Binocular Visual Navigation and Obstacle Avoidance of Mobile Robots Based on Speeded-Up Robust Features

This article presents a good robust and real-time system scheme of the mobile robot obstacle detection and navigation, which principle of work is based on the feature descriptor SURF. In this scheme, firstly, the image information of the mobile robot path was captured by the binocular camera; then the feature points were extracted and corresponding matched using SURF to the binocular images as the undetected obstacles; finally fixed the position of the objective by the parallax between the matching points combining with the binocular vision calibration model. Theoretical derivation and experimental results show that this scheme is more accurate for the detection and navigation of the interest points. It has fast matching speed and high accuracy and low error. So, it has certain practical effect and popularizing value for the mobile robot real-time obstacle avoidance and navigation.

Robust Image Watermarking Using Local Invariant Features and Independent Component Analysis

This paper proposes a novel robust image watermarking scheme for digital images using local invariant features and Independent Component Analysis （ICA）. Most present watermarking algorithms are unable to resist geometric distortions that desynchronize the location. The method we propose here is robust to geometric attacks. In order to resist geometric distortions, we use a local invariant feature of the image called the scale invariant feature transform, which is invariant to translation and scaling distortions. The watermark is inserted into the circular patches generated by scale-invariant key point extractor. Rotation invariance is achieved using the translation property of the polar-mapped circular patches. Our method belongs to the blind watermark category, because we use Independent Component Analysis for detection that does not need the original image during detection. Experimental results show that our method is robust against geometric distortion attacks as well as signal-processing attacks.

Deep Facial Emotion Recognition Using Local Features Based on Facial Landmarks for Security System

Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model.In contrast,this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions,especially around the eyes,eyebrows,nose,andmouth.Then,we apply a newclassifier using an ensemble network to increase emotion recognition accuracy.The emotion recognition performance was compared with the conventional algorithms using public databases.The results indicated that the proposed method achieved higher accuracy than the traditional based on facial expressions for emotion recognition.In particular,our experiments with the FER2013 database show that our proposed method is robust to lighting conditions and backgrounds,with an average of 25% higher performance than previous studies.Consequently,the proposed method is expected to recognize facial expressions,especially fear and anger,to help prevent severe accidents by detecting security-related or dangerous actions in advance.

Hard exudates referral system in eye fundus utilizing speeded up robust features

In the paper a referral system to assist the medical experts in the screening/referral of diabetic retinopathy is suggested. The system has been developed by a sequential use of different existing mathematical techniques. These techniques involve speeded up robust features（SURF）, K-means clustering and visual dictionaries（VD）. Three databases are mixed to test the working of the system when the sources are dissimilar. When experiments were performed an area under the curve（AUC） of 0.9343 was attained. The results acquired from the system are promising.

Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

An Adaptive Padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter.However,the related studies are insufficient.By exploring the potential of trackers in these two aspects,a novel adaptive padding correlation filter(APCF)with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework.In the tracker,three feature groups are fused by use of the weighted sum of the normalized response maps,to alleviate the risk of drift caused by the extreme change of single feature.Moreover,to improve the adaptive ability of padding for the filter training of different object shapes,the best padding is selected from the preset pool according to tracking precision over the whole video,where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames.The sequence features include three traditional features and eight newly constructed features.Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.

A robust sparse representation algorithm based on adaptive joint dictionary

Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face recognition.Aiming at the shortcomings of face feature dictionary not‘clean’and noise interference dictionary not‘representative’in sparse representation classification model,a new method named as robust sparse representation is proposed based on adaptive joint dictionary(RSR-AJD).First,a fast lowrank subspace recovery algorithm based on LogDet function(Fast LRSR-LogDet)is proposed for accurate low-rank facial intrinsic dictionary representing the similar structure of human face and low computational complexity.Then,the Iteratively Reweighted Robust Principal Component Analysis(IRRPCA)algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion etc.Finally,the above Fast LRSR-LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary,which includes the low-rank facial intrinsic dictionary,the occlusion dictionary and the remaining intra-class variant dictionary for robust sparse coding.Experiments conducted on four popular databases(AR,Extended Yale B,LFW,and Pubfig)verify the robustness and effectiveness of the authors’method.

多特征融合的无人艇视觉目标长时相关鲁棒跟踪

[目的]针对显著海浪遮挡、相机剧烈晃动引起的无人艇视觉目标跟踪脱靶问题,提出一种基于多特征融合的长时相关鲁棒跟踪算法。[方法]首先,采用多特征融合技术,增强目标特征表达,提高目标模型鲁棒性;其次,利用高维特征降维和响应图子网格插值,提高目标跟踪的效率与精度;然后,设计水面目标重识别机制,解决目标完全脱离视野时的稳定跟踪问题;最后,采用多个代表性视频数据集进行验证和比较分析。[结果]实验结果表明,相较于传统的长时相关跟踪算法,平均成功率提升15.7%,平均距离精度指标提升30.3%,F-Score指标提升7.0%。[结论]所提算法能够处理恶劣海况下的目标脱靶问题,对于提升无人船艇及海洋机器人智能感知能力,具有重要技术支撑意义。

基于问题特征变化引导的动态鲁棒优化算法

随着时间的推移,鲁棒优化是解决动态优化问题的一种新方法,其目标是找到在很长一段时间内仍然可以接受的解决方案。该领域中大多试图根据其未来预测适应度值来寻找新的鲁棒解决方案,然而,预测未来的适应度值的误差往往偏大,对其寻求较好的鲁棒解造成较大的困难。针对这一问题,提出了一个基于问题特征变化引导的算法框架(ROOT-PFCG)来进行动态鲁棒优化。其问题特征变化情况主要参考解在当前环境下的目标函数值和相应相邻环境下的目标函数浮动值,由此提出三个重要指标。在预测和非预测的情况下,基于指标分别提出了三种不同的适应度决策规则来选解,保证其所选解受预测误差影响较小或不受影响,以此寻找更优的鲁棒解,并在此基础上提出了新的性能评价指标。在基准问题上的实验结果表明,所提出的算法能更好地提升鲁棒解的性能,并对不同情况下的指标进一步分析了其对性能的影响,在此基础上分析了更好的指标结合方法。

基于IB-SURF算法的无人机图像拼接技术研究

针对传统SURF算法(speeded up robust features)在拼接高分辨率无人机航拍图像时运行速度慢、特征匹配率低的特点,提出了一种基于IB-SURF(image block-SURF)技术的无人机图像拼接算法。结合无人机定位定姿系统(position and orientation system,POS)求取图像重叠区域;构造掩模在无人机图像重叠区域检测特征点,减少特征提取时间;借助图像分块(image block,IB)的思想对图像划分网格,精简筛选特征点;引入Neighborhood-KNN(neighborhood-K nearest neighbors)进行特征点匹配,提高图像匹配效率。实验结果表明,IB-SURF算法有较快的运行速度和较高的特征匹配率,平均特征匹配率达到84.3%,特征匹配正确率超过95.1%,为图像高质量拼接提供了技术基础。

基于分类配准与全景像素坐标系的显微图像快速拼接算法

目的提出一种基于分类配准与全景像素坐标系的显微图像实时拼接算法,以解决现有图像拼接算法精度低、计算复杂度高等问题。方法依据单视野图像的质量特征,利用评价函数对图像进行分类配准,适应不同的配准模型;构建全景像素坐标系,依据配准结果在此坐标系下进行整体拼接与缝隙融合;基于自主搭建的尿液样本显微扫描系统进行实验,以验证算法的有效性。结果实验结果表明,相比现有的图像拼接方法,本文基于分类配准与全景像素坐标系的显微图像快速拼接算法更有利于拼接缝隙的融合,且更稳定,拼接效果显著优于加速鲁棒特征和快速傅里叶变换单独拼接;在拼接时间方面,基于分类配准与全景像素坐标系的显微图像实时拼接算法的拼接时间大大缩短,拼接速度提升了1倍,且对于矩形扫描区域拼接的大视野图像,无明显拼接缝隙痕迹,可完成高质量的实时拼接。结论基于分类配准与全景像素坐标系的显微图像快速拼接算法能够满足快速高分辨率的大视野图像拼接需求,为显微图像拼接提供了一种更优的方法,便于相关人员进一步分析和应用。

基于MCE/GPD的语音识别及其一种Robust应用中初始参数的选择

首先讨论了基于MCE/GPD的语音识别研究的最新进展。在此基础上 ,提出了一种环境特征判别学习的Robust语音识别方法 ,该方法基于最小分类错误准则利用梯度下降法迭代地学习环境特征。由于梯度下降法产生的是局部最优解 ,因此 ,寻找较好的环境特征初始值就显得非常重要。最后 。

仿射协变的G-质心

图像质心保持仿射协变,但仅可用于消除平移。为方便地提取仿射不变特征,需构造保持仿射协变且异于质心的类质心点。现有算法要么计算量大,要么对二值图像不适用。针对这种情况,提出G-质心,通过改造在极坐标系中图像质心的定义得到,引入极径方向积分的变换函数,使得目前大多的类质心构造算法均是其特例。实验结果表明,通过引入中心投影类的变换函数,所得G-质心可比广义质心、交叉权重质心等算法具有更优的抗噪性能。

一种多特征联合分布的Camshift目标跟踪算法

针对Camshift算法在纹理与颜色相似干扰、目标遮挡等复杂背景中鲁棒性不高、易出现跟踪丢失等问题,提出了一种多特征联合分布的Camshift目标跟踪算法。新的算法选择等价模式LBP纹理,色调与饱和度为多特征,图像从RGB空间的转化为ULBP-H-S空间。选取图像中运动目标作为目标模板,计算目标模板的ULBP-H联合概率分布图与H-S联合概率分布图,通过自适应系数将两个联合概率分布图按位与运算后到目标的联合概率分布图。在每次迭代搜索中,通过自适应搜索窗口算法预测下一帧的搜索窗口位置与大小,在预测的搜索窗口中使用Camshift算法对目标连续跟踪。实验结果表明,改进的算法能在纹理与颜色相似干扰与目标遮挡复杂环境中,对运动目标跟踪有较高的准确性与鲁棒性。

基于SURF-OKG特征匹配的三维重建技术

为了解决结构光三维重建中传统立体匹配存在的特征点匹配错误、匹配缺失和匹配重复等问题,本文将SURF算法中高斯滤波改进为自适应中值滤波结合小波变换,并提出了一种基于OKG算法的二次特征匹配方法。该算法首先使用自适应中值滤波结合小波变换算法对图像进行平滑和降噪处理,再进行初步特征点提取和匹配,然后将构建的尺度空间划分成多个网格,在每个网格内使用FAST算法提取尺度空间特征点,使用ORB算子提取左右图像的特征点,用BRIEF描述子对其进行描述,采用K-D树最近邻搜索法限制特征点选取,通过GMS算法剔除误匹配点。最后,将本文SURF-OKG算法与传统特征匹配算法进行对比分析,并对阶梯块进行三维重建来验证本文算法的有效性。实验结果表明:SURF-OKG算法的正确匹配率为92.47%;对阶梯宽度为40 mm,精度为0.02 mm的阶梯块进行三维重建,实验测得阶梯宽度的误差均值为1.312 mm,最大误差值不超过1.72 mm,基本满足结构光三维重建系统的实验要求。

变转速下L_(1,1,2)范数与张量核范数联合约束的TRPCA滚动轴承故障特征提取方法

滚动轴承作为旋转机械设备的重要部件之一,其工作状态直接影响旋转设备的运行安全,因此其故障特征的有效提取对于保障机械设备正常运行具有重要的意义。实际应用中滚动轴承通常以变化的速度运行,并且单一传感器采集的轴承的非平稳信号往往被严重的背景噪声覆盖,使得故障特征的提取非常困难。为了解决这一问题,提出一种变转速下L_(1,1,2)范数与张量核范数联合约束的张量主成分分析(tensor robust principal component analysis,TRPCA)滚动轴承故障特征提取方法。首先,使用时频表示(time-frequency representation,TFR)作为正向切片构建张量,分别探讨滚动轴承时变故障特征在张量域中的管稀疏性和背景噪声在张量域中的低管秩性。进而使用L_(1,1,2)范数与张量核范数联合约束的TRPCA对故障特征张量进行提取,得到管稀疏的故障特征张量。最后将提取的故障特征张量在通道索引中进行融合,得到能够有效表征故障特征的时频表示。仿真和试验分析验证了该方法在轴承故障特征提取中的有效性。

提取数字图像相对灰度特征的Robust估计及其快速算法

以玻璃饮料瓶清洁度的在线自动检验问题为背景，研究了应用计算机图像处理与 模式识别方法实现这一自动检验过程中的图像特征提取问题．文中提出了根据图像的 相对灰度检测方迹图像的思路，给出了描述相对灰度特征的参数和提取该参数的统计 估值方法．针对实际饮料瓶一致性差．图像复杂的特点，提出一种ｒｏｂｕｓｔ估计方法 来解决特征提取的可靠性问题．同时为了满足在线处理的实时性要求，文中在给出两 个有关数字图像相对灰度信号及其方差函数的定理的基础上，给出了一种实现ｒｏｂｕｓｔ 估计的快速算法，从而解决了特征提取中的可靠性与实时性之间的矛盾．文中方法已 在一套计算机图像处理系统上实现．结果完全满足实际要求．

High speed robust image registration and localization using optimized algorithm and its performances evaluation

Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.

基于CEEMD和RobustICA的机械设备故障特征提取方法研究

为有效提取复杂背景噪声条件下的滚动轴承故障特征,提出一种基于互补集合经验模态分解(CEEMD)和鲁棒性独立成分分析(RobustICA)相结合的方法。该方法先通过CEEMD分解故障信号并得到若干个不同频率的信号分量。然后依据所构建的组合权重指标体系完成有效信号分量的筛选与重构,并引入虚拟噪声通道。最后,通过RobustICA方法完成信号和噪声的分离,并将降噪后的信号进行包络解调。结果表明,所提出的方法不仅对强噪声干扰有很好的降噪效果,而且能够准确地提取出故障特征。