Until the middle of the last centurM the primary paradigms in mainstream science had been either experimental science or tiheoretical science. Starting in the 1950s, computational science began its rise to equal prominence and this change resulted in the realization of complex, large scale numerical calculations and simulations, Noeq scientific research is continuing to evolve witih further innovative changes brought by the advent of the Internet and related technologies. Thanks to the technology used in these sophisticated information systems, mest ef the hardware and sensors used to gather information are linked to these networks. Infbrmation distributed in digita1 form technically allows anybody to access it anytime and from anyplace. TIhe empirical scientific research methed based on complex largescale data collected through these networks is called "DataTcentric science." 'Ihe "Knowledge Circulation" infrastructure, which creates new value by projecting eoncrete information from our society into cyber space, analyzing and simulating it on the web, and enabling feedback from the web to real people and objects, wiII be one of the major pillars of future socio-informatics. In tihis paper, we describe the trends in methodology of datacentric science developed in various academic disciplines, and the possibility of these types of developments in socio-informatics. Furtheg we discuss the data sharingl collaborative researeh infrastructure that may result in breakthreughs in socio-informatics achieved through sharing vast arnounts gf data collected from throttghout society and utilizing information science.

Fast shortest path search between two points on a road network is essential demand in location based services (LBS). For this purpose, several types of road network distance materialization methods have been studied. The distance materialization approach is quite fast, however, it results in a huge amount of data. This paper proposes a shortest path search algorithm based on materialized-path-view constructed only on partitioned subgraphs, and its three variations referring different levels of distance materialization. The amount of pre-computed data is greatly reduced. The shortest path is retrieved by a best-first-search using a priority queue. The difference between three variations of the algorithm is the materialization level of the distance in the subgraphs. The performance of them is evaluated comparing with A* algorithm and HEPV experimentally. Through the results, we show the proposed algorithm outperforms the conventional methods.

Searching for the shortest paths from a query point to several target points on a road network is an essential operation for several types of queries in location-based services. This search can be performed using Dijkstra's algorithm. Although the A* algorithm is faster than Dijkstra's algorithm for finding the shortest path from a query point to a target point, the A* algorithm is not so fast to find all paths between each point and the query point when several target points are given. In this case, the search areas on road network overlap for each search, and the total number of operations at each node is increased, especially when the number of query points increases. In the present paper, we propose the single-source multi-target A* (SSMTA*) algorithm, which is a multi-target version of the A* algorithm. The SSMTA* algorithm guarantees at most one operation for each road network node, and the searched area on road network is smaller than that of Dijkstra's algorithm. Deng et al. proposed the LBC approach with the same objective. However, several heaps are used to manage the search area on the road network and the contents in each heap must always be kept the same in their method. This operation requires much processing time. Since the proposed method uses only one heap, such content synchronization is not necessary. The present paper demonstrates through empirical evaluations that the proposed method outperforms other similar methods.

Aggregate nearest neighbor (ANN) queries play an important role in location-based services (LBSs) when a group of users wants to find a suitable point of interest (POI) (e.g., a restaurant) beneficial to all of them. In ANN queries, a set of query points Q and an aggregate function (e.g., sum) are given, and then a POI is determined (or k POIs), which gives the minimum total travel distance from each query point to the POI. ANN query methods were first proposed giving results in Euclidean distance, and then were adapted to provide results using road-network distances which offer more practical solutions in the daily life. Among them, the incremental Euclidean restriction (IER) framework is a simple and powerful strategy for solving an ANN query using road-network distances. The IER framework consists of two phases: a candidate-generation phase and a verification phase. This paper proposes a powerful method that can be adapted to the verification phase, named a single-source multitarget A* (SSMTA*) algorithm. This paper first describes the SSMTA*, and then presents a method for its suitable application to ANN queries. Through experiments, this paper demonstrates that the proposed method outperforms the existing methods.

This paper proposes a fast trip planning query method in the road network distance. The current position, the final destination, and some number of point of interest (POI) categories visited during the trip are specified in advance. Then, the query searches the shortest route from the current position with stops at one of each specified POI category from the visiting sequence before reaching the final destination. Several such types of trip planning methods have been proposed. Among them, this paper deals with the optimal sequenced route (OSR) which is the simplest query because it has a strongest restriction on the visiting order. This paper proposes a fast incremental algorithm to find OSR candidates in the Euclidean space. Furthermore, it provides an efficient verification method for the road network distance.

Trip planning methods including the optimal sequenced route (OSR) query become a critical role to find the economical route for a trip in location based services and car navigation systems. OSR finds the shortest route, starting from an origin location and passing through a number of locations or points of interest (POIs), following the prespecified route sequence. This paper proposes a fast optimal sequenced route query algorithm from the current position to the destination by unidirectional and bidirectional searches adopting an A* algorithm. An OSR query on a road network tends to expand an extremely large number of nodes, which leads to an increase in processing time. To reduce the number of node expansions, we propose a visited POI graph (VPG) to register a single found path that connects neighboring POIs. By using a VPG, duplicated node expansions can be suppressed. We also perform experiments to show the effectiveness of our method compared with a conventional approach, in terms of the number of expanded nodes and processing time.

Most color-based multimedia data indexing systems are sensitive to changes in the illumination environment. Some color-based systems coming into practical use are claimed to be limited to target videos with stable lighting. In this paper, a fuzzy mode similarity measure is proposed to adaptively calibrate the feature-matching criterion based on the measure of the illumination instability. The purpose of this approach is to retrieve the identical semantic object from the video and to alleviate the impact of lighting changes. An information-theoretic measure is first proposed to automatically measure the illumination instability of the video. A fuzzy model is then proposed to estimate and characterize the impact of illumination changes to the distribution shape of the semantic object in the low-level visual feature space. Experiments are shown to demonstrate how the proposed measure of the illumination instability, which takes the information distribution within an image into account, can reflect the instability more effectively than other simple and straightforward measures. Experiments also show how the retrieval performance can be improved by using the fuzzy mode similarity measure.

When a real object is displayed in a virtual space by using computer graphics, the general approach is to draw the image on the basis of the geometrical information and the texture information about the object. However, when the object has a complex and fine structure, it is difficult to draw the object image precisely without a decrease in realism, since the geometrical model cannot be determined precisely. To deal with this problem, this paper proposes the following method. Small faces (microfacets) are generated according to the drawing viewpoint to form layers which are always perpendicular to the view line. Then view-dependent texture mapping is applied to each microfacet and drawing with greater realism is achieved. These processes can be implemented efficiently on an ordinary PC by using programmable graphic hardware. It is shown that view-dependent detail-level control can be realized in this method, so that complicated objects and scenes can be drawn at interactive speed. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(8): 44– 53, 2007; Published online in Wiley InterScience (). DOI 10.1002/scj.10703

Recent years have witnessed the increasing popularity of studies using 360° images captured with omnidirectional cameras. The authors have already created a database of building images using an omnidirectional camera. Omnidirectional cameras can capture 360° images in a single shot, but their optical characteristics are different from those of ordinary cameras, and their use presents several problems. Two problems in particular pertain to camera calibration and the low resolution obtained when capturing a single360° image. Numerous studies have been conducted in regard to the former, but only a few in regard to the latter due to the small number of instances in which omnidirectional camera images have been used. This has come to be regarded as an especially significant problem, as the number of opportunities for omnidirectional cameras to actually be used has been increasing of late. Resolution-enhancement techniques can be thought of as being broadly divided into hardware methods, in which a high-resolution CCD or the like is employed; and software methods, in which super-resolution or the like is performed using a series of multiple images. In this report, we discuss the use of software-driven methods to super-resolve and otherwise convert low-resolution omnidirectional camera images. Images that have been examined with conventional super-resolution techniques have hitherto been almost exclusively taken with static cameras, whereas we have adopted a novel approach in using images captured with a moving camera. Moreover, our proposed technique is applicable not only to omnidirectional cameras, but to ordinary image capture as well. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 89(6): 47–59, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20246

In this paper, an interactive object annotation approach for video database will be proposed. The same semantic objects such as characters, backgrounds, and the main subjects in keyframes of each video shots can be queried and annotated based on the similarity of low-level features such as the color, area, and position of each region. An adaptive image enhancement algorithm is used for handling the lighting changes, and a region-based fuzzy feature matching approach is used for addressing the typical feature representation impreciseness. The content provider can then select relevant keyframes interactively from the results to annotate matched objects in them according to the descriptions that are added into the model. Based on this approach, a video information system is proposed for supporting video content generation. Furthermore, a novel practical application is constructed by using this support system and implemented to show the practicability of it

A method has been developed to identify video shots of the same scene where camera flash lights are observed, and the method has been tested by using it to detect such shots from a large TV video archive. Camera flashes are often used in impressive scenes, such as interviews of important persons. Because such scenes are broadcasted repeatedly on various TV programs, a method for detecting them is a promising approach for semantic video indexing. The proposed identification method is invariant to the differences in viewpoint, illumination or any other visual environment because it depends on comparison between temporal occurrence patterns of flash lights. Furthermore, because each flash pattern is represented with a binary array, the comparison requires low computing cost. These advantages mean that the proposed method can be considered to provide semantic and efficient video analysis

It is important to measure traveling time along each network link in order to optimize urban traffic signal control. A vision sensor was developed which is able to measure traveling time between the adjacent intersections, installing a single camera at each intersection. This vision sensor covers whole the area of an intersection, and detects a region of vehicle in an image and classifies vehicle sizes and colors. The two vehicle feature sequences are compared with Dynamic Programming Matching to search the same vehicle sequences. Measured traveling time has a margin of error of 4.5% one way or the other for true traveling time. For the covering abstract see ITRD E134653.

A semantic video database system, based on an interactive approach that maps low-level features to high-level concepts, is proposed. A database of ontological semantic object models allows the user to get information about specific semantic objects, such as certain actors or other features of a TV drama. The system searches the database for key frames within the video to detect similarities in detailed or “low-level” features, such as the color, area, and position of a specific part of the frame. Since image recognition techniques are limited in their ability to fully identify and compare images, we propose an additional function in which a coarse model is used to recover a greater number of similar key frames, thus providing more relevant results. From these results, the content provider can select relevant key frames interactively; the matched objects in them are then automatically annotated according to descriptions that are added into the model by content provider. Therefore, more complex content can be generated with greater accuracy by using a combination of application-oriented operations. The system has high potential for use in object-based interactive multimedia applications. We also present an object-based video content generation application called the Drama Characters’ Popularity Voting System.