4872605
Progressive transmission and rendering of foveated volume data
With development of biomedical and networking technology, interactive volume rendering with data transmitted via the network becomes an interesting topic. Two technical problems are data transmission and fast volume rendering. In this paper, two schemes using Wavelet foveation are proposed and implemented: the region-based and coarse-to-fine. The first scheme transmits and renders the region of interest (ROI) of volume data on the client site at highest resolution for the first time, and consequently it iteratively expands the region layer by layer towards to the peripheral. While the second one gives a complete image in a low resolution for the first time on the client. The detail coefficients will be transmitted continuously and the rendering result will be progressively refined depending on the distance to the ROI.
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.
15320041
Feature extraction of volume data based on multi-scale representation
In this paper, we present a novel algorithm that can detect and extract salient points as features in 3-D volume datasets. This algorithm extracts not only the locations of feature points, but also finds out the scales at which the features are most significant. It applies the scale-space theory by adaptively processing the input volume in a few discrete scales. The features points, as well as their scales, detected can be used for subsequent processing, such as content-based volume data authentication and content-based volume rendering.We have implemented the algorithm and tested its effectiveness on several volume datasets. Some optimization has also been done on time-consuming intermediate steps to speed up the feature detection process.
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.
7343489
Feature difference analysis in video authentication system
In most video authentication systems, the difference between features of the original and received videos is often used to decide the authenticity. In this paper, by employing mutual information to represent the similarity between the original and received video frames, we theoretically analyze the relationship between the feature difference and the video distortion. The relationship we derived is applied to estimate the maximum allowable feature difference in a video authentication system and to show how the feature difference varies with quantization step. Experimental results demonstrate that the derived relationship is reasonable and helpful for designing a robust video authentication system.
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.
5951691
ALX, an Action Logic for Agents with Bounded Rationality
Abstract We propose a modal action logic that combines ideas from H.A. Simon's bounded rationality , S. Kripke's possible world semantics , G.H. von Wright's preference logic , Pratt's dynamic logic , Stalnaker's minimal change and more recent approaches to update semantics . ALX (the x th action logic) is sound, complete and decidable, making it the first complete logic for two-place preference operators. ALX avoids important drawbacks of other action logics, especially the counterintuitive necessitation rule for goals (every theorem must be a goal) and the equally counterintuitive closure of goals under logical implication.
16942431
A graph model for E-commerce recommender systems
Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.
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
13610294
Seeing is believing: A common sense theory of the adoption of perception-based beliefs
In this paper we present a formal common sense theory of the adoption of perception-based beliefs. We begin with a logical analysis of perception and then consider when perception should lead to belief change. Our theory is intended to apply to perception in humans and to perception in artificial agents at the level of the symbolic interface between a vision system and a belief system. In order to provide a context for our work we relate it to the emerging field of cognitive robotics, give an abstract architecture for an agent which is both embodied and capable of reasoning, and relate this to the concrete architectures of two vision-based surveillance systems.
38243180
Information Acquisition from Multi-agent Resources
Rational agents, information systems and knowledge bases all share the property that they may become more effective by combining information from multiple sources. However, as was clearly indicated by the notorious "Judge puzzle" proposed by W. J. Schoenmakers in 1986, combining information from several sources is a dangerous operation. The resulting database may turn out to be inconsistent, or even worse: there are situations where the result is consistent but supports inferences which contradict the beliefs of all contributing agents. 
 
In this paper we investigate the possibilities and limitations of strategies for coping with this problem. Our first attempt tries to characterize those situations where information can be combined without risking the undesirable situation that some derivable proposition contradicts the beliefs of all agents involved. The resulting notion is called Absolute safety. It turns out however that for that case only trivial solutions exist. Consequently any non-trivial strategy must use information about the epistemic states of the agents involved. 
 
Subsequently we investigate less restrictive notions of safety. The more interesting ones involve not only propositions about the world but also epistemic information about the knowledge of the agents. This information can be formulated conveniently using the logic of belief dependence which has been designed by the first author, and which has been used previously for designing effectively computable belief revision procedures. 
 
The results characterizing the alternative safety notions generalize for this extended logic. We present a notion of restricted almost safety within this framework which describes the safety of combining information under the hypothesis that the contributing agents eventually would have exchanged their information among themselves. For this notion an explicit solution to the Judge puzzle is given.