基于Gabor滤波器的事件流特征增强及事件相机对象识别

基于Gabor滤波器的事件驱动卷积是仿生分层脉冲神经网络中常用的事件相机对象特征提取方法。为提高该类网络事件相机对象特征提取能力,提出基于Gabor滤波器的事件流特征增强算法,并应用于奖励调节STDP规则的脉冲神经网络事件相机对象识别系统。算法首先将事件流按时间窗口划分为事件流片段,然后提取各时间窗口内的事件流片段特征,同时增强事件数量较多的时间窗口内特征。并基于奖励调节STDP规则帮助网络学习诊断性特征。采用增强算法的网络在MNIST-DVS数据集上的分类精度优于未采用增强算法的网络,并且对于较短的事件流输入也有很好的分类能力。该事件流特征增强算法能够提高基于Gabor滤波器的事件驱动卷积对事件相机对象的特征提取能力。

Robust Image Hashing via Random Gabor Filtering and DWT

Image hashing is a useful multimedia technology for many applications,such as image authentication,image retrieval,image copy detection and image forensics.In this paper,we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform(DWT).Specifically,robust and secure image features are first extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map,and then are compressed via a single-level 2-D DWT.Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band.Many experiments with open image datasets are carried out and the results illustrate that our hashing is robust,discriminative and secure.Receiver operating characteristic(ROC)curve comparisons show that our hashing is better than some popular image hashing algorithms in classification performance between robustness and discrimination.

Gabor Order Tracking Filtering Technology in Rotary Machinery

Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method is confined to the expensive hardware equipments.This paper starts from Gabor transform and applies the Gabor time-frequency filtering to vibration signal.The order component's time-frequency coefficients are extracted by mask operation.The order component is reconstructed from the obtained coefficients.The following four key technologies,such as smoothing rotary speed curve,defining filtering band width,constructing the mask operation matrix and reconstructing signal component,are also deeply discussed.Moreover,the technique to smooth the rotary speed curve based on polynomial approximation,the method to determine filtering band width,the arithmetic to constitute mask array and the iterative algorithm to reconstruct signal based on minimum mean square error are specifically analyzed.The 4th order component is successfully gained by using the methods that Gabor time-frequency filter,and the validity and feasibility of this method are approved.This method can solve the problem of order tracking filter technologies which used to depend on hardware and efficiently improve the accuracy of order analysis.

Detection of Residual Yarn in Bobbin Based on Odd Partial Gabor Filter and Multi-Color Space Hierarchical Clustering

In an automatic bobbin management system that simultaneously detects bobbin color and residual yarn,a composite texture segmentation and recognition operation based on an odd partial Gabor filter and multi-color space hierarchical clustering are proposed.Firstly,the parameter-optimized odd partial Gabor filter is used to distinguish bobbin and yarn texture,to explore Garbor parameters for yarn bobbins,and to accurately discriminate frequency characteristics of yarns and texture.Secondly,multi-color clustering segmentation using color spaces such as red,green,blue(RGB)and CIELUV(LUV)solves the problems of over-segmentation and segmentation errors,which are caused by the difficulty of accurately representing the complex and variable color information of yarns in a single-color space and the low contrast between the target and background.Finally,the segmented bobbin is combined with the odd partial Gabor’s edge recognition operator to further distinguish bobbin texture from yarn texture and locate the position and size of the residual yarn.Experimental results show that the method is robust in identifying complex texture,damaged and dyed bobbins,and multi-color yarns.Residual yarn identification can distinguish texture features and residual yarns well and it can be transferred to the detection and differentiation of complex texture,which is significantly better than traditional methods.

多模态遥感图像模板匹配Log-Gabor滤波方法

针对多模态遥感图像匹配难的问题,本文提出了一种基于Log-Gabor滤波的高精度匹配方法。该方法采用由粗到细的多层级密集匹配框架,无须进行特征点检测,避开了多模态图像特征点检测重复率低的问题,能够提取大量高精度匹配点对。本文方法主要分为两步:首先,利用多尺度多角度Log-Gabor滤波器构建对图像间非线性辐射差异稳健的特征金字塔;然后,利用粗尺度的底层特征图进行密集模板匹配,提取大量粗粒度的特征匹配点对,在此基础上再利用特征金字塔,实现粗匹配点自下而上的逐层优化,完成高精度特征匹配点对的提取。同时,针对模板匹配滑窗运算效率不高的问题,提出了一种密集模板匹配的快速实现方式,有效减少了密集模板匹配的运算时间。本文使用多组不同模态的遥感图像进行试验,结果表明,本文方法能够克服图像间非线性辐射差异的影响,在正确匹配数目、匹配准确率与匹配精度上均优于现有多模态图像特征匹配方法。

A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.

Learnable three-dimensional Gabor convolutional network with global affinity attention for hyperspectral image classification

Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image processing.Recently,researchers have found a connection between convolutional neural networks(CNNs)and Gabor filters.Therefore,some Gabor-based CNN methods have been proposed for HSI classification.However,most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning process.Moreover,these methods require patch cubes as network inputs.Such patch cubes may contain interference pixels,which will negatively affect the classification results.To address these problems,in this paper,we propose a learnable three-dimensional(3D)Gabor convolutional network with global affinity attention for HSI classification.More precisely,the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter,which can be learned and updated during the training process.Furthermore,spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube,thus alleviating the interfering pixels problem.Experimental results on three well-known HSI datasets(including two natural crop scenarios and one urban scenario)have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.

Top-hat和Gaborfilter在肺结节ROI自动检测中的应用研究

在自动诊断大量带有病变区域的CT图像时,计算机辅助诊断起着重要的作用。提出了一种自动检测肺结节感兴趣区域的方法。对肺实质进行分割;利用Top-hat滤波提取包含血管和结节在内的初始感兴趣区域;用Gaborfilter对图像进行第二次处理;对图像进行比对,从而得到更为精确的疑似结节的病灶区域。实验证明该方法能准确完整地提取出感兴趣区域。

基于Laws与Gabor滤波的田间西兰花花球识别技术

正确识别西兰花田间位置是实现西兰花自动化采收的基础,西兰花花球颜色与植株的叶片、茎秆相似,仅通过颜色特征无法对西兰花进行识别,本文以成熟期的田间西兰花为研究对象,提出了一种基于纹理特征与颜色特征的西兰花识别算法。首先通过预处理以及Laws滤波对图像进行边界纹理强化,再通过Gabor滤波对图像进行纹理特征向量提取,并对提取后的纹理特征向量进行z⁃score标准化,随后对标准化后的纹理特征向量进行K⁃means聚类与开运算,获取花球潜在存在区域。同时对RGB图像进行HSV转换,通过对图像的H分量进行阈值分割达到滤除地面像素的效果。最终对纹理特征识别与颜色特征识别的结果进行融合,实现对田间西兰花的识别。算法通过结合纹理与颜色特征,对田间西兰花进行了识别,解决了西兰花的花球与茎叶等背景颜色相近难以识别的问题。本文共使用792幅图像进行试验,试验结果表明,本方法可以准确地对西兰花田间图像进行识别,其精确率为96.96%,召回率为94.41%,F1值为95.67%。通过对3组不同拍摄环境的数据集进行算法识别,3组数据集的F1值始终保持在94%以上,具有良好的拍摄环境适应性,为农业机器人进行西兰花自动化采收奠定了基础。

Gabor-C3D手势识别算法

针对现有的手势识别方法注重高层信息,对浅层信息利用不够,导致手势识别准确性和实时性较差的问题,提出一种Gabor-C3D手势识别算法。采用多方向多尺度的Gabor滤波器组提取手势图像中空域和频域的纹理特征;针对手势动作的特点,设计轻量级C3D网络进行特征学习与分类,在保证精度的同时降低网络的复杂度。在公开数据集与自建数据集上进行实验,实验结果表明,所提算法在自建会议手势数据集与20bn-jester中的手势平均识别率分别达到98.73%与89.26%,分类效果优于传统C3D网络。

三维Gabor和多尺度残差网络的高光谱影像分类

为了减轻高光谱影像分类网络模型对训练样本的依赖性并解决网络层数加深产生的性能退化问题,文章研究了三维Gabor滤波和多尺度残差网络的高光谱影像分类方法。利用三维Gabor滤波器提取出有助于分类的光谱-纹理特征,引入扩张卷积和残差学习构建多尺度残差网络模型进行深层次特征提取,实现不同尺度下局部和全局特征融合和分类。在两幅高光谱影像上对该方法和其他方法进行实验比较。结果表明,该方法获得了最优的分类结果,能够在训练样本有限的情况下提高分类精度。

基于Gabor滤波的软包电池表面缺陷检测

针对当前锂锰软包电池表面在生产过程中容易出现凸点、凹点等缺陷的问题,提出了一种基于光度立体法与频域Gabor滤波相结合的电池表面缺陷检测方法。首先,采用3个光源采集图片,然后进行图像融合,生成电池样本的反照率图片,可以使缺陷更加明显;然后对反照率图片进行快速傅里叶变换,在频域状态下使用Gabor滤波器进行处理,处理的结果再用逆傅里叶变换转换到时域,对于去除拍摄反光引起的图片噪声,采用中值滤波去除,将缺陷部分根据灰度值的大小用一个矩形掩膜进行增强;然后采用全局自适应阈值分割提取缺陷区域;最后利用形态学操作细化缺陷,凹坑准确率在98%,凸点准确率在96.7%,能够达到实际工业应用要求。

Gabor Filter Optimization Design for Iris Texture Analysis

This paper deals with an optimization design method for the Gabor filters based on the analysis of an iris texture model. By means of analyzing the properties of an iris texture image, the energy distribution regularity of the iris texture image measured by the average power spectrum density is exploited, and the theoretical ranges of the efficient valued frequency and orientation parameters can also be deduced. The analysis shows that the energy distribution of the iris texture is generally centralized around lower frequencies in the spatial frequency domain. Accordingly, an iterative algorithm is designed to optimize the Gabor parameter field. The experimental results indicate the validity of the theory and efficiency of the algorithm.

Visual-attention gabor filter based online multi-armored target tracking

The multi-armored target tracking(MATT)plays a crucial role in coordinated tracking and strike.The occlusion and insertion among targets and target scale variation is the key problems in MATT.Most stateof-the-art multi-object tracking(MOT)works adopt the tracking-by-detection strategy,which rely on compute-intensive sliding window or anchoring scheme in detection module and neglect the target scale variation in tracking module.In this work,we proposed a more efficient and effective spatial-temporal attention scheme to track multi-armored target in the ground battlefield.By simulating the structure of the retina,a novel visual-attention Gabor filter branch is proposed to enhance detection.By introducing temporal information,some online learned target-specific Convolutional Neural Networks(CNNs)are adopted to address occlusion.More importantly,we built a MOT dataset for armored targets,called Armored Target Tracking dataset(ATTD),based on which several comparable experiments with state-ofthe-art methods are conducted.Experimental results show that the proposed method achieves outstanding tracking performance and meets the actual application requirements.

联合Gabor滤波器和核池化特征学习的单样本人脸识别与验证

针对深度网络模型的结构复杂问题,受构建Gabor滤波器无需任何学习过程且与训练数据无关,以及径向基(radial basis function, RBF)核池化能够提取非线性二阶特征的启发,提出一种联合Gabor滤波器和RBF核池化的轻量卷积网络方法。首先对人脸图像进行Gabor卷积得到特征图;然后采用双曲正切函数tanh激励特征图以提高特征的表达能力;最后利用多尺度金字塔策略将特征图划分为多个区域,在每个区域上做RBF核池化,所有区域的核池化特征串联得到人脸特征表示。探讨了多个参数对识别性能的影响,对比了协方差池化和核池化的区别和性能。在三个单样本人脸识别和一个视频人脸验证数据集上进行大量实验,结果表明本文方法学习的人脸特征具有优秀的判别能力,对光照、遮挡、年龄等因素具有强鲁棒性。

Scheme for Designing the 1-D Convolution Window of Gabor Filter

A scheme for designing one-dimensional (1-D) convolution window of the circularly symmetric Gabor filter which is directly obtained from frequency domain is proposed. This scheme avoids the problem of choosing the sampling frequency in the spatial domain, or the sampling frequency must be determined when the window data is obtained by means of sampling the Gabor function, the impulse response of the Gabor filter. In this scheme, the discrete Fourier transform of the Gabor function is obtained by discretizing its Fourier transform. The window data can be derived by minimizing the sums of the squares of the complex magnitudes of difference between its discrete Fourier transform and the Gabor function's discrete Fourier transform. Not only the full description of this scheme but also its application to fabric defect detection are given in this paper. Experimental results show that the 1-D convolution windows can be used to significantly reduce computational cost and greatly ensure the quality of the Gabor filters. So this scheme can be used in some real-time processing systems.

嵌入注意力的GaborCNN快速人脸表情识别方法

人脸表情识别是智能人机交互研究的基础问题之一,面部情绪变化与嘴、眼睛、眉毛、鼻子等区域密切相关,这些特征对识别表情非常重要。为此,提出一个由4个Gabor滤波卷积层、注意力模块和两个全链接层组成的注意力Gabor卷积网络,同时使用不平衡损失focalloss对网络进行优化。首先,通过Gabor核与传统卷积滤波器调制的Gabor定向滤波器相较于传统卷积滤波器能更好地捕获感兴趣区域的信息,然后利用通道注意力、空间注意力模块提取区域中更关键的特征。在FERPlus和RAF-DB数据集上的实验表明,该模型结构简单、易于训练、计算成本低,识别精度分别达到88.39%、87.22%。

Direction navigability analysis of geomagnetic field based on Gabor filter

Direction navigability analysis is a supplement to the navigability analysis theory, in which extraction of the direction suitable-matching features(DSMFs) determines the evaluation performance. A method based on the Gabor filter is proposed to estimate the direction navigability of the geomagnetic field. First,the DSMFs are extracted based on the Gabor filter's responses.Second, in the view of pattern recognition, the classification accuracy in fault diagnosis is introduced as the objective function of the hybrid particle swarm optimization(HPSO) algorithm to optimize the Gabor filter's parameters. With its guidance, the DSMFs are extracted. Finally, a direction navigability analysis model is established with the support vector machine(SVM), and the performances of the models under different objective functions are discussed. Simulation results show the parameters of the Gabor filter have a significant influence on the DSMFs, which, in turn, affects the analysis results of direction navigability. Moreover, the risk of misclassification can be effectively reduced by using the analysis model with optimal Gabor filter parameters. The proposed method is not restricted in geomagnetic navigation, and it also can be used in other fields such as terrain matching and gravity navigation.

Automatic Image Inspection of Fabric Defects Based on Optimal Gabor Filter

An effective method for automatic image inspection of fabric defects is presented. The proposed method relies on a tuned 2D-Gabor filter and quantum-behaved particle swarm optimization( QPSO) algorithm. The proposed method consists of two main steps:( 1) training and( 2) image inspection. In the image training process,the parameters of the 2D-Gabor filters can be tuned by QPSO algorithm to match with the texture features of a defect-free template. In the inspection process, each sample image under inspection is convoluted with the selected optimized Gabor filter.Then a simple thresholding scheme is applied to generating a binary segmented result. The performance of the proposed scheme is evaluated by using a standard fabric defects database from Cotton Incorporated. Good experimental results demonstrate the efficiency of proposed method. To further evaluate the performance of the proposed method,a real time test is performed based on an on-line defect detection system. The real time test results further demonstrate the effectiveness, stability and robustness of the proposed method,which is suitable for industrial production.

一种结合Gabor滤波器与GLCM的盲道检测方法

基于颜色的盲道检测模型对城市中存在的与背景对比度低的盲道分割效果不太理想,针对这个问题,从盲道上特有的纹理着手,设计了一种基于盲道纹理特征的盲道检测算法。该算法通过经典的纹理特征提取方法Gabor滤波器组与灰度共生矩阵相结合对摄像头采集到的盲道图像进行处理,实现盲道本身存在的纹理特征的提取,然后经过模糊C均值聚类算法实现图像中盲道区域的分割。在城市中背景与盲道区域颜色相近环境中,与一些经典的基于颜色分割的盲道检测算法进行比较,提出的基于纹理的盲道检测算法能够有效实现对盲道区域的分割,分割结果较为准确,可以在经过改善后将其应用在视觉辅助导盲设备中,帮助盲人方便、快速地寻找更好的行进方向,极大地保证出行安全。