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An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

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IET IMAGE processing 2021年第8期15卷 1722-1732页

作者： Yang, Shan Lei, Xinyue Liu, Zhenfeng Sui, Guorong Univ Shanghai Sci & Technol Sch Opt & Comp Engn 516 JunGong Rd Shanghai 200093 Peoples R China

Rapidly obtaining accurate dense disparity maps has been the focus of stereo matching research. At present, approaches that achieve superior disparity maps require a large amount of computation, which is not suitable for practical applications. To address this issue, this paper proposes an efficient local matching method based on an adaptive exponentially weighted moving average filter and simple linear iterative clustering segmentation algorithm. First, an effective matching cost is introduced to adaptively integrate absolute intensity difference with Census transform, which is robust against texture free and luminance variate. Following this, during the cost aggregation, the exponentially weighted moving average filter and the SLIC segmentation are combined to handle the problems of computing consumption and adaptive expansion of the cost aggregation window. Finally, the dense disparity map is obtained by a winner-takes-all approach and disparity refinement. To demonstrate its efficiency and validity, the method is quantitatively tested and compared to existing approaches on the Middlebury benchmark. The results show that it has a non-occlusion accuracy of 90.66% and an average runtime of 7.01 s on the 2014 Middlebury dataset. Compared with existing competitive methods, the proposed method achieves superior matching results with a lower time cost.

关键词： Optical, image and video signal processing Image recognition filtering methods in signal processing Computer vision and image processing techniques

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Orthogonal waveform separation in multiple-input and multiple-output imaging sonar with fractional Fourier filtering

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IET RADAR SONAR AND NAVIGATION 2021年第5期15卷 471-484页

作者： Yan, Lu Piao, Shengchun Xu, Feng Harbin Engn Univ Acoust Sci & Technol Lab Harbin Peoples R China Chinese Acad Sci Inst Acoust Beijing Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Peoples R China

Multiple-input and multiple-output (MIMO) imaging sonar can achieve high spatial resolution with a minimum number of elements and improve imaging performance. However, the superposed echoes for the multiple transmitted orthogonal waveforms cannot be separated perfectly using conventional matched filtering, which can severely reduce the quality of underwater acoustic images because of cross-correlation noise. Herein, an orthogonal waveform separation method based on the fractional Fourier filtering to suppress the cross-correlation noise between received orthogonal waveforms is proposed for the MIMO imaging sonar. First, the superposed echoes for transmitted linear frequency-coded waveforms are processed using a fractional Fourier transform. Then, time-varying fractional Fourier filters are used to separate echoes corresponding to different transmitted waveforms. Subsequently, images are obtained by joint transmit-receive beamforming for the output of matched filtering with weighting. Finally, the results of numerical simulations and a lake experiment demonstrate that the cross-correlation noise between the received orthogonal waveforms is effectively suppressed. Moreover, the image becomes sharp and its quality is improved with sidelobe levels lower than -40 dB.

关键词： high spatial resolution underwater acoustic communication received orthogonal waveforms joint transmit-receive beamforming Oceanographic and hydrological techniques and equipment Optical, image and video signal processing underwater acoustic images matched filters sonar imaging imaging performance MIMO imaging sonar orthogonal waveform separation method Sonar and acoustic radar multiple-input and multiple-output imaging sonar filtering methods in signal processing Acoustic and other telecommunication systems and equipment time-varying fractional Fourier filters different transmitted waveforms image filtering transmitted linear frequency-coded waveforms cross-correlation noise array signal processing fractional Fourier filtering superposed echoes conventional matched filtering multiple transmitted orthogonal waveforms image resolution MIMO communication time-varying filters

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Vector co-occurrence morphological edge detection for colour image

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IET IMAGE processing 2021年第13期15卷 3063-3070页

作者： Lu, Ying He, Chunming Yu, Yu-Feng Xu, Guoxia Zhu, Hu Deng, Lizhen Northwestern Polytech Univ Unmanned Syst Res Inst Xian Peoples R China Nanjing Univ Posts & Telecommun Bell Honors Sch Nanjing Peoples R China Guangzhou Univ Dept Stat Guangzhou Peoples R China Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Network Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Prov Key Lab Image Proc & Image Commun Nanjing 210003 Peoples R China

Morphological edge detection is a principal component in pattern recognition and machine vision. Traditional edge detection operators only take pixel mutual into consideration. However, the edges are influenced not only by pixel mutual but also by the boundary characteristics. Here, the vector co-occurrence morphological edge detection operator is proposed, which takes the pixel and boundary information both into consideration. The vector co-occurrence algorithm is exploited to resist the influence of the noise points and detect the edges from the colour image rather than the grey image. And, we lead to define a precise definition of the manner of sorting high-dimensional data for the colour image. The experiment results always illustrate the advancement and practicability of our methods against the baseline method. In terms of experiments, the BSDS500 dataset is introduced to compare and analyse with other algorithms. Based on the standard benchmark index evaluation in the BSDS500 dataset, the ODS and AP of various algorithms are compared and analysed.

关键词： Algebra Optical, image and video signal processing Image recognition filtering methods in signal processing Image sensors Image recognition signal processing theory Computer vision and image processing techniques Algebra

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Modulated Gabor filter based deep convolutional network for electrical motor bearing fault classification and diagnosis

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2021年第2期15卷 154-162页

作者： Afrasiabi, Shahabodin Mohammadi, Mohammad Afrasiabi, Mousa Parang, Benyamin Shiraz Univ Sch Elect & Comp Engn Dept Power & Control Engn Shiraz Iran

The high applicability of the electrical motor has led to gain attention in condition monitoring to diagnosis the most common type of fault in this machine, bearing element. The emergence of deep neural networks (DNN) provides the opportunity to design a network for early bearing fault diagnosis with high speed and without any additional feature extraction technique. However, robustness against the noise and some deficiencies in fully capturing features are still challenging issues. To resolve this problem, this paper proposes a one module Gabor filter based convolutional neural networks (CNN), namely Gabor convolutional neural network (GCNN), for bearing fault detection and classification. GCNN is a modulated Gabor filter to enhance the ability in capturing temporal features as well as enhance understanding spatial features with fewer parameters and higher robustness against noises and can be considered as a computational efficient deep structure. The simulation results of the bearing fault detection/classification are studied on two different experimental prototypes, including case Western Reserve University (CWRU) and Paderborn University (Paderborn) datasets. The superiority of this method is shown by comparison with accelerated CNN (ACNN), adaptive CNN, standard CNN, support vector machine (SVM), learning vector quantisation (LVQ), and feedforward neural network (FFNN).

关键词： filtering methods in signal processing a.c. machines d.c. machines Power engineering computing Neural nets

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Online identification of aerodynamics with fast time-varying features using Kalman filter

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IET CONTROL THEORY AND APPLICATIONS 2021年第2期15卷 272-280页

作者： Zhang, Wanxin Zhu, Jihong Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Tsinghua Univ Dept Precis Instrument Beijing Peoples R China

This paper investigates the online identification method of the unsteady aerodynamics in the post-stall manoeuvres. The main unsteady features are hysteresis and fast time varying due to the complex flow structure at large angles of attack. When limited test data are available, the data-driven model cannot be identified accurately, especially in dealing with the fast time-varying features, so online identification is required. A constant acceleration model-based Kalman filter is constructed by taking into account the unsteady item in the aerodynamic model. Unlike the previous work which only uses the information of second-order moments of errors, the proposed method uses the maximum correntropy criterion. Simulation studies according to the wind tunnel test data show that the proposed method can effectively track the fast time-varying features and estimate the aerodynamic coefficients with high accuracy.

关键词： Applied fluid mechanics filtering methods in signal processing Fluid mechanics and aerodynamics (mechanical engineering) Other topics in statistics Other topics in statistics

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Absolute localisation in confined spaces using deep geometric features

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ELECTRONICS LETTERS 2021年第16期57卷 621-623页

作者： Brogaard, Rune Y. Ravn, Ole Boukas, Evangelos Tech Univ Denmark Dept Elect Engn Lyngby Denmark

When operating in dark and confined spaces, the capacity of the robots to localise in an absolute reference frame is of utmost importance. This letter presents an absolute localisation system, using deep 3D landmarks, for known confined spaces. The system estimates the robot's relative localisation using visual inertial odometry. Local deep 3D landmarks are extracted from the robot's view. Similar 3D landmarks are, also, extracted from the prior map which are then registered with the local landmarks to provide absolute localisation via an extended Kalman filter. To the best of knowledge, deep 3D feature registration has not been used before for absolute localisation. The proposed localisation system is tested within a representative application area-i.e. a structured, confined space-and the results indicate greater accuracy and lower processing time when compared to mainstream 3D registration approaches.

关键词： Optical, image and video signal processing filtering methods in signal processing Mobile robots Computer vision and image processing techniques

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Noise radar range doppler imaging via 2D generalized smoothed-l0

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ELECTRONICS LETTERS 2021年第11期57卷 448-450页

作者： Lu, Xingyu Gu, Hong Su, Weimin Nanjing Univ Sci & Technol Sch Elect & Opt Engn 200 Xiaolingwei St Nanjing Peoples R China

This paper presents an algorithm to enhance the range-Doppler imaging performance for noise radar. Traditional matched filtering based method suffers from high range and Doppler sidelobes, which makes the weak targets overwhelmed by the sidelobes of strong targets or clutters in the range-Doppler map. Sparse recovery based methods have been widely used to suppress such sidelobes, but most of them assume a repetitive transmit waveform, which cannot be applied to noise radar applications. In this letter, the range-Doppler imaging problem for noise radar is formulated as a sparse recovery model and solved by a 2D generalized smoothed-l(0) algorithm. The proposed method can deal with the random waveforms varying among different pulses in noise radar. The robustness of the proposed method in strong noise and clutters is validated by simulation results.

关键词： Optical, image and video signal processing filtering methods in signal processing Radar theory Radar equipment, systems and applications

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Aircraft tracking in infrared imagery with adaptive learning and interference suppression

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ELECTRONICS LETTERS 2021年第16期57卷 636-638页

作者： Wu, Sijie Zhang, Kai Li, Shaoyi Yan, Jie Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

Airborne target tracking is a crucial part of infrared imaging guidance. In contrast to visual tracking tasks, the target in infrared imagery shows different visual patterns. Moreover, severe background clutter and frequent occlusion caused by infrared interference make it a challenging task. Recently, discriminative correlation filter (DCF)-based trackers have shown impressive performance. However, the features adopted in DCF-based trackers are either handcrafted or pre-trained from a different task, which do not closely intertwine with the domain-specific video. To settle this problem, it is proposed to make full use of online training to learn domain-specific features. By integrating the correlation filter layer into the convolutional neural networks, the feature domain and the response maps of the DCF can be optimized iteratively in the initial frame. Meanwhile, utilizing the measurement of the response maps' peak strength, further adjustments to the feature domain can be made to achieve a sharper peak and suppress the interference region during the tracking process. Evaluations are conducted to prove the validity of proposed aircraft-tracking algorithm.

关键词： Electromagnetic compatibility and interference Optical, image and video signal processing filtering methods in signal processing Radar equipment, systems and applications Optimisation techniques Computer vision and image processing techniques Video signal processing Other topics in statistics Optimisation techniques Other topics in statistics

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Normalized stochastic gradient descent learning of general complex-valued models

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ELECTRONICS LETTERS 2021年第12期57卷 493-495页

作者： Paireder, T. Motz, C. Huemer, M. Johannes Kepler Univ Linz Inst Signal Proc Christian Doppler Lab Digitally Assisted RF Trans A-4040 Linz Austria

The stochastic gradient descent (SGD) method is one of the most prominent first-order iterative optimisation algorithms, enabling linear adaptive filters as well as general nonlinear learning schemes. It is applicable to a wide range of objective functions, while featuring low computational costs for online operation. However, without a suitable step-size normalisation, the convergence and tracking behaviour of the stochastic gradient descent method might be degraded in practical applications. In this letter, a novel general normalisation approach is provided for the learning of (non-)holomorphic models with multiple independent parameter sets. The advantages of the proposed method are demonstrated by means of a specific widely-linear estimation example.

关键词： filtering methods in signal processing Optimisation techniques signal processing theory Interpolation and function approximation (numerical analysis) Linear algebra (numerical analysis) Other topics in statistics Optimisation techniques Interpolation and function approximation (numerical analysis) Linear algebra (numerical analysis) Other topics in statistics

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An efficient multi-sensor fusion and tracking protocol in a vehicle-road collaborative system

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IET COMMUNICATIONS 2021年第18期15卷 2330-2341页

作者： Xu, Zhehan Zhao, Shengjie Zhang, Rongqing Tongji Univ Sch Software Engn Shanghai Peoples R China SoutheastUniv Natl Mobile Commun Res Lab Nanjing Peoples R China

Nowadays, driving safety has become an important topic in the field of intelligent driving. The key step in the intelligent transportation system (ITS) is to detect and track the vehicles on the road through various sensors equipped on the vehicle. While putting the sensors aside the road can have benefits in the vehicle-road collaborative scenario. In this paper, an efficient and applicable multi-sensor fusion protocol for a vehicle-road collaborative system is proposed where roadside units (RSUs) are equipped with cameras and radars. To fully use the history information and the multi-source heterogeneous sensing data to improve the reliability of multi-sensor fusion, a Bayesian-based two-layer fusion scheme is further proposed. Moreover, the efficiency and robustness of the proposed scheme are evaluated in a real highway environment in Changsha, China.

关键词： filtering methods in signal processing Mobile radio systems Radionavigation and direction finding Sensing devices and transducers Aerospace instrumentation and equipment Sensor fusion Computer vision and image processing techniques Traffic engineering computing

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