13939828
Mutual information regularized Bayesian framework for multiple image restoration
Bayesian methods have been extensively used in various applications. However, there are two intrinsic issues rarely addressed, namely generalization and validity, in the context of multiple image restoration, we show that traditional Bayesian methods are sensitive to model errors and cannot guarantee valid results satisfying the underlying prior knowledge, e.g. independent noise property. To improve the Bayesian framework's generalization, we propose to explicitly enforce the validity of the result. Independent noise prior is very important but largely under-utilized in previous literature. In this paper, we use mutual information (MI) to explicitly enforce the independence. Efficient approximations based on Taylor expansion are proposed to adapt MI into standard energy forms to regularize the Bayesian methods. The new regularized Bayesian framework effectively utilizes the traditional generative signal/noise models but is much more robust to various model errors, as demonstrated in experiments on some demanding imaging applications.
14613142
Coupled Bayesian Framework for Dual Energy Image Registration
Image registration for X-ray dual energy imaging is challenging due to the overlaid transparent layers (i.e., the bone and soft tissue) and the different appearances between the dual images acquired with X-rays at different energy spectra. Moreover, subpixel accuracy is necessary for good reconstruction of the bone and soft-tissue layers. This paper addresses these problems with a novel coupled Bayesian framework, in which the registration and reconstruction can effectively reinforce each other. With the reconstruction results, we can design accurate matching criteria for aligning the dual images, instead of treating them as multi-modality registration. Furthermore, prior knowledge of the bone and soft tissue can be exploited to detect poor reconstruction due to inaccurate registration; and hence correct registration errors in the coupled framework. A multiscale freeform registration algorithm is implemented to achieve subpixel registration accuracy. Promising results are obtained in the experiments.
12006939
Mode-based multi-hypothesis head tracking using parametric contours
The paper describes a probabilistic mode-based multi-hypothesis tracking (MHT) algorithm. The modes are the local maximums refined from initial samples in a parametric state space. Because the modes are highly representative, this technique allows us to use a small number of hypotheses to effectively model nonlinear probabilistic distributions. To ensure real-time tracking performance, we propose a novel parametric causal contour model and an efficient dynamic programming scheme to refine the initial contours to nearby modes. Furthermore, to overcome the common drawback of conventional MHT techniques, i.e., producing only the maximum likelihood estimates instead of the desired posterior, we introduce the highly effective importance sampling framework into MHT, and develop a novel procedure to estimate the posterior from the importance function. Experiments on a challenging real-world video sequence demonstrate that the proposed tracking technique is both robust in complex environment (e.g., clutter background and partial occlusion) and efficient in computation.
6045052
Basis pursuit for tracking
This paper introduces a novel adaptive texture feature selection algorithm for tracking. Specifically, we provide a statistical wavelet basis paradigm to maximally separate statistical characteristics of the object-in-interest and its background. The algorithm is based upon nonlinearly selecting basis elements out of dual dictionaries in an iterative fashion to continually improve a cost function that is suitable for tracking. We demonstrate that such a selection is effective with several difficult sequences that are affected by lighting changes, occlusion and background motion.
8180421
Better proposal distributions: object tracking using unscented particle filter
Tracking objects involves the modeling of non-linear non-Gaussian systems. On one hand, variants of Kalman filters are limited by their Gaussian assumptions. On the other hand, conventional particle filter, e.g., CONDENSATION, uses transition prior as the proposal distribution. The transition prior does not take into account current observation data, and many particles can therefore be wasted in low likelihood area. To overcome these difficulties, unscented particle filter (UPF) has recently been proposed in the field of filtering theory. In this paper, we introduce the UPF framework into audio and visual tracking. The UPF uses the unscented Kalman filter to generate sophisticated proposal distributions that seamlessly integrate the current observation, thus greatly improving the tracking performance. To evaluate the efficacy of the UPF framework, we apply it in two real-world tracking applications. One is the audio-based speaker localization, and the other is the vision-based human tracking. The experimental results are compared against those of the widely used CONDENSATION approach and have demonstrated superior tracking performance.
14712495
One-class SVM for learning in image retrieval
Relevance feedback schemes using linear/quadratic estimators have been applied in content-based image retrieval to improve retrieval performance significantly. One major difficulty in relevance feedback is to estimate the support of target images in high dimensional feature space with a relatively small number of training samples. We develop a novel scheme based on one-class SVM, which fits a tight hyper-sphere in the nonlinearly transformed feature space to include most of the target images based on positive examples. The use of a kernel provides us an elegant way to deal with nonlinearity in the distribution of the target images, while the regularization term in SVM provides good generalization ability. To validate the efficacy of the proposed approach, we test it on both synthesized data and real-world images. Promising results are achieved in both cases.
206594758
Detecting Collusion Attacks in Security Protocols
Security protocols have been widely used to safeguard secure electronic transactions. We usually assume that principals are credible and shall not maliciously disclose their individual secrets to someone else. Nevertheless, it is impractical to completely ignore the possibility that some principals may collude in private to achieve a fraudulent or illegal purpose. Therefore, it is critical to address the possibility of collusion attacks in order to correctly analyse security protocols. This paper proposes a framework by which to detect collusion attacks in security protocols. The possibility of security threats from insiders is especially taken into account. The case study demonstrates that our methods are useful and promising in discovering and preventing collusion attacks.
6783224
Fingerprint Deformation Models Using Minutiae Locations and Orientations
Nonlinear deformations in fingerprint images, arising from the elasticity of the skin as well as the pressure and movement of the finger during image acquisition, lead to difficulties in establishing a match between multiple impressions acquired from the same finger. One solution to this problem is to estimate and remove the relative deformations prior to the matching stage. In this paper, these relative deformations are represented as an average deformation model based on minutiae locations and orientations using 2-D thin plate splines (TPS). The estimated average deformation is used to pre-distort a template prior to matching it with a query image in a verification task. Experimental results show that the use of minutiae locations and orientations to estimate the deformation leads to a more representative deformation model than using minutiae locations only. An index of deformation based on the bending energy is also proposed to select templates with the least variability in the deformations. The EER goes down by ap 1.1% when we incorporate minutiae orientation information and use the template selection strategy.
20308502
Object signatures for supporting efficient navigation in object-oriented databases
Most methods that have been proposed to improve query processing in object-oriented databases are designed for the aggregation hierarchy. Although the structure of the generalization hierarchy is similar to the aggregation hierarchy, techniques developed for the aggregation hierarchy cannot fully use the characteristics of the generalization hierarchy to improve performance. We devise a new object signature for the generalization hierarchy. The object signature consists of two parts: a reference signature and a structure signature. The former can be used to eliminate objects that do not match the nested predicates specified in the query, and the latter can be used to eliminate objects that do not belong to the target classes. We propose some processing methods using the object signatures for queries that involve generalization hierarchies. In addition, we define cost models of the object signature and then compare the performance of the traditional query processing method with the object signature method. The results show that using the object signatures can effectively reduce the number of disk accesses in a query.
15321284
From Durational Specifications to TLA Designs of Timed Automata
Different temporal logics tend to emphasise different aspects of a hybrid system. In this paper, we study the predicative interpretation of Duration Calculus (DC) and Temporal Logic of Actions (TLA) and the link between them. A notation called generic composition is used to simplify the manipulation of predicates. The modalities of possibility and necessity become generic composition and its inverse of converse respectively. The transformation between different temporal logics is also characterised as such modalities. The formalism provides a framework in which human experience about hybrid system development can be formalised as refinement laws. A high-level durational specification can be decomposed to two durational specifications driven by an automaton. In such a stepwise design process, durational features are reduced while automaton features increase gradually. The application of the technique is demonstrated in the case study of the gas burner problem.
12775848
Analyzing Inconsistency Toward Enhancing Integration of Biological Molecular Databases
The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.