The algebra of spatio-temporal intervals

[[abstract]]The relations among temporal intervals can be used to model all time dependent objects. We propose a fast mechanism for temporal relation compositions. A temporal transitive closure table is derived, and an interval-based temporal relation algebraic system is constructed. Thus, we propagate the time constraints of arbitrary two objects across long distances n by linear time. We also give a complete discussion of different possible domains of interval relations. A set of algorithms is proposed to detect time conflicts and to derive reasonable interval relations. The algorithms are extended for time-based media in an arbitrary n-dimensional space[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]19980121~19980121[[conferencelocation]]Tokyo, Japa

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

Rain Removal in Traffic Surveillance: Does it Matter?

Varying weather conditions, including rainfall and snowfall, are generally regarded as a challenge for computer vision algorithms. One proposed solution to the challenges induced by rain and snowfall is to artificially remove the rain from images or video using rain removal algorithms. It is the promise of these algorithms that the rain-removed image frames will improve the performance of subsequent segmentation and tracking algorithms. However, rain removal algorithms are typically evaluated on their ability to remove synthetic rain on a small subset of images. Currently, their behavior is unknown on real-world videos when integrated with a typical computer vision pipeline. In this paper, we review the existing rain removal algorithms and propose a new dataset that consists of 22 traffic surveillance sequences under a broad variety of weather conditions that all include either rain or snowfall. We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow. If successful, the de-rained frames of a rain removal algorithm should improve segmentation performance and increase the number of accurately tracked features. The results show that a recent single-frame-based rain removal algorithm increases the segmentation performance by 19.7% on our proposed dataset, but it eventually decreases the feature tracking performance and showed mixed results with recent instance segmentation methods. However, the best video-based rain removal algorithm improves the feature tracking accuracy by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System

Processing Structured Hypermedia : A Matter of Style

With the introduction of the World Wide Web in the early nineties, hypermedia has become the uniform interface to the wide variety of information sources available over the Internet. The full potential of the Web, however, can only be realized by building on the strengths of its underlying research fields. This book describes the areas of hypertext, multimedia, electronic publishing and the World Wide Web and points out fundamental similarities and differences in approaches towards the processing of information. It gives an overview of the dominant models and tools developed in these fields and describes the key interrelationships and mutual incompatibilities. In addition to a formal specification of a selection of these models, the book discusses the impact of the models described on the software architectures that have been developed for processing hypermedia documents. Two example hypermedia architectures are described in more detail: the DejaVu object-oriented hypermedia framework, developed at the VU, and CWI's Berlage environment for time-based hypermedia document transformations

Shape-based Image Retrieval Using Spatio-temporal Relation Computations

[[abstract]]Content-based retrieval of multimedia information is one of the most difficult research topics in multimedia computing and information retrieval. In this paper, we present a visual system which allows content-based retrieval of still image. The recognition algorithms we used are based on spatio-temporal relations. Two approaches and algorithms were developed based on the similarity between polygons. The system is incorporated with a visual interface which allows the user to specify polygons as the shape specification of pictures. The preliminary experience shows that, within an image database containing about 300 bitmapped images, the system is able to retrieve correct information of a high satisfaction.[[notice]]補正完

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end

An ICON programming technique for multimedia presentation designs

[[abstract]]Relations among temporal intervals can be used to assist the automatic generation of multimedia presentations. The authors analyze the domains of interval temporal relations. A set of algorithms is proposed to derive reasonable relations between intervals. Possible conflicts in the user specification are firstly detected and eliminated. The mechanism then constructs partial order relations among temporal intervals before the presentation time chart is built. The algorithm is extended for objects in an arbitrary n-dimensional space. Thus, presentation layouts in 2D space, or virtual reality object representations in 3D space can be constructed. They use the algorithms to design a reasoning system that generates the schedule and layout of multimedia presentations.[[conferencetype]]國際[[conferencedate]]19971028~19971031[[booktype]]紙本[[conferencelocation]]Beijing, Chin