We propose using SQP (Sequential Quadratic Programming) to directly recover 3D quadratic surface parameters from multiple views. A surface equation is used as a constraint. In addition to the sum of squared reprojection errors defined in the traditional bundle adjustment, a Lagrangian term is added to force recovered points to satisfy the constraint. The minimization is realized by SQP. Our algorithm has three advantages. First, given corresponding features in multiple views, the SQP implementation can directly recover the quadratic surface parameters optimally instead of a collection of isolated 3D points coordinates. Second, the specified constraints are strictly satisfied and the camera parameters and 3D coordinates of points can be determined more accurately than that by unconstrained methods. Third, the recovered quadratic surface model can be represented by a much smaller number of parameters instead of point clouds and triangular patches. Experiments with both synthetic and real images show the power of this approach.

In this paper, an efficient optimization algorithm for solving the inverse problem of a two-dimensional lossless homogeneous dielectric object is investigated. A lossless homogeneous dielectric cylinder of unknown permittivity scatters the incident wave in free space and the scattered fields are recorded. Based on the boundary condition and the incident field, a set of nonlinear surface integral equation is derived. The imaging problem is reformulated into optimization problem and the steady-state genetic algorithm is employed to reconstruct the shape and the dielectric constant of the object. Numerical results show that the permittivity of the cylinders can be successfully reconstructed even when the permittivity is fairly large. The effect of random noise on imaging reconstruction is also investigated.

Knudsen et al. proposed an efficient method based on a tree-search algorithm with recursive process for reconstructing the internal state of RC4 stream cipher. However, the method becomes infeasible for word size n > 5 because its time complexity to reconstruct the internal state is too large. This letter proposes a more efficient method than theirs. Our method can reconstruct the internal state by using the pre-known internal-state entries, which are fewer than their method.

Our work introduces a speech enhancement algorithm that modifies on-line the spectral representation of degraded speech to approximate the spectral coefficients of high quality speech. The proposed framework is based on the application of Discrete Fourier Transform (DFT) to a large ensemble of clean speech frames and the estimation of parametric, heavy-tail non-Gaussian probability distributions for the spectral magnitude. Each clean spectral band possesses a unique pdf. This is selected according to the smallest Kullback-Leibler divergence between each candidate heavy-tail pdf and the non-parametric pdf of the magnitude of each spectral band of the clean ensemble. The parameters of the distributions are derived by Maximum Likelihood Estimation (MLE). A maximum a-posteriori (MAP) formulation of the degraded spectral bands leads to soft threshold functions, optimally derived from the statistics of each spectral band and effectively reducing white and slowly varying coloured Gaussian noise. We evaluate the new algorithm on the task of improving the quality of speech perception as well as Automatic Speech Recognition (ASR) and demonstrate its robustness at SNRs as low as 0 dB.

This paper proposes a fast method for the calculation of exponential B-splines sampled at regular intervals. This algorithm is based on a combination of FIR and IIR filters which enables a fast decomposition and reconstruction of a signal. When complex values are selected for the parameters of the exponentials, complex trigonometric functions are obtained. Only the real part of these functions are used for the interpolation of real signals, leading less bandlimited signals when they are compared with the polynomial B-spline counterparts. These characteristics were verified with 1-D and 2-D examples. This paper also discusses the effectiveness of exponential B-splines, when they are applied to image processing.

With increasing Internet traffic congestion, the provision of reliable transmission and packet loss recovery continues to be of substantial importance. In this paper, we analyze a new recovery method using punctured convolutional codes, demonstrating the simplicity and efficiency of the proposed method for the recovery of lost packets. The analysis provides a method for determining the recoverability and the post-reconstruction receiving rate for a given convolutional code. The exact expressions for calculating the recovery rate are derived for a number of convolutional codes and the (2, 1, m) punctured convolutional code. Where packet loss probabilities are in the range typically found in Internet transmissions, the convolutional code-based method delivers superior performance over the traditional parity method with the same redundancy.

A technique that uses a linear prediction error filter (LPEF) and an adaptive digital filter (ADF) to achieve noise reduction in a speech degraded by additive background noise is proposed. It is known that the coefficients of the LPEF converge such that the prediction error signal becomes white. Since a voiced speech can be represented as the stationary periodic signal over a short interval of time, most of voiced speech cannot be included in the prediction error signal of the LPEF. On the other hand, when the input signal of the LPEF is a background noise, the prediction error signal becomes white. Assuming that the background noise is represented as generate by exciting a linear system with a white noise, then we can reconstruct the background noise from the prediction error signal by estimating the transfer function of noise generation system. This estimation is performed by the ADF which is used as system identification. Noise reduction is achieved by subtracting the noise reconstructed by the ADF from the speech degraded by additive background noise.

We consider the problem of reconstructing architectural scenes from multiple photographs taken from arbitrary viewpoints. The original contribution is the use of a map as a source of geometric constraints to obtain in a fast and simple way a detailed model of a scene. We suppose that images are uncalibrated and have at least one planar structure as a faade for exploiting the planar homography induced between world plane and image to calculate a first estimation of the projection matrix. Estimations are improved by using correspondences between images and map. We show how these simple constraints can be used to calibrate the cameras and recover the projection matrices for each viewpoint. Finally, triangulation is used to recover 3D models of the scene and to visualise new viewpoints. Our approach needs minimal a priori information about the camera being used. A working system has been designed and implemented to allow the user to interactively build a model from uncalibrated images from arbitrary viewpoints and a simple map.

In this paper, genetic algorithms is employed to determine the shape of a conducting cylinder buried in a half-space. Assume that a conducting cylinder of unknown shape is buried in one half-space and scatters the field incident from another half-space where the scattered filed is measured. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization problem. The genetic algorithm is then employed to find out the nearly global extreme solution of the object function such that the shape of the conducting scatterer can be suitably reconstructed. In our study, even when the initial guess is far away from the exact one, the genetic algorithm can avoid the local extremes and converge to a reasonably good solution. In such cases, the gradient-based methods often get stuck in local extremes. Numerical results are presented and good reconstruction is obtained both with and without the additive Gaussian noise.

This paper proposes a linear algorithm for metric reconstruction from projective reconstruction. Metric reconstruction problem is equivalent to estimating the projective transformation matrix that converts projective reconstruction to Euclidean reconstruction. We build a quadratic form from dual absolute conic projection equation with respect to the elements of the transformation matrix. The matrix of quadratic form of rank 2 is then eigen-decomposed to produce a linear estimate. The algorithm is applied to three different sets of real data and the results show a feasibility of the algorithm. Additionally, our comparison of results of the linear algorithm to results of bundle adjustment, applied to sets of synthetic image data having Gaussian image noise, shows reasonable error ranges.

This paper proposes a novel fast algorithm for the decomposition and reconstruction of two-dimensional (2-D) signals by box splines. The authors have already proposed an algorithm to calculate the discrete box splines which enables the fast reconstruction of 2-D signals (images) from box spline coefficients. The problem still remains in the decomposition process to derive the box spline coefficients from an input image. This paper first investigates the decomposition algorithm which consists of the truncated geometric series of the inverse filter and the steepest descent method with momentum (SDM). The reconstruction process is also developed to correspond to the enlargement of images. The proposed algorithm is tested for the expansion of several natural images. As a result, the peak signal-to-noise ratio (PSNR) of the reconstructed images became more than 50 dB, which can be considered as enough high level. Moreover, the property of box splines are discussed in comparison with 2-D (the tensor product of) B-splines.

The genetic algorithm is used to reconstruct the shapes of multiple perfectly conducting cylinders. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization problem. The genetic algorithm is then employed to find out the global extreme solution of the object function. Numerical examples are given to demonstrate the capability of the inverse algorithm. Good reconstruction is obtained even when the multiple scattering between two conductors is serious. In addition, the effect of Gaussian noise on the reconstruction results is investigated.

An unwrapping of signal coefficients in transform domain is proposed for applications in which a lossy operation is performed on the coefficients between analysis and synthesis. It is shown that the unwrapping-based modification of signal-to-additive-signal ratio can employ the fact that an implementation of a biorthogonal decomposition is characterized by a mutually orthogonal eigenvectors. An example to illustrate the benefits of the presented approach in lossy image compression applications is shown.

This paper deals with a quantitative inversion algorithm for reconstructing the permittivity and conductivity profiles of bounded inhomogeneous buried objects from measured multifrequency and multiincidence backscattered field data. An Edge-Preserving regularization scheme is applied leading to a significant enhancement in the profiles reconstructions. The applications concern civil engineering and geophysics as well as mine detection and localization. The performance of the reconstructions are illustrated with different synthetic data.

In CDMA mobile system, network connection is constructed with orthogonal spreading codes assigned to each user in order to distinguish one from the other. The number of distinguishable codes and the process speed are different according to the orthogonal spreading factors which, in another literature, can be described as the tree structure. In this paper, we investigate methods to improve the quality of services (QoS) of communication, by changing the spreading factors of orthogonal spreading codes according to the number of users. We propose the effective method to reconstruct the tree structure of orthogonal spreading codes for supporting various data rates transmission in DS-CDMA mobile system. We compare spreading factors with and without the reconstruction and evaluate the effectiveness of the reconstruction method.

In this paper, we propose a projection based design of near perfect reconstruction QMF banks. An advantage of this method is that additional design specifications are easily implemented by defining new convex sets. To apply convex projection technique, the main difficulty is how to approximate the design specifications by some closed convex sets. In this paper, introducing a notion of Magnitude Product Space where a pair of magnitude responses of analysis filters is expressed as a point, we approximate design requirements of QMF banks by multiple closed convex sets in this space. The proposed method iteratively applies a convex projection technique, Hybrid Steepest Descent Method, to find a point corresponding to the optimal analysis filters at each stage, where the closed convex sets are dynamically improved. Design examples show that the proposed design method leads to significant improvement over conventional design methods.

In many fields such as city administration and facilities management, there are an increasing number of requests for a Geographic Information System (GIS) that provides users with automated mapping functions. A mechanism which displays 3D views of an urban scene is particularly required because it would allow the construction of an intuitive and understandable environment for managing objects in the scene. In this paper, we present a new urban modeling system utilizing both image-based and geometry-based approaches. Our method is based on a new concept in which a wide urban area can be displayed with natural photo-realistic images, and each object drawn in the view can be identified by pointing to it. First, to generate natural urban views from any viewpoint, we employ an image-based rendering method, Image Walkthrough, and modify it to handle aerial images. This method can interpolate and generate natural views by assembling several source photographs. Next, to identify each object in the scene, we recover its shape using computer vision techniques (a geometry-based approach). The rough shape of each building is reconstructed from various aerial images, and then its drawn position on the generated view is also determined. This means that it becomes possible to identify each building from an urban view. We have combined both of these approaches yielding a new style of urban information management. The users of the system can enjoy an intuitive understanding of the area and easily identify their target, by generating natural views from any viewpoint and suitably reconstructing the shapes of objects. We have made a prototype system of this new concept of GIS, which have shown the validity of our method.

A new method to recover and display 3D fundus pattern on the inner bottom surface of eye-ball from stereo fundus image pair is developed. For the fundus stereo images, a simple stereo technique does not work, because the fundus is observed through eye lens and a contact wide-angle enlarging lens. In this method, utilizing the fact that fundus forms a part of sphere, we identify their optical parameters and correct the skews of the lines-of-sight. Then, we obtain 3D images of the fundus by back-projecting the stereo images.

In this paper, a method of fusing ground-based laser range image and CCD images for the reconstruction of textured 3D urban object is proposed. An acquisition system is developed to capture laser range image and CCD images simultaneously from the same platform. A registration method is developed using both laser range and CCD images in a coarse-to-fine process. Laser range images are registered with an assumption on sensor's setup, which aims at robustly detecting an initial configuration between the sensor's coordinate system of two views. CCD images are matched to refine the accuracy of the initial transformation, which might be degraded by improper sensor setup, unreliable feature extraction, or limited by low spatial resolution of laser range image. Textured 3D model is generated using planar faces for vertical walls and triangular cells for ground surface, trees and bushes. Through an outdoor experiment of reconstructing a building using six views of laser range and CCD images, it is demonstrated that textured 3D model of urban objects can be generated in an automated manner.

This paper proposes a new method for reconstruction a shape of skin surface replica from shaded image sequence taken with different light source directions. Since the shaded images include shadows caused by surface height fluctuation, and specular and inter reflections, the conventional photometric stereo method is not suitable for reconstructing its surface accurately. In the proposed method, we choose measured intensity which does not include specular and inter reflections and self-shadows so that we can calculate accurate normal vector from the selected measured intensity using SVD (Singular Value Decomposition) method. The experimental results from real images demonstrate that the proposed method is effective for shape reconstruction from shaded images, which include specular and inter reflections and self-shadows.