Sparse representation based classification (SRC) has emerged as a new paradigm for solving face recognition problems. Further research found that the main limitation of SRC is the assumption of pixel-accurate alignment between the test image and the training set. A. Wagner used a series of linear programs that iteratively minimize the sparsity of the registration error. In this paper, we propose another face registration method called three-point positioning method. Experiments show that our proposed method achieves better performance.

Since the number of server providing the facilities for users is usually more than one, remote user authentication schemes used for multi-server architectures, rather than single server circumstance, is considered. As far as security is concerned, privacy is the most important requirements, though some other properties are also desirable in practice. Recently, a number of dynamic ID-based user authentication schemes have been proposed. However, most of those schemes have more or less weaknesses and/or security flaws. In the worst case, user privacy cannot be achieved since malicious servers or users can mount some attacks, i.e., server spoofing attack and impersonation attack, to identify the unique identifier of users and masquerade of one entity as some other. In this paper, we analyze two latest research works and demonstrate that they cannot achieve true anonymity and have some other weaknesses. We further propose the improvements to avoid those security problems. Besides user privacy, the key features of our scheme are including no verification table, freely chosen password, mutual authentication, low computation and communication cost, single registration, session key agreement, and being secure against the related attacks.

An anonymous credential system enables individuals to selectively prove their attributes while all other knowledge remains hidden. We considered the applicability of such a system to large scale infrastructure systems and perceived that revocations are still a problem. Then we contrived a scenario to lessen the number of revocations by using more attributes. In this scenario, each individual needs to handle a huge number of attributes, which is not practical with conventional systems. In particular, each individual needs to prove small amounts of attributes among a huge number of attributes and the manager of the system needs to certify a huge number of attributes of individuals periodically. These processes consume extremely large resources. This paper proposes an anonymous credential system in which both a user's proving attributes set, which is included in a huge attribute set, and manager's certifying attributes are very efficient. Conclusion Our proposal enables an anonymous credential system to be deployed as a large scale infrastructure system.

We describe a technique for the registration of three dimensional (3D) knee femur surface points from MR image data sets; it is a technique that can track local cartilage thickness changes over time. In the first coarse registration step, we use the direction vectors of the volume given by the cloud of points of the MR image to correct for different knee joint positions and orientations in the MR scanner. In the second fine registration step, we propose a global search algorithm that simultaneously determines the optimal transformation parameters and point correspondences through searching a six dimensional space of Euclidean motion vectors (translation and rotation). The present algorithm is grounded on a mathematical theory - Lipschitz optimization. Compared with the other three registration approaches (ICP, EM-ICP, and genetic algorithms), the proposed method achieved the highest registration accuracy on both animal and clinical data.

In group location management, when a transportation system (TS) with mobile stations (MSs) changes location area (LA), only a single group location update by the TS is needed, instead of multiple individual location updates by MSs riding on the TS. Therefore, group location management significantly reduces location update signaling of the current individual location management. In this paper, we further improve the conventional group location management, by paging cells containing the route of public TS within an LA only, if an incoming call arrives at an MS riding on the TS, based on the observation that public TS, such as bus, subway, and train, follows a fixed route; its movement is not random. The performance of the proposed scheme is analyzed in terms of total signaling cost based on the modeling of public TS route. Numerical results reveal that the proposed scheme significantly outperforms the conventional scheme from the aspect of total signaling cost, at the expense of small network overhead due to the additional queries needed to acquire public TS route information.

Accurate registration is crucial for medical image analysis. In this letter, we proposed an improved Demons technique (IDT) for medical image registration. The IDT improves registration quality using orthogonal gradient information. The advantage of the proposed IDT is assessed using 14 medical image pairs. Experimental results show that the proposed technique provides about 8% improvement over existing Demons-based techniques in terms of registration accuracy.

Image registration is an important topic in medical image analysis. It is usually used in 2D mosaics to construct the whole image of a biological specimen or in 3D reconstruction to build up the structure of an examined specimen from a series of microscopic images. Nevertheless, owing to a variety of factors, including microscopic optics, mechanisms, sensors, and manipulation, there may be great differences between the acquired image slices even if they are adjacent. The common differences include the chromatic aberration as well as the geometry discrepancy that is caused by cuts, tears, folds, and deformation. They usually make the registration problem a difficult challenge to achieve. In this paper, we propose an efficient registration method, which consists of a feature-based registration approach based on analytic robust point matching (ARPM) and a refinement procedure of the feature-based Levenberg-Marquardt algorithm (FLM), to automatically reconstruct 3D vessels of the rat brains from a series of microscopic images. The registration algorithm could speedily evaluate the spatial correspondence and geometric transformation between two point sets with different sizes. In addition, to achieve subpixel accuracy, an FLM method is used to refine the registered results. Due to the nonlinear characteristic of FLM method, it converges much faster than most other methods. We evaluate the performance of proposed method by comparing it with well-known thin-plate spline robust point matching (TPS-RPM) algorithm. The results indicate that the ARPM algorithm together with the FLM method is not only a robust but efficient method in image registration.

Image sequence registration has attracted increasing attention due to its significance in image processing and computer vision. In this paper, we put forward a new kernel based image registration approach, combining both feature-based and intensity-based methods. The proposed algorithm consists of two steps. The first step utilizes feature points to roughly estimate a motion parameter between successive frames; the second step applies our kernel based idea to align all the frames to the reference frame (typically the first frame). Experimental results using both synthetic and real image sequences demonstrate that our approach can automatically register all the image frames and be robust against illumination change, occlusion and image noise.

This study examines movement-based registration (MBR). In MBR, a mobile station (MS) performs location registration whenever the number of entering cells reaches the specified movement threshold M. MBR is simple and its implementation is quite straightforward. However, it may result in more registrations than other similar schemes. We propose an improved MBR scheme, in which MBR combines with implicit registration (IR), to reduce registration cost. The performance of the proposed scheme is evaluated using a mathematical approach based on the 2-dimensional random walk mobility model in a hexagonal cell configuration. The numerical results for varying circumstances show that the proposed scheme performs better than conventional MBR.

The problem of recovering a high-resolution frame from a sequence of low-resolution frames is considered. In general, video frames cannot be related through global parametric transformation due to the arbitrary individual pixel movement between frame pairs. To overcome this problem, we propose to employ region-matching technique for motion estimation with a modified model for frame alignment. To do that, the reference frame is segmented into arbitrary-shaped regions which are further matched with that of the other frames. Then, the frame alignment is accomplished by optimizing the cost function that consists of L1-norm of the difference between the interpolated low-resolution (LR) frames and the simulated LR frames. The experimental results demonstrate that using region matching in motion estimation step with the modified alignment model works better than other motion models such as affine, block matching, and optical flow motion models.

This paper presents a technique for high-accuracy estimation of image rotation using 1D Phase-Only Correlation (POC). The rotation angle between two images is estimated as follows: (i) compute the amplitude spectra of the given images, (ii) transform the coordinate system of amplitude spectra from Cartesian coordinates to polar coordinates, and (iii) estimate the translational displacement between the polar-mapped amplitude spectra to obtain the rotation angle. While the conventional approach is to employ 2D POC for high-accuracy displacement estimation in (iii), this paper proposes the use of 1D POC with an adaptive line selection scheme. The proposed technique makes possible to improve the accuracy of rotation estimation for low contrast images of artificial objects with regular geometric shapes and to reduce the total computation cost by 50%.

Considering the inaccuracy of image registration, we propose a new regularization restoration algorithm to solve the ill-posed super-resolution (SR) problem. Registration error is used to obtain cross-channel error information caused by inaccurate image registration. The registration error is considered as the noise mean added into the within-channel observation noise which is known as Additive White Gaussian Noise (AWGN). Based on this consideration, two constraints are regulated pixel by pixel within the framework of Miller's regularization. Regularization parameters connect the two constraints to construct a cost function. The regularization parameters are estimated adaptively in each pixel in terms of the registration error and in each observation channel in terms of the AWGN. In the iterative implementation of the proposed algorithm, sub-sampling operation and sampling aliasing in the detector model are dealt with respectively to make the restored HR image approach the original one further. The transpose of the sub-sampling operation is implemented by nearest interpolation. Simulations show that the proposed regularization algorithm can restore HR images with much sharper edges and greater SNR improvement.

Although a lot of works for location management in wireless networks have been reported in the literature, most of the works have been focused on designing per-user-based strategies. This means that they can achieve the performance enhancement only for a certain class of mobile users with a specific range of CMR (call-to-mobility ratio). However, these per-user-based strategies can actually degrade the performance if a user's CMR changes significantly. Therefore, an efficient uniform location management strategy, which can be commonly applied to all mobile users regardless of their CMR, is proposed and analyzed in this paper. The motivation behind the proposed strategy is to exploit the concepts of the two well-known existing strategies: the location caching strategy and the local anchor strategy. That is, the location caching strategy exploits locality in a user's calling pattern, whereas the local anchor strategy exploits locality in a user's mobility pattern. By exploiting these characteristics of both strategies together with the profile management at the HLR (home location register), the proposed strategy can reduce the frequent access to the HLR, and thus effectively results in significant reduction in terms of the total location management cost. The analytical results also demonstrate that the proposed strategy can be uniformly applied to all mobile users, while always maintaining the performance gain over the IS-41 standard regardless of a user's CMR and the network traffic conditions.

In mass disasters such as earthquakes, fire disasters, tsunami, and terrorism, dental records have been used for identifying victims due to their processing time and accuracy. The greater the number of victims, the more time the identification tasks require, since a manual comparison between the dental radiograph records is done by forensic experts. Addressing this problem, this paper presents an efficient dental radiograph recognition system using Phase-Only Correlation (POC) for human identification. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of dental radiograph images makes possible to achieve highly robust image registration and recognition. Experimental evaluation using a set of dental radiographs indicates that the proposed system exhibits efficient recognition performance for low-quality images.

Image Registration can be seen as an optimization problem to find a cost function and then use an optimization method to get its minimum. Normalized mutual information is a widely-used robust method to design a cost function in medical image registration. Its calculation is based on the joint histogram of the fixed and transformed moving images. Usually, only a discrete joint histogram is considered in the calculation of normalized mutual information. The discrete joint histogram does not allow the cost function to be explicitly differentiated, so it can only use non-gradient based optimization methods, such as Powell's method, to seek the minimum. In this paper, a parzen-window based method is proposed to estimate the continuous joint histogram in order to make it possible to derive the close form solution for the derivative of the cost function. With this help, we successfully apply the gradient-based optimization method in registration. We also design a new kernel for the parzen-window based method. Our designed kernel is a second order polynomial kernel with the width of two. Because of good theoretical characteristics, this kernel works better than other kernels, such as a cubic B-spline kernel and a first order B-spline kernel, which are widely used in the parzen-window based estimation. Both rigid and non-rigid registration experiments are done to show improved behavior of our designed kernel. Additionally, the proposed method is successfully applied to a clinical CT-MR non-rigid registration which is able to assist a magnetic resonance (MR) guided microwave thermocoagulation of liver tumors.

This paper describes a modified ICP registration system of facial point data with range-scanning equipment for medical Augmented Reality applications. The reference facial point data are extracted from the pre-stored CT images; the floating facial point data are captured from range-scanning equipment. A modified soft-shape-context ICP including an adaptive dual AK-D tree for searching the closest point and a modified shape-context objective function is used to register the floating data to reference data to provide the geometric relationship for a medical assistant system and pre-operative training. The adaptive dual AK-D tree searches the closest-point pair and discards insignificant control coupling points by an adaptive distance threshold on the distance between the two returned closest neighbor points which are searched by using AK-D tree search algorithm in two different partition orders. In the objective function of ICP, we utilize the modified soft-shape-context information which is one kind of projection information to enhance the robustness of the objective function. Experiment results of using touch and non-touch capture equipment to capture floating point data are performed to show the superiority of the proposed system.

Paging extensions for Mobile Internet Protocol (P-MIP) decreases only the number of registration, but it does not much improve the method of registration, which still gives rise to a lot of lost packets and long handoff latency, and may also waste the data buffering and time during registration. In the active state, P-MIP behaves in the same way as Mobile Internet Protocol (MIP), thus, in this state, the packet loss rate of P-MIP is the same as that of MIP. However, the packet loss rate of P-MIP is lower than that of MIP, when changing from idle state to active state, because P-MIP buffers packets at the registered FA. We propose an improvement method for the registration delay, while the mobile node is entering the active state to decrease the mobile node waiting time for data packets. The proposed method can reduce the requirement of data buffering and also improve the method of registration to decrease lost packets and handoff latency when the mobile node moves across the cell in the same paging area during active state.

A new method for automatically building statistical shape models from a set of training examples and in particular from a class of hands. In this study, we utilise a novel approach to automatically recover the shape of hand outlines from a series of 2D training images. Automated landmark extraction is accomplished through the use of the self-organising model the growing neural gas (GNG) network, which is able to learn and preserve the topological relations of a given set of input patterns without requiring a priori knowledge of the structure of the input space. The GNG is compared to other self-organising networks such as Kohonen and Neural Gas (NG) maps and results are given for the training set of hand outlines, showing that the proposed method preserves accurate models.

We introduce a new hierarchical key management scheme which can be applied for secure Mobile IP registration protocol. Contrary to the previous schemes, AAA protocol used for registration key distribution is separated from the base registration protocol, so that the registration key distribution can be simplified and the delay caused by the AAA protocol can be avoided. Also proposed is the non-repudiation service based on a hash chain, which is useful for secure auditing.

A novel distributed dynamic regional location management scheme called MORR (Mobility Oriented Regional Registration) is proposed for Mobile IP to improve the signaling traffic cost of a mobile node. This improvement is achieved by adjusting each mobile node's optimal regional domains according to its mobility behavior. With Mobile IP, the capricious mobility and variable traffic load of a mobile node has an impact on its average signaling traffic cost. In this paper, the mobility of all mobile nodes is classified into two modes: random mobility mode and regular mobility mode. We develop new analytical models to formulate the movement behavior and mathematically evaluate their characteristics when applied to these two modes. MORR has the adaptability to manipulate various mobility modes of each mobile node in dedicated ways to determine an optimal regional domain of this mobile node. Simulation results show that anywhere from 3 to 15 percent of the signaling cost is saved by MORR in comparison with the previous distributed dynamic location management schemes for various scenarios.