17908385
The hierarchical degree-of-visibility tree
In this paper, we present a novel structure called the hierarchical degree-of-visibility tree (HDoV-tree) for visibility query processing in visualization systems. The HDoV-tree builds on and extends the R-tree such that 1) the search space is pruned based on the degree of visibility of objects and 2) internal nodes store level-of-details (LoDs) that represent a collection of objects in a coarser form. We propose two tree traversal algorithms that balance performance and visual fidelity, explore three storage structures for the HDoV-tree, and develop novel caching techniques for disk-based HDoV-tree. We implemented the HDoV-tree in a prototype walkthrough system called VISUAL. Our experimental study shows that VISUAL can lead to high frame rates without compromising visual fidelity.
490406
Skyline Queries Against Mobile Lightweight Devices in MANETs
Skyline queries are well suited when retrieving data according to multiple criteria. While most previous work has assumed a centralized setting this paper considers skyline querying in a mobile and distributed setting, where each mobile device is capable of holding only a portion of the whole dataset; where devices communicate through mobile ad hoc networks; and where a query issued by a mobile user is interested only in the user’s local area, although a query generally involves data stored on many mobile devices due to the storage limitations. We present techniques that aim to reduce the costs of communication among mobile devices and reduce the execution time on each single mobile device. For the former, skyline query requests are forwarded among mobile devices in a deliberate way, such that the amount of data to be transferred is reduced. For the latter, specific optimization measures are proposed for resource-constrained mobile devices. We conduct extensive experiments to show that our proposal performs efficiently in real mobile devices and simulated wireless ad hoc networks.
26245064
Feature-guided painterly image rendering
Non-photo-realistic rendering (NPR) refers to any technique which can produce a non-photo-realistic image. We present a method for automatically generating a stroke-based painting from a digital image. The rendering process generates rectangular brush strokes with suitable location, orientation and size. Inspired by the real painting process, where a painter always observes the distinctive features and decides the shape and orientation of the stroke, we apply techniques of image moment functions and texture analysis, from which features are extracted and used to guide the stroke generation. Techniques are also developed for dynamic determination of cropped image size and edge enhancement. The main features in the source image are well preserved.
62135913
Kernel-based method for tracking objects with rotation and translation
This paper addresses the issue of tracking translation and rotation simultaneously. Starting with a kernel-based spatial-spectral model for object representation, we define an l/sub 2/-norm similarity measure between the target object and the observation, and derive a new formulation to the tracking of translational and rotational object. Based on the tracking formulation, an iterative procedure is proposed. We also develop an adaptive kernel model to cope with varying appearance. Experimental results are presented for both synthetic data and real-world traffic video.
14317741
Interactive 3D modeling using only one image
For virtual reality systems, modeling of 3D objects and scenes is important and challenging. In this paper, we present an image-based interactive 3D modeling framework consisting of three major modules: photogrammetric modeling, human interaction, and texture mapping. These three modules are not sequentially used and they are mixed in the whole modeling process. The major idea is to explore the use of images in interactive modeling systems to achieve the automation. In particular, the use of only one image is addressed. On one side, unlike the common fully interactive modeling framework, the users are not required to specify some low level details interactively which can be derived automatically from the image. On the other side, it still requires human interactions to do some high level tasks that the algorithms are difficult to perform automatically. We have implemented the framework and experimental results are good.
18712320
Decomposing polygon meshes by means of critical points
Polygon mesh is among the most common data structures used for representing objects in computer graphics. Unfortunately, a polygon mesh does not capture high-level structures, unlike a hierarchical model. In general, high-level abstractions are useful for managing data in applications. In this paper, we present a method for decomposing an object represented in polygon meshes into components by means of critical points. The method consists of steps to define the root vertex of the object, define a function on the polygon meshes, compute the geodesic tree and critical points, decide the decomposition order, and extract components using backwards flooding. We have implemented the method. The preliminary results show that it works effectively and efficiently. The decomposition results can be useful for applications such as 3D model retrieval and morphing.
5239541
Link prediction approach to collaborative filtering
Recommender systems can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms
14446666
Awareness, Negation and Logical Omniscience
General Epistemic Logics suffer from the problem of logical omniscience, which is that an agent's knowledge and beliefs are closed under implication. There have been many attempts to solve the problem of logical omniscience. However, according to our intuition, sometimes an agent's knowledge and beliefs are indeed closed under implication. Based on the notion of awareness, we introduce two kinds of negations: general negation and strong negation. Moreover, four kinds of implications, general implication, strong implication, weak implication, and semi-strong implication, are introduced to correspond with the two kinds of negations. In our logics of regular awareness, explicit beliefs are not necessarily closed under general implication, which means that agents are not logically omniscient. However, explicit beliefs are closed under strong implication and semi-strong implication, which captures an intuitive closure property of beliefs.
12410756
Reasoning with Multi-version Ontologies: A Temporal Logic Approach
In this paper we propose a framework for reasoning with multi-version ontology, in which a temporal logic is developed to serve as its semantic foundation. We show that the temporal logic approach can provide a solid semantic foundation which can support various requirements on multi-version ontology reasoning. We have implemented the prototype of MORE (Multi-version Ontology REasoner), which is based on the proposed framework. We have tested MORE with several realistic ontologies. In this paper, we also discuss the implementation issues and report the experiments with MORE.
32544015
Belief revision for adaptive negotiation agents
Existing negotiation agents are primitive in terms of what they can learn and how responsive they are towards the changing negotiation contexts. These weaknesses can be alleviated if an expressive representation language is used to represent negotiation contexts and a sound inference mechanism is applied to reason about the preferential changes arising in these negotiation contexts. This paper illustrates a novel adaptive negotiation agent model, which is underpinned by the well-known AGM belief revision logic. Our preliminary experiments show that the performance of the belief-based adaptive negotiation agents is promising.
14014109
Dynamic Goal Hierarchies
In this paper we introduce and formalise dynamic goal hierarchies. We begin with a formal definition of goals, according to which they are rational desires. In particular, we require that an agent's goals are coherent; that is, that the agent believes that each goal is jointly realisable with all of the goals which the agent considers to be more important. Thus an agent's goals form a hierarchy, and new goals are defined with reference to it. We then show how preferential entailment can be used to formalise the rational revision of goals and goal hierarchies.