transformation and ratio bands, the linear spectral unmixing to removing vegetation produced slightly better overall accuracy of soil classification, so it was useful for soils surveying by remote sensing.
The paper researches texture extraction using wavelet transform. After introducing the wavelet transform and the texture analysis methods, the image is decomposed by wavelet transform, and the sub-images are gained. Secondly, the paper takes entropy and mean as texture parameter, so the texture image is an entropy or mean image. Finally, the image is classified by the spectral and texture information. The size of the texture calculating window and the treatment to the sub-image are researched in this paper. On condition that the spectral classification adding with texture feature, the precision will improve 4% averagely. Wavelet transform can decomposed image at several levels, so it can provide many information to classify and extract, which is helpful to those applications. Because of the texture window, texture image has fuzzy edge, it will lead to error for the image that have fine object or the area with different objects interleaved.
It needs a lot of consideration to manage the presentation of a large amount of moving objects in virtual environment. Motion state model (MSM) is used to represent the motion of objects and 2n tree is used to index the motion data stored in database or files. To minimize the necessary memory occupation for static models, cache with LRU or FIFO refreshing is introduced. DCT and wavelet work well with different playback speeds of motion presentation because they can filter low frequencies from motion data and adjust the filter according to playback speed. Since large amount of data are continuously retrieved, calculated, used for displaying, and then discarded, multithreading technology is naturally employed though single thread with carefully arranged data retrieval also works well when the number of objects is not very big. With multithreading, the level of concurrence should be placed at data retrieval, where waiting may occur, rather than at calculating or displaying, and synchronization should be carefully arranged to make sure that different threads can collaborate well. Collision detection is not needed when playing with history data and sampled current data; however, it is necessary for spatial state prediction. When the current state is presented, either predicting-adjusting method or late updating method could be used according to the users' preference.
In this paper, a topology-based strategy for 3D reconstruction of complicated buildings from stereo image pair is put forward. It comes from our investigation on the applicability of topology analysis and a strongly topology-driven process that combines different levels of geometrical description with different levels of topological abstraction.
The authors emphasize the topology-based strategy on different levels of geometrical description: Firstly a topology-based 3D data model is presented in which the topological relationships within a building or between geometrical objects are described implicitly or explicitly. Secondly based on description of vertexes level, interested vertexes are collected from stereo image pair and saturated attribute of each interior vertex is defined, furthermore an adjacency table is defined to store the connection attributes of verges automatically. Thirdly surfaces are looked on as polygons with closed verges on the basis of bi-directional querying of the adjacency table. Finally complicated buildings are described as graphs with interior and exterior topological attributes. Based on the strategy mentioned above, a software platform for 3D reconstruction of complicated buildings is built up. The efficiency of suggested method is examined through practical experiments.
The management of dynamics objects is significant to VR systems, especially its data management, which plays an important role in the efficiency and reality of the systems. By separating and classifying the position, orientation, geometry structure and other relative attributes of moving objects into static and dynamic categories, different methods are used to process them. Generally, two steps are taken. First, we set up the static model of the objects, and then add dynamic attributes to them to make them dynamic. This paper pays more attention to dynamic attributes, especially spatial state time series, which may take various forms when stored in database. A compression algorithm, Hyper-Rectangle Compression (HRC), is introduced to avoid over dilating of the storage space with rapid growing time series data.
Moving objects are complicated to manage because they involve temporal attributes as well as spatial attributes. There are two methods to represent the motion of moving objects, function method and sampling method. Motion state modeling, based on sampling method, can give object's position, orientation and their changes at a specific epoch, and encapsulates all the calculation by object orientation method. A big job is to search the motion state vectors efficiently, which can be performed with the help of 2n index trees. 2n index tree is an efficient index method to multi-dimensional data. Different kinds of motion data retrieval can be transformed to basic searching in 2n index trees. With proper operation algorithm, 2n index trees work well with the indexing and retrieval of moving objects.