A novel deghosting method for exposure fusion

A novel ghost-free exposure fusion method for generating an HDR image of a dynamic scene is presented in this paper. Given a sequence of input images with gradually increased exposures, due to the theory that the luminance is linearly depended on the exposure time (Mertens et al. Comput Graph Forum 28(1):161–171, 2009), each input image is normalized to make it have consistent luminance with a reference image. Then moving objects in the dynamic scene are detected using a modified difference method for further exposure fusion. Experiments and comparisons show that our method has advantage in deghosting when the reference image contains saturated regions and generate high-quality results with natural textures. Furthermore, our method has a largely improved timing performance compared with previous reference-guided methods.     

A fast adaptive load balancing method for parallel particle-based simulations

Balancing the work load can improve the performance of distributed simulation systems. In this paper we propose a fast adaptive balancing method, in which a binary tree structure is used to partition the simulation region into sub-domains. From a global view to local views, we balance the loads between sub-domains recursively by compressing and stretching sub-domains in group. This method can adjust the sub-domains with heavy loads and decompose their loads very fast. Then we compare the algorithm with two previously proposed algorithms by an artificial case and a real distributed case respectively. In both cases, our method can get a faster convergence speed and a lower communication overhead.     

一种新的基于2DPCA的遥感图像融合方法

作为一种图像融合的重要技术,PCA已在遥感领域得到了较为广泛的应用.但这种方法也有缺点:首先,在对图像进行PCA分析时需将图像转换为一维向量来实现,从而不能有效利用图像的结构信息;其次,融合后的图像空间分辨率改善明显,但光谱信息损失严重.为解决些问题,我们提出了一种基于2DPCA的遥感图像融合方法.与PCA融合方法相比,该方法的主要特点为:首先,2DPCA是直接对图像矩阵进行,而不是对一维向量,这样就可以有效利用图像的结构信息;其次,融合后的图像不仅空间分辨率大大提高,而且保持了良好的光谱信息.本文的研究和实验证明这种新的图像融合方法是有效的.     

A novel adaptive quantization method for video coding

In this paper, we propose a novel AQ (Adaptive Quantization) algorithm to improve the subjective coding performance. Firstly, the factors affecting a suitable adaptive quantization method are carefully analyzed. Two important conclusions are drawn to guide designing a suitable AQ method. Secondly, based on the drawn analysis, a novel SATD (Sum of the Transformed Difference) based temporal adaptive quantization method is proposed. The proposed method fully considers the temporal characteristics, which can produce more visual-friendly QP (quantization parameter) offset distribution. Experiments are performed on x264 and HM16.0, respectively. With SSIM (Structure Similarity) as the metric, more than 21.07% BD-Rate (Bjontegaard-Delta Rate) saving can be achieved on x264, and on HM16.0, 8.07% and 7.99% BD-Rate savings can be obtained for LDP (Low-Delay-main-P) and LDB (Low-Delay-main-B) configurations, respectively, which is better than the state-of-the-art methods.     

A novel Hough transform method for line detection by enhancing accumulator array

In this paper, an improved Hough transform (HT) method is proposed to robustly detect line segments in images with complicated backgrounds. The work focuses on detecting line segments of distinct lengths, totally independent of prior knowledge of the original image. Based on the characteristics of accumulation distribution obtained by conventional HT, a local operator is implemented to enhance the difference between the accumulation peaks caused by line segments and noise. Through analysis of the effect of the operator, a global threshold is obtained in the histogram of the enhanced accumulator to detect peaks. Experimental results are provided to demonstrate the efficiency and robustness of the proposed method.     

A fast fusion method for multi-videos with three-dimensional GIS scenes

Multimedia Tools and Applications - Techniques for the fusion of real-world videos with virtual scenes are key to the augmentation of three-dimensional (3-D) virtual geographic scenes, which...     

A high-performance fast Fourier transform algorithm for the Cray-2

Most implementations of a radix-2 fast Fourier transform on large scientific computers use algorithms that involve memory accesses whose strides are powers of two. (The term stride means the memory increment between successive elements stored or fetched.) Such strides are unacceptable for recently developed supercomputers, particularly the Cray-2, because of serious difficulties with memory bank conflicts.This article describes an algorithm for evaluating the fast Fourier transform that avoids this difficulty and thus could provide the basis for implementations that more fully utilize the power of the Cray-2. A Fortran program implementing this algorithm is included, and timing comparisons with the Cray assembly-coded library subroutine are shown.The author is with the Numerical Aerodynamic Simulation Systems Division at NASA Ames Research Center.     

A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform

A novel methodology based on multiscale spectral and spatial information fusion using wavelet transform is proposed in order to classify very high resolution (VHR) satellite imagery. Conventional wavelet‐based feature extraction methods employ single windows of a fixed size, which are not satisfactory as the VHR imagery contains complex and multiscale objects. In this paper, spectral and spatial features are extracted based on a set of concentric windows around a central pixel in order to integrate the information across different windows/scales. The proposed method is made up of three blocks: (1) the conventional wavelet‐based feature extraction methods are extended from single band processing to multispectral bands, and from single window to multi‐windows, (2) two multiscale fusion algorithms are proposed to exploit the multiscale spectral and spatial information and (3) a support vector machine (SVM), a relatively new method of machine learning, is used to classify the multiscale spectral–spatial feature sets. The proposed classification method is evaluated on two VHR datasets and the results show that the multiscale approach can improve the classification accuracy in homogeneous areas while simultaneously preserving accuracy in edge regions.     

A novel solution procedure for fuzzy fractional heat equations by homotopy analysis transform method

In this paper, we designed a reliable recipe of homotopy analysis method and Laplace decomposition method namely homotopy analysis transform method to solve fuzzy fractional heat and wave equations. This method overcomes the difficulties arise in other analytical method and removes the restrictive condition of nonlinearity and assumptions of small and large parameters.     

一种基于融合指标的小波去云参数选取方法

遥感图像普遍存在着薄云噪声,对其使用造成影响,因此需要针对薄云的特性来进行去云处理.一种利用频率特点将各层小波系数分开处理的小波去云方法有较好的去云效果,但其中一些参数的正确选取对结果有很大影响,尚未完全找到有效的方法来选择.根据遥感图像的特点,选择评价指标来构造融合指标,利用该融合指标寻找到最佳的分界层和分解层数.通过使用该方法求得的两个参数能够实现较好的去云目的,证明方法的高效性.     

A global adaptive link position-tracking controller for robot manipulators driven by induction motors

We present an adaptive, partial-state feedback, link position tracking controller for robot manipulators driven by induction motors. The proposed controller compensates for parametric uncertainty in the mechanical subsystem while yielding global asymptotic link position tracking. The proposed controller does not require measurement of rotor flux; furthermore, the controller does not exhibit any singularities. Preliminary experimental and simulation results are provided to illustrate the effectiveness of the proposed controller.     

A novel method for adaptive knowledge map construction in the aircraft development

Aircraft is a typical transportation facility and its development need to refer to the existing knowledge. With the rapid increase of knowledge, a knowledge map may deliver excess knowledge to users that they cannot manage at once, thereby causing the problem of knowledge overload. Hence, a novel method for adaptive knowledge map construction was proposed to solve this problem. First, the knowledge was semantically annotated and stored with the domain ontology and a knowledge model that integrates context. Then, user requirement was described by the context of product design, and knowledge nodes that met users’ requirement could be extracted from the knowledge retrieval technology on the basis of context similarity. Finally, the connection between knowledge nodes was constructed with a composite connection model, and the knowledge map was visualized using a hierarchical approach. To verify the effectiveness of the proposed method, the constructed knowledge map was applied in an airplane wing design to assist users in browsing the knowledge base. Results indicate that the proposed method can change the displayed contents according to user requirement and identify the displayed knowledge nodes at a highly acceptable level, the constructed knowledge map can guide users efficiently, and the knowledge overload can be reduced significantly.

     

基于特征差异的彩色目标快速识别方法

本文从人类视觉系统识别彩色目标的特点出发,提出一种基于特征差异的彩色目标快速识别方法,从而有效地提高了机器人视觉系统的实时性,并进行了多物体情况下特定目标的识别与动态目标的识别实验。     

A novel trajectory planning-based adaptive control method for 3-D overhead cranes

A three-dimensional (3-D) overhead crane is a complicated nonlinear underactuated mechanical system, for which high-speed positioning and anti-sway control are the kernel objective. Existing trajectory-based methods for 3-D overhead cranes focus on combining efficient and smooth trajectories with anti-sway tracking controllers without regard for payload motion; moreover, the exact value of plant parameters is required for accurate compensation during the control process. Motivated by these facts, we present a two-step design tracking strategy which consists of a trajectory planning stage and an adaptive tracking control design stage for 3-D overhead cranes. As shown by Lyapunov techniques and Barbalat's Lemma, the proposed controller guarantees asymptotic swing elimination and trolley positioning result in the presence of system uncertainties including unknown parameters and external disturbances. Simulation results also showed the applicability of the proposed method with good robustness against parameter uncertainties and external disturbances.     

A novel analytical solution of a fractional diffusion problem by homotopy analysis transform method

In this study, we present the homotopy analysis transform method for finding solution of fractional diffusion-type equations. We can attain these equations by substituting a first-order time derivative by a fractional-order derivative in regular diffusion equation. We add some examples in order to illustrate the usefulness and efficiency of our novel proposed technique for fractional diffusion equations.     

A fast and adaptive method for complex-valued SAR image denoising based on l_k norm regularization

This paper developed a fast and adaptive method for SAR complex image denoising based on lk norm regularization, as viewed from parameters estimation. We firstly establish the relationship between denoising model and ill-posed inverse problem via convex half-quadratic regularization, and compare the difference between the estimator variance obtained from the iterative formula and biased Cramer-Rao bound, which proves the theoretic flaw of the existent methods of parameter selection. Then, the analytic expre...     

A novel ortho normalized multi-stage discrete fast Stockwell transform based memory-aware high-speed VLSI implementation for image compression

Multimedia Tools and Applications - Image Compression is one of the emerging technique of Digital System for storing, retrieving of digital media applications. The main problem of Image Compression...     

基于移动代理技术的移动IP网络中链路快速建立方法

提出了一种新型移动IP漫游切换过程中链路建立方法,通过在移动主机中集成移动代理,并结合MPLS来完成链路建立时的自动信令和自主建立过程,可以改变目前移动IP网络中树状的网络体系结构,使得切换时建立新链路的信令过程大大简化,显著减小切换时延。给出了链路建立过程和性能分析,通过仿真模拟可以减少时延27%~41%。     

A novel active contour model driven by J-divergence entropy for SAR river image segmentation

It is of great difficulty to utilize the existing active contour models (ACMs) to achieve accurate segmentation of synthetic aperture radar (SAR) river images. To address this problem, a novel ACM driven by J-divergence entropy is proposed. The external energy constraint term of the proposed model is defined by the J-divergence entropy, which differs from those of many existing ACMs defined by the Euclidean distance. Moreover, the median absolute deviations of pixel grayscale values inside and outside the curve are utilized as energy weights, which can adaptively adjust proportions of region energies inside and outside the curve, leading to the improvement in segmentation efficiency. Experiments are performed on a large number of SAR river images, and the results demonstrate that, compared with the existing ACMs, the proposed model shows clear advantages in terms of both segmentation performance and segmentation efficiency.     

A novel Jacob spectral method for multi-dimensional weakly singular nonlinear Volterra integral equations with nonsmooth solutions

The main purpose of this work is to develop a spectrally accurate collocation method for solving weakly singular integral equations of the second kind with nonsmooth solutions in high dimensions. The proposed spectral collocation method is based on a multivariate Jacobi approximation in the frequency space. The essential idea is to adopt a smoothing transformation for the spectral collocation method to circumvent the curse of singularity at the beginning of time. As such, the singularity of the numerical approximation can be tailored to that of the singular solutions. A rigorous convergence analysis is provided and confirmed by numerical tests with nonsmooth solutions in two dimensions. The results in this paper seem to be the first spectral approach with a theoretical justification for high-dimensional nonlinear weakly singular Volterra type equations with nonsmooth solutions.