Exact and interpolatory quadratures for curvature tensor estimation

The computation of the curvature of smooth surfaces has a long history in differential geometry and is essential for many geometric modeling applications such as feature detection. We present a novel approach to calculate the mean curvature from arbitrary normal curvatures. Then, we demonstrate how the same method can be used to obtain new formulae to compute the Gaussian curvature and the curvature tensor. The idea is to compute the curvature integrals by a weighted sum by making use of the periodic structure of the normal curvatures to make the quadratures exact. Finally, we derive an approximation formula for the curvature of discrete data like meshes and show its convergence if quadratically converging normals are available.     

Simultaneous two-view epipolar geometry estimation and motion segmentation by 4D tensor voting

We address the problem of simultaneous two-view epipolar geometry estimation and motion segmentation from nonstatic scenes. Given a set of noisy image pairs containing matches of n objects, we propose an unconventional, efficient, and robust method, 4D tensor voting, for estimating the unknown n epipolar geometries, and segmenting the static and motion matching pairs into n, independent motions. By considering the 4D isotropic and orthogonal joint image space, only two tensor voting passes are needed, and a very high noise to signal ratio (up to five) can be tolerated. Epipolar geometries corresponding to multiple, rigid motions are extracted in succession. Only two uncalibrated frames are needed, and no simplifying assumption (such as affine camera model or homographic model between images) other than the pin-hole camera model is made. Our novel approach consists of propagating a local geometric smoothness constraint in the 4D joint image space, followed by global consistency enforcement for extracting the fundamental matrices corresponding to independent motions. We have performed extensive experiments to compare our method with some representative algorithms to show that better performance on nonstatic scenes are achieved. Results on challenging data sets are presented.     

复杂背景下的自适应前景分割算法

复杂背景下的运动前景分割是计算机视觉领域研究的一个重点研究问题。为了对复杂背景下的运动前景进行有效分割,提出了一种复杂背景下自适应前景分割算法。该算法 的背景模型是由一系列聚类和聚类的权重构成。每个聚类表示背景的一个历史状态,并能够根据背景的变化,自适应创建、更新或删除聚类,使得背景模型能够准确反映出场景的 变化。每个聚类权重是根据聚类的大小和更新时间自动确定的。为了自动确定该方法的重要阈值,还提出一种基于非参数密度估计的阈值估计方法,并在不同的场景下与多个背景 建模方法进行了比较, 实验结果表明,该算法是有效的。     

基于自适应参数估计的鲁棒语音增强算法

基于鲁棒H_∞滤波器理论和共轭梯度自适应参数估计方法提出了一种对复杂噪声有抑制效果的语音增强算法。应用这种方法自适应地从带噪信号中提取语音参数时不必预先知道噪声源的统计特性,只要求噪声信号能量有限。因为它基于H_∞滤波器,所以可保证由外界干扰和附加噪声引起的性能指标恶化达到最小。仿真结果表明：该语音增强算法具有计算速度快、鲁棒性好、语音增强效果明显、易于实现、可抑制复杂背景噪声等特点。     

Time-aware tensor decomposition for sparse tensors

Machine Learning - Given a sparse time-evolving tensor, how can we effectively factorize it to accurately discover latent patterns? Tensor decomposition has been extensively utilized for analyzing...     

Inference of segmented color and texture description by tensor voting

A robust synthesis method is proposed to automatically infer missing color and texture information from a damaged 2D image by (N)D tensor voting (N > 3). The same approach is generalized to range and 3D data in the presence of occlusion, missing data and noise. Our method translates texture information into an adaptive (N)D tensor, followed by a voting process that infers noniteratively the optimal color values in the (N)D texture space. A two-step method is proposed. First, we perform segmentation based on insufficient geometry, color, and texture information in the input, and extrapolate partitioning boundaries by either 2D or 3D tensor voting to generate a complete segmentation for the input. Missing colors are synthesized using (N)D tensor voting in each segment. Different feature scales in the input are automatically adapted by our tensor scale analysis. Results on a variety of difficult inputs demonstrate the effectiveness of our tensor voting approach.     

On improving the efficiency of tensor voting

This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: The stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used in applications where efficiency is an issue since they have a complexity of order O(1). Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.     

Automatically improving image quality using tensor voting

A novel corrupted region detection technique based on tensor voting is proposed to automatically improve the image quality. This method is suitable for restoring degraded images and enhancing binary images. First, the input images are converted into layered images in which each layer contains objects having similar characteristics. By encoding the pixels in the layered images with second-order tensors and performing voting among them, the corrupted regions are automatically detected using the resulting tensors. These corrupted regions are then restored to improve the image quality. The experimental results obtained from automatic image restoration and binary image enhancement applications show that our method can successfully detect and correct the corrupted regions.     

Edge-preserving color image denoising through tensor voting

This paper presents a new method for edge-preserving color image denoising based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information through tensors in order to propagate them in a neighborhood by using a specific voting process. This voting process is specifically designed for edge-preserving color image denoising by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Measurements of removed noise, edge preservation and undesirable introduced artifacts, additionally to visual inspection, show that the proposed method has a better performance than the state-of-the-art image denoising algorithms for images contaminated with CCD camera noise.     

Notes on the curvature tensor

We present a collection of formulas for computing the curvature tensor on parametrized surfaces, on implicit surfaces, and on surfaces obtained by space deformation.     

Robust adaptive beamforming using a novel signal power estimation algorithm

Recently, many robust adaptive beamforming (RAB) methods based on covariance matrix reconstruction have been proposed. Motivated by the idea, in this paper, a novel and efficient signal power estimator is devised to reconstruct the interference-plus-noise covariance (INC) matrix, with the corresponding RAB algorithm proposed. Firstly, the steering vectors of the incoming sources are derived using the Capon spatial spectrum and known array geometry. Secondly, a set of linear equations is established based on the signal subspace projection, from which the powers of the incoming sources are estimated. Based on the presumed angular sector of the signal-of-interest (SOI), the steering vectors and powers of the SOI and interferences are distinguished, and the INC matrix is then reconstructed. Finally, the beamformer is determined by the estimated INC matrix and SOI steering vector. The proposed algorithm is computationally more efficient than other reconstruction-based methods because there are closed-form solutions for the signal powers. Simulation results indicate that our proposed algorithm performs better than the existing methods at high signal-to-noise ratios (SNRs), and achieves nearly optimal performance across a wide range of SNR.     

一种鲁棒性的基于运动估计的自适应时空域视频去噪算法

提出了一种鲁棒的基于运动估计的自适应时空域视频去噪算法。在运动估计前的自适应维纳滤波,提高了运动估计的准确性与匹配率;在运动估计后基于小块的再次判断以及Duncan滤波器的采用,提高了运动估计的鲁棒性。实验数据表明,此算法取得了很好的预期效果。     

N-dimensional tensor voting and application to epipolar geometryestimation

We address the problem of epipolar geometry estimation by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8D space. Given a set of noisy point correspondences in two images of a static scene without correspondences, even in the presence of moving objects, our method extracts good matches and rejects outliers. The methodology is novel and unconventional, since, unlike most other methods optimizing certain scalar, objective functions, our approach does not involve initialization or any iterative search in the parameter space. Therefore, it is free of the problem of local optima or poor convergence. Further, since no search is involved, it is unnecessary to impose simplifying assumption to the scene being analyzed for reducing the search complexity. Subject to the general epipolar constraint only, we detect wrong matches by a computation scheme, 8D tensor voting, which is an instance of the more general N-dimensional tensor voting framework. In essence, the input set of matches is first transformed into a sparse 8D point set. Dense, 8D tensor kernels are then used to vote for the most salient hyperplane that captures all inliers inherent in the input. With this filtered set of matches, the normalized eight-point algorithm can be used to estimate the fundamental matrix accurately. By making use of efficient data structure and locality, our method is both time and space efficient despite the higher dimensionality. We demonstrate the general usefulness of our method using example image pairs for aerial image analysis, with widely different views, and from nonstatic 3D scenes. Each example contains a considerable number of wrong matches     

应用分形维数的自适应张量投票算法

张量投票算法是感知聚类方法中一种比较常用的计算方法,可以应用到图像处理等各个方面,具有较强的鲁棒性,非迭代等特性。张量投票算法中尺度参数的自适应选取对于投票域的建立起着至关重要的作用。通过分形维数来选取尺度参数,建立了尺度参数与分形维数的关系,提出了基于分形维数的自适应张量投票算法,并将该方法应用于图像的线特征提取和边缘修复。与传统的张量投票算法进行比较,该方法在图像线特征提取和边缘修复方面获得了较好的实验结果。     

基于张量投票的摄像机自标定方法研究

针对传统的基于Kruppa方程摄像机自标定算法的欠鲁棒性,首次提出将鲁棒的张量投票算法用于摄像机自标定方法中。利用基于尺度不变的SIFT算法查找并匹配出每对图像的特征点,其中待匹配图像由摄像机对同一场景从三个不同角度位置拍摄,对图像张量投票后按棒张量特征值降序排序,由此筛选得到具有鲁棒性边缘特征的前八对特征点,利用八点算法求解相应的基础矩阵和极点,根据Kruppa方程和三维重建（SFM）算法求得摄像机参数矩阵。实验结果证明,该方法具有较高标定精度,并通过加入高斯噪声的仿真实验证明该算法是一种鲁棒的摄像机自标定方法。     

Robust adaptive fault estimation and fault tolerant control for quadrotor attitude systems

ABSTRACT

This paper addresses the problem of fault estimation and fault tolerant control for quadrotor unmanned aerial vehicle. Firstly, a robust adaptive fault estimation observer (AFEO) is proposed to achieve fault estimation of quadrotor with actuator fault in the presence of external disturbances and parameter uncertainties. Furthermore, based on the estimation of fault, a dynamic output feedback fault tolerant controller (DOFFTC) is designed to stabilise the closed-loop system with faults and uncertainties. Sufficient conditions for the existence of both AFEO and DOFFTC are given in terms of linear matrix inequalities. Finally, simulation results are presented to illustrate the effectiveness of the proposed strategy.     

Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis

We introduce a novel family of adaptive robust channel estimators for highly challenging underwater acoustic (UWA) channels. Since the underwater environment is highly non-stationary and subjected to impulsive noise, we use adaptive filtering techniques based on minimization of a logarithmic cost function, which results in a better trade-off between the convergence rate and the steady state performance of the algorithm. To improve the convergence performance of the conventional first and second order linear estimation methods while mitigating the stability issues related to impulsive noise, we intrinsically combine different norms of the error in the cost function using a logarithmic term. Hence, we achieve a comparable convergence rate to the faster algorithms, while significantly enhancing the stability against impulsive noise in such an adverse communication medium. Furthermore, we provide a thorough analysis for the tracking and steady-state performances of our proposed methods in the presence of impulsive noise. In our analysis, we not only consider the impulsive noise, but also take into account the frequency and phase offsets commonly experienced in real life experiments. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.     

Color image segmentation using tensor voting based color clustering

A novel color image segmentation method using tensor voting based color clustering is proposed. By using tensor voting, the number of dominant colors in a color image can be estimated efficiently. Furthermore, the centroids and structures of the color clusters in the color feature space can be extracted. In this method, the color feature vectors are first encoded by second order, symmetric, non-negative definite tensors. These tensors then communicate with each other by a voting process. The resulting tensors are used to determine the number of clusters, locations of the centroids, and structures of the clusters used for performing color clustering. Our method is based on tensor voting, a non-iterative method, and requires only the voting range as its input parameter. The experimental results show that the proposed method can estimate the dominant colors and generate good segmented images in which those regions having the same color are not split up into small parts and the objects are separated well. Therefore, the proposed method is suitable for many applications, such as dominant colors estimation and multi-color text image segmentation.     

Stereo using monocular cues within the tensor voting framework

We address the fundamental problem of matching in two static images. The remaining challenges are related to occlusion and lack of texture. Our approach addresses these difficulties within a perceptual organization framework, considering both binocular and monocular cues. Initially, matching candidates for all pixels are generated by a combination of matching techniques. The matching candidates are then embedded in disparity space, where perceptual organization takes place in 3D neighborhoods and, thus, does not suffer from problems associated with scanline or image neighborhoods. The assumption is that correct matches produce salient, coherent surfaces, while wrong ones do not. Matching candidates that are consistent with the surfaces are kept and grouped into smooth layers. Thus, we achieve surface segmentation based on geometric and not photometric properties. Surface overextensions, which are due to occlusion, can be corrected by removing matches whose projections are not consistent in color with their neighbors of the same surface in both images. Finally, the projections of the refined surfaces on both images are used to obtain disparity hypotheses for unmatched pixels. The final disparities are selected after a second tensor voting stage, during which information is propagated from more reliable pixels to less reliable ones. We present results on widely used benchmark stereo pairs.