Morphable face reconstruction with multiple images
Efficient 3D face reconstruction is very important for face animation and recognition. The slow speed of the 3D morphable model is due to the texture mapping. To improve the speed, we only use the shape matching to recover the 3D shape and use texture mapping to get the texture. However, only with the shape information, one image is not enough for accurate 3D face reconstruction. So, we propose to use multiple images with the morphable shape model. First, with the feature points given on the multiple images, the 3D coordinates of the feature points are estimate by the pose estimation. Then, frontal and profile 2D morphable shape models are built to estimate the 3D morphable shape model. These two steps works iteratively to improve the result. At last, the texture is extracted from multiple images with the pose estimation from the estimated 3D face. The effectiveness of our method is demonstrated by the experimental results

title: Automatic Person Annotation of Family Photo Album
abstract: Digital photographs are replacing tradition films in our daily life and the quantity is exploding. This stimulates the strong need for efficient management tools, in which the annotation of “who” in each photo is essential. In this paper, we propose an automated method to annotate family photos using evidence from face, body and context information. Face recognition is the first consideration. However, its performance is limited by the uncontrolled condition of family photos. In family album, the same groups of people tend to appear in similar events, in which they tend to wear the same clothes within a short time duration and in nearby places. We could make use of social context information and body information to estimate the probability of the persons' presence and identify other examples of the same recognized persons. In our approach, we first use social context information to cluster photos into events. Within each event, the body information is clustered, and then combined with face recognition results using a graphical model. Finally, the clusters with high face recognition confidence and context probabilities are identified as belonging to specific person. Experiments on a photo album containing over 1500 photos demonstrate that our approach is effective.

title: Face Alignment with Unified Subspace Optimization of Active Statistical Models
abstract: Active statistical models including active shape models and active appearance models are very powerful for face alignment. They are composed of two parts: the subspace model(s) and the search process. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. Another problem with the subspace model(s) is that the two kinds of parameters of subspaces (the number of components and the constraints on the components) are also treated separately. So they are not jointly optimized. To tackle these two problems, an unified subspace optimization method is proposed. This method is composed of two unification aspects: (I) unification of the statistical model and the search process: the subspace models are optimized according to the search procedure; (2) unification of the number of components and the constraints: the two kinds of parameters are modelled in an unified way, such that they can be optimized jointly. Experimental results demonstrate that our method can effectively find the optimal subspace model and significantly improve the performance

title: Visual Synset: Towards a higher-level visual representation
abstract: We present a higher-level visual representation, visual synset, for object categorization. The visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with similar spatial context. Second, the approach achieves better intra-class invariance power, by clustering delta visual phrases into visual synset, based their probabilistic dasiasemanticspsila, i.e. class probability distribution. Hence, the resulting visual synset can partially bridge the visual differences of images of same class. The tests on Caltech-101 and Pascal-VOC 05 dataset demonstrated that the proposed image representation can achieve good accuracies.

title: Exploring knowledge of sub-domain in a multi-resolution bootstrapping framework for concept detection in news video
abstract: In this paper, we present a model based on a multi-resolution, multi-source and multi-modal (M3) bootstrapping framework that exploits knowledge of sub-domains for concept detection in news video. Because the characteristics and distributions of data in different sub-domains are different, we model and analyze the video in each sub-domain separately using a transductive framework. Along with this framework, we propose a "pseudo-Vapnik combined error bound" to tackle the problem of imbalanced distribution of training data in certain segments of sub-domains. For effective fusion of multi-modal features, we utilize multi-resolution inference and constraints to permit evidences from different modal features to support each other. Finally, we employ a bootstrapping technique to leverage unlabeled data to boost the overall system performance. We test our framework by detecting semantic concepts in the TRECVID 2004 dataset. Experimental results demonstrate that our approach is effective.

title: Markovian mixture face recognition with discriminative face alignment
abstract: A typical automatic face recognition system is composed of three parts: face detection, face alignment and face recognition. Conventionally, these three parts are processed in a bottom-up manner: face detection is performed first, then the results are passed to face alignment, and finally to face recognition. The bottom-up approach is one extreme of vision approaches. The other extreme approach is top-down. In this paper, we proposed a Markovian stochastic mixture approach for combining bottom-up and top-down face recognition: face recognition is performed from the results of face alignment in a bottom-up way, and face alignment is performed based on the results of face recognition in a top-down way. By modeling the mixture face recognition as a stochastic process, the recognized person is decided probabilistically according to the probability distribution coming from the stochastic face recognition, and the recognition problem becomes that ldquowho the most probable person is when the stochastic process of face recognition goes on for an infinite long durationrdquo. This problem is solved with the theory of Markov chains by properly modeling the stochastic process of face recognition as a Markov chain. As conventional face alignment is not suitable for this mixture approach, discriminative face alignment is proposed. And we also prove that the Markovian mixture face recognition results only depend on discriminative face alignment, not on conventional face alignment. Our approach can surprisingly outperform the face recognition performance with manual face localization, which is demonstrated by extensive experiments.

title: Support for data-intensive, variable-granularity grid applications via distributed file system virtualization - a case study of light scattering spectroscopy
abstract: A key challenge faced by large-scale, distributed applications in grid environments is efficient, seamless data management. In particular, for applications that can benefit from access to data at variable granularities, data management can pose additional programming burdens to an application developer. This work presents a case for the use of virtualized distributed file systems as a basis for data management for data-intensive, variable-granularity applications. The approach leverages on-demand transfer mechanisms of existing, de-facto network file system clients and servers that support transfers of partial data sets in an application-transparent fashion, and complement them with user-level performance and functionality enhancements such as caching and encrypted communication channels. The paper uses a nascent application from the medical imaging field (light scattering spectroscopy -LSS) as a motivation for the approach, and as a basis for evaluating its performance. Results from performance experiments that consider the 16-processor parallel execution of LSS analysis and database generation programs show that, in the presence of data locality, a virtualized wide-area distributed file system setup and configured by grid middleware can achieve performance levels close (13% overhead or less) to that of a local disk, and superior (up to 680% speedup) to nonvirtualized distributed file systems.

title: On the Use of Fuzzy Modeling in Virtualized Data Center Management
abstract: One of the most important goals of data-center management is to reduce cost through efficient use of resources. Virtualization techniques provide the opportunity of carving individual physical servers into multiple virtual containers that can be run and managed separately. A key challenge that comes with virtualization is the simultaneous on-demand provisioning of shared resources to virtual containers and the management of their capacities to meet service quality targets at the least cost. This paper proposes a two-level resource management system with local controllers at the virtual-container level and a global controller at the resource-pool level. Autonomic resource allocation is realized through the interaction of the local and global controllers. A novelty of the controller designs is their use of fuzzy logic to efficiently and robustly deal with the complexity of the virtualized data center and the uncertainties of the dynamically changing workloads. Experimental results obtained through a prototype implementation demonstrate that, for the scenarios under consideration, the proposed resource management system can significantly reduce resource consumption while still achieving application performance targets.

title: WSN03-4: A Novel Semi-Markov Smooth Mobility Model for Mobile Ad Hoc Networks.
abstract: Existing random mobility models have their limitations such as speed decay and sharp turn which have been demonstrated by the previous studies. More importantly, mobility models need to mimic the movements that abide by the physical law for accurate analysis and simulations of mobile networks. Therefore, in this paper, we propose a novel mobility model, semi-Markov smooth (SMS) model. Each SMS movement includes three consecutive phases: speed up phase, middle smooth phase, and slow down phase. Thus, the entire motion in the SMS model is smooth and consistent with the moving behaviors in real environment. Through steady state analysis, we demonstrate that SMS model has no average speed decay problem and always maintains a uniform spatial node distribution. The analytical results are validated by extensive simulation experiments. In addition, we compare the simulation results on link lifetime and percentage of node degree with random waypoint model, Gauss-Markov model and the proposed SMS model.

title: Supporting application-tailored grid file system sessions with WSRF-based services
abstract: This paper presents novel service-based grid data management middleware that leverages standards defined by WSRF specifications to create and manage dynamic grid file system sessions. A unique aspect of the service is that the sessions it creates can be customized to address application data transfer needs. Application-tailored configurations enable selection of both performance-related features (block-based partial file transfers and/or whole-file transfers, cache parameters and consistency models) and reliability features (file system copy-on-write checkpointing to aid recovery of client-side failures; replication, autonomous failure detection and data access redirection for server-side failures). These enhancements, in addition to cross-domain user identity mapping and encrypted communication, are implemented via user level proxies managed by the service, requiring no changes to existing kernels. Sessions established using the service is mounted as distributed file systems and can be used transparently by unmodified binary applications. The paper analyzes the use of the service to support virtual machine based grid systems and workflow execution, and also reports on the performance and reliability of service managed wide-area file system sessions with experiments based on scientific applications (NanoMOS/Matlab, CHID, GAUSS and SPECseis).

title: Joint Stream-Wise THP Transceiver Design for the Multiuser MIMO Downlink
abstract: This paper addresses the problem of joint transceiver design for Tomlinson-Harashima Precoding (THP) in the multiuser multiple-input-multiple-output (MIMO) downlink under both perfect and imperfect channel state information at the transmitter (CSIT). For the case of perfect CSIT, we differ from the previous work by performing stream-wise (both inter-user and intra-user) interference pre-cancelation at the transmitter. A minimum total mean square error (MT-MSE) criterion is used to formulate our optimization problem. By some convex analysis of the problem, we obtain the necessary conditions for the optimal solution. An iterative algorithm is proposed to handle this problem and its convergence is proved. Then we extend our designed algorithm to the robust version by minimizing the conditional expectation of the T-MSE under imperfect CSIT. Simulation results are given to verify the efficacy of our proposed schemes and to show their superiorities over existing MMSE-based THP schemes.