206590834
Composite Templates for Cloth Modeling and Sketching
Cloth modeling and recognition is an important and challenging problem in both vision and graphics tasks, such as dressed human recognition and tracking, human sketch and portrait. In this paper, we present a context sensitive grammar in an And-Or graph representation which will produce a large set of composite graphical templates to account for the wide variabilities of cloth configurations, such as T-shirts, jackets, etc. In a supervised learning phase, we ask an artist to draw sketches on a set of dressed people, and we decompose the sketches into categories of cloth and body components: collars, shoulders, cuff, hands, pants, shoes etc. Each component has a number of distinct subtemplates (sub-graphs). These sub-templates serve as leafnodes in a big And-Or graph where an And-node represents a decomposition of the graph into sub-configurations with Markov relations for context and constraints (soft or hard), and an Or-node is a switch for choosing one out of a set of alternative And-nodes (sub-configurations) - similar to a node in stochastic context free grammar (SCFG). This representation integrates the SCFG for structural variability and the Markov (graphical) model for context. An algorithm which integrates the bottom-up proposals and the topdown information is proposed to infer the composite cloth template from the image.
17718282
A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction
In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image and its associated reflectance map into a number of surfaces which fit to various 3D surface models and have homogeneous reflectance (material) properties. In comparison to previous work on range image segmentation, the paper makes the following contributions. Firstly, it is aimed at generic natural scenes, indoor and outdoor, which are often much complexer than most of the existing experiments in the "polyhedra world". Natural scenes require the algorithm to automatically deal with multiple types (families) of surface models which compete to explain the data. Secondly, it integrates the range image with the reflectance map. The latter provides material properties and is especially useful for surface of high specularity, such as glass, metal, ceramics. Thirdly, the algorithm is designed by reversible jump and diffusion Markov chain dynamics and thus achieves globally optimal solutions under the Bayesian statistical framework. Thus it realizes the cue integration and multiple model switching. Fourthly, it adopts two techniques to improve the speed of the Markov chain search: One is a coarse-to-fine strategy and the other are data driven techniques such as edge detection and clustering. The data driven methods provide important information for narrowing the search spaces in a probabilistic fashion. We apply the algorithm to two data sets and the experiments demonstrate robust and satisfactory results on both. Based on the segmentation results, we extend the reconstruction of surfaces behind occlusions to fill in the occluded parts.
398989
Parsing Images into Regions, Curves, and Curve Groups
In this paper, we present an algorithm for parsing natural images into middle level vision representations—regions, curves, and curve groups (parallel curves and trees). This algorithm is targeted for an integrated solution to image segmentation and curve grouping through Bayesian inference. The paper makes the following contributions. (1) It adopts a layered (or 2.1D-sketch) representation integrating both region and curve models which compete to explain an input image. The curve layer occludes the region layer and curves observe a partial order occlusion relation. (2) A Markov chain search scheme Metropolized Gibbs Samplers (MGS) is studied. It consists of several pairs of reversible jumps to traverse the complex solution space. An MGS proposes the next state within the jump scope of the current state according to a conditional probability like a Gibbs sampler and then accepts the proposal with a Metropolis-Hastings step. This paper discusses systematic design strategies of devising reversible jumps for a complex inference task. (3) The proposal probability ratios in jumps are factorized into ratios of discriminative probabilities. The latter are computed in a bottom-up process, and they drive the Markov chain dynamics in a data-driven Markov chain Monte Carlo framework. We demonstrate the performance of the algorithm in experiments with a number of natural images.
7443832
An interleaved hop-by-hop authentication scheme for filtering of injected false data in sensor networks
Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects false data into the network with the goal of deceiving the base station or depleting the resources of the relaying nodes. Standard authentication mechanisms cannot prevent this attack if the adversary has compromised one or a small number of sensor nodes. In this paper, we present an interleaved hop-by-hop authentication scheme that guarantees that the base station will detect any injected false data packets when no more than a certain number t nodes are compromised. Further, our scheme provides an upper bound B for the number of hops that a false data packet could be forwarded before it is detected and dropped, given that there are up to t colluding compromised nodes. We show that in the worst case B is O(t/sup 2/). Through performance analysis, we show that our scheme is efficient with respect to the security it provides, and it also allows a tradeoff between security and performance.
15408418
CatRelate: A New Hierarchical Document Category Integration Algorithm by Learning Category Relationships
We address the problem of integrating documents from a source catalog into a master catalog. Current technologies for solving the problem deem it as a flat category integration problem without considering the useful hierarchy information in the catalog, or deal with it hierarchically but without a rigorous model. In contrast, our method is based on correctly identifying relationships among categories, such as Match, Disjoint, SubConcept, SuperConcept, and Overlap, which come from the relations of sets in Set theory. Compared with traditional Match/NotMatch relationship in literature, our approach is more expressive in defining the relationship. The relationships among categories are first learned in a probabilistic way, and then refined by considering the hierarchy context. Our preliminary experiments show that it can help to correctly identify category relationships, and thus increase the accuracy of document integration.
15435799
Structural and temporal analysis of the blogosphere through community factorization
The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. In this paper, we propose a novel technique that captures the structure and temporal dynamics of blog communities. In our framework, a community is a set of blogs that communicate with each other triggered by some events (such as a news article). The community is represented by its structure and temporal dynamics: a community graph indicates how often one blog communicates with another, and a community intensity indicates the activity level of the community that varies over time. Our method, community factorization, extracts such communities from the blogosphere, where the communication among blogs is observed as a set of subgraphs (i.e., threads of discussion). This community extraction is formulated as a factorization problem in the framework of constrained optimization, in which the objective is to best explain the observed interactions in the blogosphere over time. We further provide a scalable algorithm for computing solutions to the constrained optimization problems. Extensive experimental studies on both synthetic and real blog data demonstrate that our technique is able to discover meaningful communities that are not detectable by traditional methods.
37654146
Moore Automata for the Supervisory Control of Robotic Manufacturing Workcells
The potential of flexible-manufacturing workcells (FMCs) to produce a family of parts in many possible orders of operations and choices of different machines is advantageous. Despite intensive research on the theoretical control of discrete-event systems (DESs), however, current techniques can still only be used for the supervisory control of simple cells. In this paper, a novel modeling and control synthesis technique is presented for FMCs that allow part-routing flexibility. Our proposed methodology combines Extended Moore Automata (EMA) and Controlled-Automata theories to synthesize supervisors for such FMCs.
570616
Least privilege and privilege deprivation: towards tolerating mobile sink compromises in wireless sensor networks
Mobile sinks are needed in many sensor network applications for efficient data collection, data querying, localized sensor reprogramming, identifying and revoking compromised sensors, and other network maintenance. Employing mobile sinks however raises a new security challenge: if a mobile sink is given too many privileges, it will become very attractive for attack and compromise. Using a compromised mobile sink, an adversary may easily bring down or even take over the sensor network. Thus, security mechanisms that can tolerate mobile sink compromises are essential. In this paper, based on the principle of least privilege, we first propose several efficient schemes to restrict the privilege of a mobile sink without impeding its capability of carrying out any authorized operations for an assigned task. To further reduce the possible damages caused by a compromised mobile sink, we then propose efficient message forwarding schemes for depriving the privilege assigned to a compromised mobile sink immediately after its compromise has been detected. Through detailed analysis and simulations, we show that our schemes are secure and efficient, and are highly practical for sensor networks consisting of the current generation of sensors.