Given a current point, a final destination, and visiting data point categories on a trip, a sequenced route query finds the shortest route which meets the query condition. Several algorithms have been proposed targeting to this query. When this query is used in real applications, the popularity of the visiting points (for example, restaurant, shopping center, etc.) in a trip are also important besides the length of the route. Therefore, a recommendation query method that evaluates the goodness by multiple criteria, such as the length and the popularity, is requested. This type of queries has been called Top-k query. This paper proposes a fast Top-k query method targeting to sequenced route queries.

In this paper, we propose a novel synthesis technique for BTFs. A BTF (bidirectional texture function) is a 6D function which can represent appearances of a texture under arbitrary view and lighting conditions. Until now, several approaches of BTF synthesis have been researched. For ordinary textures, patch based methods are promising techniques for texture synthesis. However, it has not been effectively tried to BTFs yet. This is mainly because data size of BTFs is so large and it is not easy to apply the techniques to BTFs. Further, efficient rendering of BTFs is still under research. In this paper, we extend a patch based synthesis technique for BTFs by utilizing compact representation of BTFs. In addition, we propose a novel technique to render the synthesized BTFs efficiently. With our proposed method, we can successfully and effectively render objects with complicated surfaces under arbitrary sizes.

The development of a graphical kernel for geographical information systems is reported. It is based on a multi-dimensional data structure called GBD tree which provides an efficient environment for spatial data retrieval. Graphic entities are classified and represented by a small set of primitive geometrical entities which are implemented as a C++ class library. Applications can derive their own data types from the providing kernel by exploiting the IFO object-oriented data model. Although the main purpose is to support the GIS applications, it can be expected that this graphic kernel can be extended to a general-purpose one for graphic applications.>

A new multidimensional data structure, multidimensional tree (MD-tree), is proposed. The MD-tree is developed by extending the concept of the B-tree to the multidimensional data, so that the MD-tree is a height balanced tree similar to the B-tree. The theoretical worst-case storage utilization is guaranteed to hold more than 66.7% (2/3) of full capacity. The structure of the MD-tree and the algorithms to perform the insertion, deletion, and spatial searching are described. By the series of simulation tests, the performances of the MD-tree and conventional methods are compared. The results indicate that storage utilization is more than 80% in practice, and that retrieval performance and dynamic characteristics are superior to conventional methods.>

Color video images have become a very important media in communication. This has increased the necessity of displaying and handling color video images on various types of computers. In computerized color image processing, most color images are represented by 24 bits per pixel. Such images usually contain a lot of redundancy and require a large amount of space to be stored. Furthermore, they require expensive full color display devices, so that many general-purpose computers which have only colormap display devices are not capable of displaying them. In order to lower the display and the storage cost, color image quantization algorithms are needed to reduce the number of colors in original images. The authors propose a color quantization method for video images which efficiently generates color quantized video images with stable color allocations.>

An efficient spatial data management method for non-zero size spatial objects, called R(Region)MD-tree, is proposed. In this method, a graphic object in an N-dimensional space is represented as a point in a 2*N dimensional space by the centroid and the extension in each axial direction. The point data are managed by the MD-tree after applying the coordinate transformation. The point location problem or range searching in the original N-dimensional space are carried out by the hyper-rectangular range searching to the point data in the 2*N dimensional space. In this representation and data management, data objects are sorted and classified according to not only the location but also the size of the data. By the simulation experiments, the performances and efficiencies of the RMD-tree are compared with the conventional MD-tree and the R-tree. As the data objects become larger, the RMD-tree is superior in the searching speed by 2 approximately 4 times to the conventional ones, and the performances are almost comparable even if the objects are points or small graphic data.>

A new dynamic data structure for spatial retrieval called a GBD tree is proposed. The GBD tree is systematically constructed using homogeneous nodes with a small amount of extra data called a DZ expression which plays an important role in efficient multiway recursive division of N-dimensional space. On the GBD tree, CPU cost during insertion and deletion is much smaller than on an R-tree, while attaining higher spatial retrieval efficiency.>

A multilayer (ML) data structure, which is a hierarchical data structure for N-dimensional data classified into layers, is proposed. In the ML structure, a node structure is extended so that data can be stored not only in the leaves (terminal nodes) but also in internal nodes by adding a layer-management mechanism. The creation, management, and searching methods of the ML structure are described, taking two-dimensional point data classified into layers as an object. The management of line data or expansion to n(n>2)-dimensional data can be easily realized. Good retrieval performances are obtained in the case of a spatial search for a specific layer's data and that for plural layers' data.>

A flexible drawing understanding system with state transition models is proposed. The drawing processor AI-Mudams (written in C) is used as the token extractor in the embodiment discussed. Given drawing images are converted efficiently to suitable geometrical primitives, such as contour vectors, core vectors, dots loops, or, in some cases, primitives with semantics (road line, or house etc.). The understanding system kernel is implemented in Prolog, and the geometrical evaluator is also prepared in C for checking basic geometrical situations, including shape, geometrical relations, and allocations. This understanding kernel accepts the individual state transition rules corresponding to individual drawing images and recognition targets and realizes understanding in the form of bottom-up and top-down state transition. Experiments on different types of drawings reveal that the framework is flexible and effective for various kinds of drawing image.>

A multidimensional data structure, the MD-tree (multidimensional tree), is proposed. The MD-tree is developed by extending the concept of the B-tree to multidimensional data, so that the MD-tree is a height-balanced tree similar to the B-tree and, even its worst-case storage utilization is more than 66.7%. A description is given of the structure of an MD-tree and the results of a series of simulation tests which indicate that the storage utilization is more than 80% in practice and that the retrieval performance and the dynamic characteristics are superior to those of conventional methods.>