Event-based systems are gaining increasing popularity for building loosely coupled and distributed systems. Since business processes are becoming more interconnected and event-driven, event-based systems fit well for ...
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In this paper we introduce a framework for automated text recognition from images. We first describe a simple but efficient text detection and recognition method based on analysis of Maximally Stable Extremal Regions ...
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A common task in medical image analysis is the alignment of data from different sources, e.g., X-ray images and computed tomography (CT) data. Such a task is generally known as registration. We demonstrate the applica...
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This paper presents a method for statistical modeling and classification of motion trajectories using Hidden Markov Models. Mass recordings from visual surveillance are processed to extract objects trajectories. Hidde...
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This paper presents a method for statistical modeling and classification of motion trajectories using Hidden Markov Models. Mass recordings from visual surveillance are processed to extract objects trajectories. Hidden Markov Models of classes of behaviour are created upon some annotated trajectories. In this way, information about complex object behaviour of objects can be discovered. Additionally, an experiment shows the successful application of Hidden Markov Models on trajectories of people in an underground station in Roma. Finally, a comparison of efficiency on different data sets, is discussed.
The construction and processing of surface geometry can be done by connecting surface primitives to vertices, edges, and neighbors. The representation schemes of surface geometry are vertex-based, edge-based, and quad...
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The construction and processing of surface geometry can be done by connecting surface primitives to vertices, edges, and neighbors. The representation schemes of surface geometry are vertex-based, edge-based, and quad-edge-based. Catmull and Clark rule of subdivision includes three different rules to create new vertices in the refined control mesh, that are vertex, edge, and face subdivision rule. A new vertex can be created in the refined mesh as a convex combination of the vertices in the neighborhood of vertex, edge, and face in the control mesh. The first subdivision step has a quadrangle control mesh and number of vertices with valence unequal to four constant during refinement. The Charles Loop subdivision scheme is based on box spline and during a subdivision a triangle is divided into four triangles through vertex and edge subdivision rule.
Obscurances, from which ambient occlusion is a particular case, is a technology that produces natural-looking lighting effects in a faster way than global illumination. Its application in volume visualization is of sp...
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The high resolution NEXRAD Level II data provides critical information for researchers and the broader community to understand monitor, and predict weather in a timely manner. There are several limitations in existing...
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ISBN:
(纸本)9781424428601
The high resolution NEXRAD Level II data provides critical information for researchers and the broader community to understand monitor, and predict weather in a timely manner. There are several limitations in existing systems for providing easy-toaccess 3D visualization of the radar data to the user community. In this paper, we present a scalable and user driven solution for near-real-time remote radar data access, processing, and 3D visualization. The system provides multi-layered interfaces for a broad range of users with different levels of services, allowing them to interactively explore data from multiple radar stations over a time period of interest Parallel data pre-processing using the Purdue Condor pool and volume caching are implemented to help improve the system response time and scalability. The system also provides reusable radar data services and a set of access points which can be invoked by third party applications. With this hierarchical and user driven design, our system creates a rich and easy to use NEXRAD data service environment for research and education users.
This paper describes the video summarization system built for the TRECVID 2008 evaluation by the Brno team. Motivations for the system design and its overall structure are described followed by more detailed descripti...
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Tracking across non-overlapping camera views is still an unsolved problem. Appearance is a popular cue that does not work when the views are considerably different. This paper proposes tracking in the 3-D space withou...
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The consensus tree problem arises in the domain of phylogenetics and seeks to find for a given collection of trees a single tree best representing it. Usually, such a tree collection is obtained by biologists for a gi...
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ISBN:
(纸本)9781605581309
The consensus tree problem arises in the domain of phylogenetics and seeks to find for a given collection of trees a single tree best representing it. Usually, such a tree collection is obtained by biologists for a given taxa set either via different phylogenetic inference methods or multiple applications of a non-deterministic procedure. There exist various consensus methods which often have the drawback of being very strict, limiting the resulting consensus tree in terms of its resolution and/or precision. A reason for this typically is the coarse granularity of the tree metric used. To find fully resolved (binary) consensus trees of high quality, we consider the fine-grained TreeRank similarity measure and extend a previously presented evolutionary algorithm (EA) to a memetic algorithm (MA) by including different variants of local search using neighborhoods based on moves of single taxa as well as subtrees. Furthermore, we propose a variable neighborhood search (VNS) with an embedded variable neighborhood descent (VND) based on the same neighborhood structures. Finally sequential and intertwined combinations of the EA and MA with the VNS/VND are investigated. We give results on real and artificially generated data indicating in particular the benefits of the hybrid methods. Copyright 2008 ACM.
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