16998450
Consistent multi-modal non-rigid registration based on a variational approach
In this paper, a novel variational approach for multi-modal image registration based on consistent non-rigid transforms is proposed. The forward and backward transforms are computed in a variational framework simultaneously. A consistency energy is added into the variational registration framework and an iterative method assures that the forward and backward transforms are close approximate inverses of each other. This new scheme can provide more constraints on the forward and backward transforms and make them smoother. This reduces the chance of being stuck into a local minimum. We can incorporate the consistent transforms together with several kinds of information based similarity metrics such as mutual information and correlation ratio into the registration framework. An implicit finite difference scheme is used to solve the Euler-Lagrange equation numerically. Our proposed method is evaluated by using simulated multi-modal magnetic resonance images (MRI) and real medical images. The results of our proposed consistent registration and those using only forward or backward registration are compared. Improvements at the edge area can be easily seen from the registration results.
26210485
Robust Registration of 3-D Ultrasound Images Based on Gabor Filter and Mean-Shift Method
A novel robust method is presented for the registration of 3-D ultrasound images. The proposed method improves the performance of the voxel property-based affine registration in two aspects. First, a set of wavelet-like Gabor filters is used to extract the texture and edge features of the voxels. By using these features, the smoothness of the similarity function in large scale can be improved. Furthermore, adopting edge information can improve the registration accuracy. Second, a robust maximization method based on the mean-shift algorithm and Powell’s direction set method is proposed. The implicitly embedded smoothing process of the mean-shift algorithm can effectively remove the local fluctuation of the similarity function and significantly improve the robustness of optimization. Experimental results demonstrate the robust and accurate performance of the proposed method in the registration of 3-D ultrasound fetal head images.
22330856
Shape Alignment by Learning a Landmark-PDM Coupled Model
This paper revisits the model-based approaches for groupwise shape alignment. The key contribution is modeling the landmarks instead of considering them as nodes sliding along the shape contour. The shape group is thus modeled by a landmark-PDM coupled model instead of a constrained point distribution model (PDM). This coupled model is estimated by a stable four-stage estimation algorithm. There are two significant achievements. First, shapes are aligned in a fully unsupervised manner - both the number and location of landmarks are automatically decided. Second, extremely noisy and largely deformed shapes can be robustly aligned. These are demonstrated using both synthesized and real data
26702978
Relationship between guaranteed rate server and latency rate server
To analyze scheduling algorithms in computer networks, two models have been proposed and widely used in the literature, namely the Guaranteed Rate (GR) server model and the Latency Rate (LR) server model. While a lot scheduling algorithms have been proved to belong to GR or LR or both, it is not clear what is the relationship between them. The purpose of this paper is to investigate this relationship. We prove that if a scheduling algorithm belongs to GR, it also belongs to LR and vice versa. In addition, we review several other models that are also used for analysis of scheduling algorithms and discuss their relationship with LR model and GR model.
41802198
Dynamic Service Matchmaking in Intelligent Web
Intelligent Web functions essentially as an enormously autonomic entity, and it automatically regulates the functions and cooperation of related Web sites and available application services. Agent is the core component of the Intelligent Web. Intelligent Web not only can present the static information, but also can present dynamic services. In this paper, we study the problems of service management in Intelligent Web and analyze the insufficiencies of the service description language CDL, SDL and LARKS. Combining the features of the Intelligent Web, Web Services, and Grid Services, we propose an agent service description language SDLSIN which satisfies ten properties of agent service description. Based on SDLSIN, we mainly study the service matchmaking problem of Intelligent Web, and propose three kinds of service matchmaking algorithms which are adapted to open and dynamic Intelligent Web. At last, the development of SDLSIN and three kinds of service matchmaking algorithms in Multi-AGent Environment MAGE which we developed is introduced.
3077757
Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation
The identification of near-duplicate keyframe (NDK) pairs is a useful task for a variety of applications such as news story threading and content-based video search. In this paper, we propose a novel approach for the discovery and tracking of NDK pairs and threads in the broadcast domain. The detection of NDKs in a large data set is a challenging task due to the fact that when the data set increases linearly, the computational cost increases in a quadratic speed, and so does the number of false alarms. This paper explores the symmetric and transitive nature of near-duplicate for the effective detection and fast tracking of NDK pairs based upon the matching of local keypoints in frames. In the detection phase, we propose a robust measure, namely pattern entropy (PE), to measure the coherency of symmetric keypoint matching across the space of two keyframes. This measure is shown to be effective in discovering the NDK identity of a frame. In the tracking phase, the NDK pairs and threads are rapidly propagated and linked with sitivity without the need of detection. This step ends up a significant boost in speed efficiency. We evaluate proposed approach against a month of the 2004 broadcast videos. The experimental results indicate our approach outperforms other techniques in terms of recall and precision with a large margin. In addition, by considering the transitivity and the underlying distribution of NDK pairs along time span, a speed up of 3 to 5 times is achieved when keeping the performance close enough to the optimal one obtained by exhaustive evaluation.
15580001
On the true Cramer-Rao lower bound for the DA joint estimation of carrier phase and timing offsets
This paper concerns the Cramer-Rao lower bound (CRB) for the data-aided (DA) timing and/or phase recovery, i.e., the synchronization parameter acquisition is aided by a training sequence known to the receiver. For the DA parameter estimation, the CRB typically varies with the training sequence. This indicates that different training sequences offer fundamental different performance. We derive a closed-form formula of the CRB for timing and phase recovery with respect to any particular training sequence. The bound illustrates the close relation between the training sequence and the fundamental limit on timing and phase synchronization. It provides additional insights on the training sequence design.