Stereo Matching Using Belief Propagation
In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion. After eliminating the line process and the binary process by introducing two robust functions, we apply the belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the Markov network. Other low-level visual cues (e.g., image segmentation) can also be easily incorporated in our stereo model to obtain better stereo results. Experiments demonstrate that our methods are comparable to the state-of-the-art stereo algorithms for many test cases.

title: Picture Collage
abstract: In this paper, we address a novel problem of automatically creating a picture collage from a group of images. Picture collage is a kind of visual image summary-to arrange all input images on a given canvas, allowing overlay, to maximize visible visual information. We formulate the picture collage creation problem in a conditional random field model, which integrates image salience, canvas constraint, natural preference, and user interaction. Each image is represented by a group of weighted rectangles, which indicate the salient regions. Then picture collage is resolved by minimizing the energy, guided by the constraints. A two-step optimization method is proposed. First, a quick initialization algorithm based on the proposed 1D collage method is presented. Second, a very efficient Markov chain Monte Carlo method is designed for the refined optimization. We also integrate user interaction in the formulation and optimization to obtain an interactive collage reflecting personalized preference. Visual and quantitative experimental evaluations indicate the efficiency of the proposed collage creation technique.

title: Poisson matting
abstract: In this paper, we formulate the problem of natural image matting as one of solving Poisson equations with the matte gradient field. Our approach, which we call Poisson matting, has the following advantages. First, the matte is directly reconstructed from a continuous matte gradient field by solving Poisson equations using boundary information from a user-supplied trimap. Second, by interactively manipulating the matte gradient field using a number of filtering tools, the user can further improve Poisson matting results locally until he or she is satisfied. The modified local result is seamlessly integrated into the final result. Experiments on many complex natural images demonstrate that Poisson matting can generate good matting results that are not possible using existing matting techniques.

title: Drag-and-drop pasting
abstract: In this paper, we present a user-friendly system for seamless image composition, which we call drag-and-drop pasting. We observe that for Poisson image editing [Perez et al. 2003] to work well, the user must carefully draw a boundary on the source image to indicate the region of interest, such that salient structures in source and target images do not conflict with each other along the boundary. To make Poisson image editing more practical and easy to use, we propose a new objective function to compute an optimized boundary condition. A shortest closed-path algorithm is designed to search for the location of the boundary. Moreover, to faithfully preserve the object's fractional boundary, we construct a blended guidance field to incorporate the object's alpha matte. To use our system, the user needs only to simply outline a region of interest in the source image, and then drag and drop it onto the target image. Experimental results demonstrate the effectiveness of our "drag-and-drop pasting" system.

title: Stereo Matching Using Belief Propagation
abstract: In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF's). These three MRF's model a smooth field for depth/disparity, a line process for depth discontinuity and a binary process for occlusion, respectively. After eliminating the line process and the binary process by introducing two robust functions, we obtain the maximum a posteriori (MAP) estimation in the Markov network by applying a Bayesian belief propagation (BP) algorithm. Furthermore, we extend our basic stereo model to incorporate other visual cues (e.g., image segmentation) that are not modeled in the three MRF's, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.

title: Background Cut
abstract: In this paper, we introduce background cut, a high quality and real-time foreground layer extraction algorithm. From a single video sequence with a moving foreground object and stationary background, our algorithm combines background subtraction, color and contrast cues to extract a foreground layer accurately and efficiently. The key idea in background cut is background contrast attenuation, which adaptively attenuates the contrasts in the background while preserving the contrasts across foreground/background boundaries. Our algorithm builds upon a key observation that the contrast (or more precisely, color image gradient) in the background is dissimilar to the contrast across foreground/background boundaries in most cases. Using background cut, the layer extraction errors caused by background clutter can be substantially reduced. Moreover, we present an adaptive mixture model of global and per-pixel background colors to improve the robustness of our system under various background changes. Experimental results of high quality composite video demonstrate the effectiveness of our background cut algorithm.

title: Semantic Web for Extending and Linking Formalisms
abstract: The diversity of various formal specification techniques and the need for their effective combinations requires an extensible and integrated supporting environment. The Web provides infrastructure for such an environment for formal specification and design because it allows sharing of various design models and provides hyper textual links among the models. Recently the Semantic Web Activity proposed the idea of having data on the web defined and linked in a way that it can be used for automation, extension and integration. The success of the Semantic Web may have profound impact on the web environment for formal specifications, especially for extending and integrating different formalisms. This paper demonstrates how RDF and DAML can be used to build a Semantic Web environment for supporting, extending and integrating various formal specification languages. Furthermore, the paper illustrates how RDF query techniques can facilitate specification comprehension.

title: Efficient Immunization Algorithm for Peer-to-Peer Networks
abstract: In this paper, we present a detail study about the immunization of viruses in Peer-to-Peer networks with power-law degree distributions. By comparing two different immunization strategies, we conclude that it is efficient to immunize the highly connected nodes in order to eradicate viruses from the network. Furthermore, we propose an efficient updating algorithm of global virus database according to the degree-based immunization strategy.

title: Visualizing and Simulating Semantic Web Services Ontologies
abstract: The development of Web Services has transformed the World Wide Web into a more application-aware information portal. The various standards ensure that Web Services are interpretable and extensible, opening up possibilities for simple services to be combined to build complex ones. The Semantic Web presents a new mechanism for users and software agents to discover, describe, invoke, compose and monitor Web services. For these purposes the Semantic Web Services (OWL-S) ontologies have been developed to provide vocabularies to describe Web Services in a precise and machine-understandable way. It is necessary to ensure the ontological descriptions of the services capture the intended meaning as erroneous description may cause invocation of wrong services, with wrong parameters, resulting in undesired outcome. In this paper, we propose to apply software engineering method and tools to visualize, simulate and verify OWL-S process models. Namely, Live Sequence Charts (LSCs) is used to model services, capturing the inner workings of services, and its tool support Play-Engine is used to perform automated visualization, simulation and checking.

title: Neighborhood formation and anomaly detection in bipartite graphs
abstract: Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (Neighborhood formation), and 2) finding abnormal nodes (Anomaly detection). And we propose algorithms to compute the neighborhood for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify abnormal nodes, using neighborhood information. We evaluate the quality of neighborhoods based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.

title: Relevance search and anomaly detection in bipartite graphs
abstract: Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (relevance search), and 2) finding nodes connecting irrelevant nodes (anomaly detection). And we propose algorithms to compute the relevance score for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify anomalies, using relevance scores. We evaluate the quality of relevance search based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.