Drag-and-drop pasting
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: Symmetric stereo matching for occlusion handling
abstract: In this paper, we propose a symmetric stereo model to handle occlusion in dense two-frame stereo. Our occlusion reasoning is directly based on the visibility constraint that is more general than both ordering and uniqueness constraints used in previous work. The visibility constraint requires occlusion in one image and disparity in the other to be consistent. We embed the visibility constraint within an energy minimization framework, resulting in a symmetric stereo model that treats left and right images equally. An iterative optimization algorithm is used to approximate the minimum of the energy using belief propagation. Our stereo model can also incorporate segmentation as a soft constraint. Experimental results on the Middlebury stereo images show that our algorithm is state-of-the-art.

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: 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: 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: Flash matting
abstract: In this paper, we propose a novel approach to extract mattes using a pair of flash/no-flash images. Our approach, which we call flash matting, was inspired by the simple observation that the most noticeable difference between the flash and no-flash images is the foreground object if the background scene is sufficiently distant. We apply a new matting algorithm called joint Bayesian flash matting to robustly recover the matte from flash/no-flash images, even for scenes in which the foreground and the background are similar or the background is complex. Experimental results involving a variety of complex indoors and outdoors scenes show that it is easy to extract high-quality mattes using an off-the-shelf, flash-equipped camera. We also describe extensions to flash matting for handling more general scenes.

title: Probabilistic performance guarantee for real-time tasks with varying computation times
abstract: Describes how the scheduling algorithms and schedulability analysis methods developed for periodic tasks can be extended to provide performance guarantees to semi-periodic tasks. Like periodic tasks, the requests in a semi-periodic task are released regularly. However, their computation times vary widely. We focus on systems where the total maximum utilization of the tasks on each processor is larger than one. Hence, according to the existing schedulability conditions for periodic tasks, we cannot guarantee that the semi-periodic tasks are schedulable, even though their total average utilization is very small. We describe two methods of providing probabilistic schedulability guarantees to the semi-periodic tasks. The first method, called probabilistic time-demand analysis, is a modification of the exact schedulability test for periodic tasks. The second method, called the transform-task method, transforms each task into a periodic task followed by a sporadic task. The transform-task method can provide an absolute guarantee to requests with shorter computation times and a probabilistic guarantee to the longer requests.

title: Building bridges for web query classification
abstract: Web query classification (QC) aims to classify Web users' queries, which are often short and ambiguous, into a set of target categories. QC has many applications including page ranking in Web search, targeted advertisement in response to queries, and personalization. In this paper, we present a novel approach for QC that outperforms the winning solution of the ACM KDDCUP 2005 competition, whose objective is to classify 800,000 real user queries. In our approach, we first build a bridging classifier on an intermediate taxonomy in an offline mode. This classifier is then used in an online mode to map user queries to the target categories via the above intermediate taxonomy. A major innovation is that by leveraging the similarity distribution over the intermediate taxonomy, we do not need to retrain a new classifier for each new set of target categories, and therefore the bridging classifier needs to be trained only once. In addition, we introduce category selection as a new method for narrowing down the scope of the intermediate taxonomy based on which we classify the queries. Category selection can improve both efficiency and effectiveness of the online classification. By combining our algorithm with the winning solution of KDDCUP 2005, we made an improvement by 9.7% and 3.8% in terms of precision and F1 respectively compared with the best results of KDDCUP 2005.

title: Extracting FSMs from Object-Z specifications with history invariants
abstract: Object-Z with history invariants can present precise and abstract models for complex systems. The system behavior patterns are often implicitly embedded within various state/operational constraints and history invariants. Without explicit system behavior representations, it is difficult to implement those abstract models. In this paper, we present a sound and systematic approach to automatically extract explicit system behaviors (as FSMs) from the abstract Object-Z specifications. Safety and liveness and additional crucial requirements for open systems are ensured.

title: A semi-local paradigm for wavelet denoising
abstract: Wavelet denoising methods have been proven useful for many one- and two-dimensional problems. Most existing methods can in principle be carried over to three-dimensional problems, such as the denoising of volumetric positron emission tomography (PET) images, but they may not be sufficiently flexible in allowing some regions of an image to be denoised more aggressively than others. In this paper, we propose a semi-local paradigm for wavelet denoising. The semi-local paradigm involves the division of an image into suitable blocks, which are then individually denoised. To denoise the blocks, we use our modification of the generalized cross validation (GCV) technique of Jansen and Bultheel to choose thresholding parameters; we also present risk estimators to guide some of the other choices involved in the implementation. Experiments with phantom PET images show that the semi-local paradigm provides superior denoising compared to standard application of the GCV technique. An asymptotic analysis demonstrates that, under some regularity conditions, semi-local denoising is asymptotically consistent on the logarithmic scale. The paper concludes with a discussion on the nature of semi-local denoising and some topics for future research.

title: Query enrichment for web-query classification
abstract: Web-search queries are typically short and ambiguous. To classify these queries into certain target categories is a difficult but important problem. In this article, we present a new technique called query enrichment, which takes a short query and maps it to intermediate objects. Based on the collected intermediate objects, the query is then mapped to target categories. To build the necessary mapping functions, we use an ensemble of search engines to produce an enrichment of the queries. Our technique was applied to the ACM Knowledge Discovery and Data Mining competition (ACM KDDCUP) in 2005, where we won the championship on all three evaluation metrics (precision, F1 measure, which combines precision and recall, and creativity, which is judged by the organizers) among a total of 33 teams worldwide. In this article, we show that, despite the difficulty of an abundance of ambiguous queries and lack of training data, our query-enrichment technique can solve the problem satisfactorily through a two-phase classification framework. We present a detailed description of our algorithm and experimental evaluation. Our best result for F1 and precision is 42.4% and 44.4%, respectively, which is 9.6% and 24.3% higher than those from the runner-ups, respectively.