Optimal design of 2-D FIR digital differentiator using $$L_1$$ L 1 -norm based cuckoo-search algorithm

Multidimensional Systems and Signal Processing - In this article, an optimal design of two-dimensional finite impulse response digital differentiators (2-D FIR-DD) with quadrantally odd symmetric...     

一种用于局部低照度水下图像的自适应增强方法

针对局部低照度导致的水下图像细节丢失以及使用现有的水下图像整体增强方法产生的增强过度现象,提出一种基于改进Retinex-Net的水下图像增强方法.通过基于HSV空间颜色阈值的图像二值化获取图像任意位置的低照度区域;利用卷积神经网络对图像的低照度区域学习与分解,并对分解结果进行端对端训练;在增强网络中运用U-Net,构...     

基于模糊逻辑的指纹图像增强滤波

本文提出了一种新的基于模糊逻辑技术的指纹图像增强滤波器，通过模糊边缘判别器在对任一像素点属于边缘的程度进行模糊软判决的同时完成边缘检测，并针对指纹图像的平滑区域和边缘区域分别采用不同的模糊规则进行增强滤波。实验结果表明这种滤波器在图像的平滑区域具有好的滤波性能，对于边缘区域，则能够在保持边缘细节的同时滤除噪声。     

An online approach for multi-sprite generation based on camera parameters estimation

We propose in this work to synthesize online the visual content of a complex video into multiple sprites, while detecting on-the-fly each sprite’s limits. For each received frame, physically meaningful camera rotation angles and focal lengths are firstly estimated from frame-to-sprite homography. These physical parameters are then used by a thresholding-based assignment stage to confirm whether the frame can be added without distortions on one of the sprites that have been built up to that instant. In this case, the selected sprite will be updated online accordingly by applying direct frame-to-sprite warping and advanced median blending of only the newly discovered background content. Else, a new sprite must be defined while selecting the current frame as a reference frame. Extensive experiments accomplished on challenging videos demonstrate that the proposed approach can process online a long video while preventing accumulation of alignment errors.     

一种改进的基于各向异性微分的SAR图像增强方法

合成孔径雷达(SAR)图像经过相干斑抑制后,图像中目标边缘会出现模糊。针对这一问题,提出了一种改进的基于各向异性微分的SAR图像增强方法。该方法在梯度方向和与梯度垂直方向构造了两个扩散系数分布函数。在非目标边缘区域,各方向扩散程度不同,对相干斑噪声具有较好的抑制效果;在目标边缘区域,梯度方向不做平滑,以保护和增强边缘,而在垂直于梯度方向有较大的平滑,突出边缘轮廓。实验结果表明,该方法具有很好的相干斑抑制能力和边缘增强能力。     

基于学习的光栅图像噪声抑制方法

基于条纹投影的三维形貌测量广泛应用于工业制造、质量检测、生物医疗、航空航天等领域。然而在高速测量的场景下,由于光栅图像的采集过程曝光时间短,三维重建结果通常会受到较为严重的图像噪声干扰。近年来,深度学习技术在计算机视觉等领域得到了广泛应用,并且取得了巨大的成功。受此启发,提出了一种基于学习的光栅图像噪声抑制方法。首先构建了一个基于U-net的卷积神经网络。其次在训练过程中,构建的神经网络学习从含有噪声的条纹图像到对应高质量包裹相位之间的映射关系。当经过适当训练,该网络可从含有噪声的条纹图像中准确恢复相位信息。实验结果表明:针对离线的快速运动场景三维测量,该方法仅利用一幅光栅图像可恢复高精度的相位信息,且相位精度优于传统的三步相移方法。该方法可为提升运动高速场景三维测量的精度提供切实可靠的解决方案。     

基于DCT与维纳滤波的单通道语音增强算法

针对复杂噪声背景下的语音增强问题,基于离散余弦变换(DCT)和维纳滤波提出了一种新的单通道语音增强算法。该算法不依赖任何语音信号模型且无需对噪声的统计特性进行先验假定,它利用DCT域中连续时刻语音信号分量间的相关特性结合最小均方误差算法实现纯净语音分量的最优估计,弥补了一般算法仅依赖单帧带噪语音对语音分量估计得不足。多种噪声背景下的仿真结果表明,该算法在主观和客观测试中都具有良好的语音增强效果。     

A new approach to authentication of binary images for multimedia communication with distortion reduction and security enhancement

A new approach to binary image authentication in multimedia communication with distortion reduction and security enhancement is proposed. Special codes are embedded into the blocks of given images and verified to accomplish the authentication purpose. Enhancement of security in detecting tampered images is achieved by randomly generating the codes and embedding them into randomly selected locations in the image blocks. The reduction of image distortion coming from pixel value replacement in code embedding is carried out by allowing multiple locations for embedding the codes. Security analysis and experimental results are also included to show the effectiveness of the proposed approach.     

An approach to feature selection and classification of remotesensing images based on the Bayes rule for minimum cost

Classification of remote-sensing images is usually carried out by using approaches aimed at minimizing the overall error affecting land-cover maps. However, in several remote-sensing problems, it could be useful to perform classification by taking into account the different consequences (and hence the different costs) associated with each kind of error. This allows one to obtain land-cover maps in which the total classification cost involved by errors is minimized, instead of the overall classification error. To this end an approach to feature selection and classification of remote-sensing images based on the Bayes rule for minimum cost (BRMC) is proposed. In particular a feature-selection criterion function is presented that permits one to select the features to be given as input to a classifier by taking into account the different cost associated with each confused pair of land-cover classes. Moreover, a classification technique based on the BRMC and implemented by using a neural network is described. The results of experiments carried out on a multisource data set concerning the Island of Elba (Italy) point out the ability of the proposed minimum cost approach to produce land-cover maps in which the consequences of each kind of error are considered     

An ergodic approach for chaotic signal estimation at low SNR with application to ultra-wide-band communication

This paper proposes a novel method to estimate the parameters of chaotic signals corrupted by noise. By exploiting the ergodic property of chaotic signals, it is shown here that signal parameters can be estimated accurately from the noisy chaotic signal. The proposed estimator is proved to be consistent. The asymptotic variance of the proposed method and the Cramer-Rao lower bound (CRLB) are derived analytically to assess the estimation performance. Computer simulations confirm that the proposed ergodic estimation approach provides good parameter estimates even at low signal-to-noise ratio (SNR), and its performance is superior to conventional estimation techniques. This method is then applied to ultra-wide-band (UWB) communication by proposing a novel ergodic chaotic parameter modulation based UWB (ECPM-UWB) scheme. The ECPM-UWB scheme is analog and noncoherent. The communication performance of ECPM-UWB is found to be superior through theoretical mean-square-error (MSE) analysis and computer simulations. The ECPM-UWB scheme is shown to have good spectral characteristics. In addition, the proposed scheme is shown to be robust against channel estimation error and multipath fading using both theoretical and numerical analyses.     

An original approach to positioning with cellular fingerprints based on decision tree ensembles

In addition to being a fundamental infrastructure for communication, cellular networks are increasingly employed for outdoor positioning through signal fingerprinting. In this respect, the choice of the specific strategy used to obtain a position estimation from fingerprints plays a major role in determining the overall accuracy. In this paper, we propose a novel fingerprint comparison method, to be used in dynamic and large-scale contexts, such as the outdoor one, based on a machine learning approach. We explore two possible machine learning solutions, that make use of decision tree ensembles and support vector machines, respectively, and carefully contrast and evaluate them against a set of well-known, state-of-the-art fingerprint comparison functions from the literature. Tests are carried out with different tracking devices and environmental settings. It turns out that the machine learning approach, especially when implemented using decision tree ensembles, provides consistently better estimations than all the other considered strategies.     

Nonlinear least square approach for range estimation based on attenuation of EM waves in seawater using world ocean data from 1955 to 2012

In this research paper, we estimated absorption loss (L_α) and spreading loss (L_o) based on electromagnetic (EM) waves propagation, considering multiple seawater depth (1101) points from surface to 5500 m distributed uniformly with 5‐m difference. Estimation of parameters aforementioned was done based on world ocean data (WOD13) from the National Center for Environmental Information (NCEI) averaged between 1955 and 2012 along decades for basic parameters of seawater T (C^o) and S (ppt) up to 5500‐m depth vertically and also across (41 088) latitude/longitude points for all oceans that includes Indian, Pacific, Southern, Atlantic, and Arctic horizontally. We also computed another important factor that contributes in overall path loss (L_UW), loss due to polarization of EM fields between transmitter T_x and receiver R_x such as antenna polarization factor (L_?). L_? (dB) along with L_α (dB) and L_o (dB) by substituting into basic propagation model for EM waves in seawater helps us to predict efficiently accurate achievable range (R_est) using nonlinear least square (NLLS) approximation combined with Lambert transformation considering nonlinear exponential P_T (dBm) decay. Moreover, predicted R_est (m) helps us to minimize mean error (mean(e(t))) by adapting to actual range R (m) using NLLS approach. Simulation tool MATLAB has been used for implementation of NLLS approach and performance analysis.     

A project management approach to using simulation for cost estimation on large, complex software development projects

This publication contains reprint articles for which IEEE does not hold copyright. You may purchase this article from the Ask*IEEE Document Delivery Service at http://www.ieee.org/services/askieee/.     

An efficient approach for facial action unit intensity detection using distance metric learning based on cosine similarity

Emotions of human beings are largely represented by facial expressions. Facial expressions, simple as well as complex, are well decoded by facial action units. Any facial expression can be detected and analyzed if facial action units are decoded well. In the presented work, an attempt has been made to detect facial action unit intensity by mapping the features based on their cosine similarity. Distance metric learning based on cosine similarity maps the data by learning a metric that measures orientation rather than magnitude. The motivation behind using cosine similarity is that change in facial expressions can be better represented by changes in orientation as compared to the magnitude. The features are applied to support vector machine for classification of various intensities of action units. Experimental results on the popularly accepted database such as DISFA database and UNBC McMaster shoulder pain database confirm the efficacy of the proposed approach.     

ISAR imaging of target with complex motion based on novel approach for the parameters estimation of multi-component cubic phase signal

Inverse synthetic aperture radar (ISAR) imaging of target with complex motion is very important in the radar signal processing domain. In this case, the received signal can be characterized as multi-component cubic phase signal (CPS), and the high quality instantaneous ISAR images can be obtained by the parameters estimation approach. The match Fourier transform (MFT) has been proposed for the parameters estimation of linear frequency modulated (LFM) signal, and it has been used successfully in the field of ISAR imaging. In this paper, the third-order match Fourier transform (TMFT) is proposed as an extension of the traditional MFT for the parameters estimation of cubic phase signal, and the asymptotic statistical performance is analyzed theoretically with the derivation of asymptotic statistical results for the estimated parameters. Finally, the TMFT algorithm is used as a tool for the improvement of inverse synthetic aperture radar (ISAR) images quality of target with complex motion, and the results of simulated and real data validate the effectiveness of the TMFT algorithm.     

An algorithm for the minimization of mixed l1 andl2 norms with application to Bayesian estimation

The regularizing functional approach is widely used in many estimation problems. In practice, the solution is defined as one minimum point of a suitable functional, the main part of which accounts for the underlying physical model, whereas the regularizing part represents some prior information about the unknowns. In the Bayesian interpretation, one has a maximum a posteriori (MAP) estimator in which the main and regularizing parts are represented, respectively, by likelihood and prior distributions. When either the prior or likelihood is a Laplace distribution and the other is a Gaussian distribution, one is led to consider functionals that include both absolute and square norms. The authors present a characterization of the minimum points of such functionals, together with a descent-type algorithm for numerical computations. The results of Monte-Carlo simulations are also reported