Image Feature Extraction and Matching of Augmented Solar Images in Space Weather

Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.

CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.

Rough Set Theory Based Approach for Fault Diagnosis Rule Extraction of Distribution System

As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safety.This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task.Aiming at this reduction problem,a heuristic reduction algorithm based on attribution length and frequency is proposed.At the same time,the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction.Meanwhile,a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking.Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise.Moreover,it needs less calculation towards specific discernibility matrix,and thus avoids the corresponding NP hard problem.The whole process is realized by MATLAB programming.A simulation example shows that the method has a fast calculation speed,and the extracted rules can reflect the characteristic of fault with a concise form.The rule database,formed by different reduction of decision table,can diagnose single fault and multi-faults efficiently,and give satisfied results even when the existed information is incomplete.The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.

SEMIAUTOMATIC BUILDING EXTRACTION FROM STEREO IMAGE PAIR BASED ON LINES GROUPING AND LEAST SQUARES MATCHING

The paper presents a general paradigm of semiautomatic building extraction from aerial stereo image pair.In the semiautomatic extraction system,the building model is defined by selected roof type through human-machine interface and input the approximation of area where the extracted building exists.Then under the knowledge of the roof type,low-level and mid-level processing including edge detection,straight line segments extraction and line segments grouping are used to establish the initial geometrical model of the roof-top.However,the initial geometrical model is not so accurate in geometry.To attain accurate results,a general least squares adjustment integrating the linear templates matching model with geometrical constraints in object-space is applied to refine the initial geometrical model.The adjustment model integrating the straight edge pattern and 3D constraints together is a well-studied optimal and anti-noise method.After gaining proper initial values,this adjustment model can flexibly process extraction of kinds of roof types by changing or assembling the geometrical constraints in object-space.

Error Searching System with Keyword Extraction and Keyword Fuzzy Matching

This paper has proposed an error searching method to search the solutions of errors that occurred in the unified commanding platform mix-deployed software (UCPMD). Because those errors belong to different stages or may be happened in different services, applications, IP ports, system software, or different versions of software, and those errors are also can be classified into different types. It is necessary to locate accurate reason that cause an error as well as find out its solution. The proposed error searching system applies Chinese keyword extraction and Chinese fuzzy matching between keywords, which considers the processed keywords as the index to find out the solutions of errors. Besides, the error searching system had made correspondence among errors, reasons, and solutions, and put them to different categories in terms of their characteristics, such that it is easy to manage, search, and use. Among others, we have added specialized thesaurus as the index of keywords, which enriches and completes the searching results. Because of the proposed error searching system evolves keyword extraction and keyword fuzzy matching technologies;it is more accurate to find out user-interested solutions.

Fingerprint Enhancement, Minutiae Extraction and Matching Techniques

Nowadays, it is a new technology era. Fingerprint is necessary identification recognition of citizens. Fingerprint technology has become more popular and connected to human being life and come to replace traditional identification and verifying recognition today. The fingerprint will continue to substitute the ID of citizens as soon as possible in the future. Fingerprint refers to a complex of combination between gap of ridges and valleys on all of the fingertips. Clearer ridges quality is more convenient to analyze who you are and system can recognize your unique identity. Poorer ridges quality image is a significant problem that system has to improve and enhance the images quality before analyzing the results. Dry and wet ridges are the main issues that developers and researchers need to work on as it provides poor quality image. Medium ridge image is a good condition for analysis, but it also needs to be improved. Therefore, fingerprint images have to control the clearer quality and computing minutiae result and then comparing to templates, which stored in the database. The result will display if it is matched but it will not appear when that person has not yet registered. The paper proposed three algorithms to enhance image, extract minutiae and match with fingerprint templates. The first step, is used to enhance the image quality using brightness and Gabor filters on the fingerprint surface to make ridgelines darker. The second step is to extract minutia. It used to convert the images to binary (0 and 1) and process thinning image with Zhang Suen algorithms. Then, the pictures go through the fixing procedure to correct any missed links, error ridges or spurious minutiae that generated by fingerprint algorithms before they undergo final analysis, calculate location of minutiae and the total of the minutiae on the fingerprint surface. The last step is matching algorithms that can be proof of a person identity by comparing minutiae result with those in the database. If a person has already enrolled, the result will confirm.

Individual Identification of Dairy Cows Based on Deep Feature Extrac-tion and Matching

Individual identification of dairy cows is the prerequisite for automatic analysis and intelligent perception of dairy cows'behavior.At present,individual identification of dairy cows based on deep convolutional neural network had the disadvantages in prolonged training at the additions of new cows samples.Therefore,a cow individual identification framework was proposed based on deep feature extraction and matching,and the individual identification of dairy cows based on this framework could avoid repeated training.Firstly,the trained convolutional neural network model was used as the feature extractor;secondly,the feature extraction was used to extract features and stored the features into the template feature library to complete the enrollment;finally,the identifies of dairy cows were identified.Based on this framework,when new cows joined the herd,enrollment could be completed quickly.In order to evaluate the application performance of this method in closed-set and open-set individual identification of dairy cows,back images of 524 cows were collected,among which the back images of 150 cows were selected as the training data to train feature extractor.The data of the remaining 374 cows were used to generate the template data set and the data to be identified.The experiment results showed that in the closed-set individual identification of dairy cows,the highest identification accuracy of top-1 was 99.73%,the highest identification accuracy from top-2 to top-5 was 100%,and the identification time of a single cow was 0.601 s,this method was verified to be effective.In the open-set individual identification of dairy cows,the recall was 90.38%,and the accuracy was 89.46%.When false accept rate(FAR)=0.05,true accept rate(TAR)=84.07%,this method was verified that the application had certain research value in open-set individual identification of dairy cows,which provided a certain idea for the application of individual identification in the field of intelligent animal husbandry.

Some of the Conventional Accounting Concepts That Are Not in Accordance With the Paradigm of Islamic Accounting

This paper discusses some of the conventional accounting concepts, such as historical cost concept, conservatism concept, matching concept, objectivity concept, stable monetary unit assumption, and going-concern assumption, which are not in accordance with the Islamic accounting paradigm due to their divergence with some verses of the Holy AI-Qur＇an, the Hadiths of the Prophet Muhammad, peace be upon him （pbuh）, and the basic of Zakat calculation. This is a conceptual paper describing some of the conventional accounting concepts that are not in accordance with the paradigm of Islamic accounting. The paper concludes that the historical cost concept can be replaced by： current cash equivalent concept, historical cost and current value concepts （market selling price）, current cost concept, historical cost concept in all （except for Zakat purposes） accounting calculations, current valuation concept, and fair value concept. Conservatism concept must be eliminated if historical cost concept is not used anymore. Matching concept can be replaced by asset-liability approach concept. Objectivity concept can be replaced by Zakat accountability concept. Stability of the monetary unit assumption can be replaced by gold or silver measurement, or Dirham currency, and going-concern assumption must be eliminated.

The automatic height extraction of buildings from shadow region

Height extraction for buildings is a fundamental step of 3D scene reconstruction in many virtual reality applications. In this paper, we propose an automatic method to extract the height of buildings in high resolution satellite imagery based on the length of shadow. Taking into account the limitation of traditional algorithms, we make use of the boundary information of a building to facilitate detecting and matching the shadow regions with higher accuracy. Then, we introduce a shadow-cast model to correct the shadow location in our system. The experimental result shows that when extracting the height of buildings from complex urban regions, our method has better accuracy.

基于OFAST和BRISK特征耦合三重过滤策略的图像匹配算法

为了在不牺牲性能的前提下提高图像匹配算法的检测速度,提出一种组合式的OFAST和BRISK耦合三重过滤策略的图像特征点匹配算法。首先利用OFAST算法提取特征点,采用BRISK特征描述算法计算描述子,之后使用暴力匹配方法计算汉明距离,结合最小距离过滤法对匹配点对进行预筛选,在使用PROSAC算法前通过向量的余弦相似度消除误匹配特征点,优化匹配结果实现图像的准确匹配。反复实验结果证明,该算法能够很好地适应图像的旋转、模糊、尺度变换,保证了匹配过程的运算开销,具有较好的实时性和准确性,解决了误匹配率高和鲁棒性差的问题。

Preparation of protein samples for gel electrophoresis by sequential extraction

Since preparation and solubilization of protein samples are crucial factors in proteome research,the authors established a sequential extraction technique to prepare protein samples from the body wall of the 5th instar larvae of silkworm.Bombyx mori.Two kinds of protein samples were obtained from the body wall using the method.Between the two types of samples only about 15% proteins were identical;the majority were different,indicating that more species of proteins could be obtained with the sequential extraction method;which will be useful for preparation of protein samples for proteome study.

Modified SIFT descriptor and key-point matching for fast and robust image mosaic

To improve the performance of the scale invariant feature transform （ SIFT）, a modified SIFT （M-SIFT） descriptor is proposed to realize fast and robust key-point extraction and matching. In descriptor generation, 3 rotation-invariant concentric-ring grids around the key-point location are used instead of 16 square grids used in the original SIFT. Then, 10 orientations are accumulated for each grid, which results in a 30-dimension descriptor. In descriptor matching, rough rejection mismatches is proposed based on the difference of grey information between matching points. The per- formance of the proposed method is tested for image mosaic on simulated and real-worid images. Experimental results show that the M-SIFT descriptor inherits the SIFT＇ s ability of being invariant to image scale and rotation, illumination change and affine distortion. Besides the time cost of feature extraction is reduced by 50% compared with the original SIFT. And the rough rejection mismatches can reject at least 70% of mismatches. The results also demonstrate that the performance of the pro- posed M-SIFT method is superior to other improved SIFT methods in speed and robustness.

Automatic detection and removal of static shadows

In case of complex textures,existing static shadow detection and removal algorithms are prone to false detection of the pixels.To solve this problem,a static shadow detection and removal algorithm based on support vector machine(SVM)and region sub-block matching is proposed.Firstly,the original image is segmented into several superpixels,and these superpixels are clustered using mean-shift clustering algorithm in the superpixel sets.Secondly,these features such as color,texture,brightness,intensity and similarity of each area are extracted.These features are used as input of SVM to obtain shadow binary images through training in non-operational state.Thirdly,soft matting is used to smooth the boundary of shadow binary graph.Finally,after finding the best matching sub-block for shadow sub-block in the illumination region based on regional covariance feature and spatial distance,the shadow weighted average factor is introduced to partially correct the sub-block,and the light recovery operator is used to partially light the sub-block.The experimental results show the number of false detection of the pixels is reduced.In addition,it can remove shadows effectively for the image with rich textures and uneven shadows and make a natural transition at the boundary between shadow and light.

A Template Matching Based Feature Extraction for Activity Recognition

Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.

Recognition of Natural Road Sign Based on the Improved Curvature Feature

To solve the recognition of road sign with an intelligent vehicle in vision-based navigation,road sign extraction and matching techniques required in outdoor scene was proposed in this paper.The method of the improved curvature based on feature extraction and binary description took the advantage of reasonable features distribution to overcome the problems of traditional features uneven distribution.Binary description method was represented to solve the real-time problem of feature matching.Through the validity and real-time performance of different algorithms are compared by experiments and indicate that the method can not only overcome negative influences from the disturb of non-targets,while spending on average only 46 ms processing each frame,but also meet the requirements of robustness,real-time,and accuracy.

基于平面匹配与目标检测的视觉SLAM算法

针对移动机器人同时定位与建图(SLAM)算法在动态复杂、低纹理场景中存在算法精度下降甚至无法正常工作等问题,提出一种基于快速运动目标检测与平面匹配的算法。将视觉特征提取与运动目标检测方法相结合,降低动态目标对定位的干扰;采用平面匹配技术,克服低纹理环境特征缺少问题;同时生成一个稠密的三维点云地图,用于机器人环境解析等应用。该算法在数据集KITTI和TUM上的绝对轨迹误差RMSE指数相对于ORB-SLAM2算法分别降低了66.67%、98.77%,算法运行速率为22.20 fps。结果表明该算法具有良好定位精度、运行效率和鲁棒性。

一种基于改进ORB特征匹配的无人机视觉导航方法

为了解决在全球导航卫星系统(Global Navigation Satellite System)拒止情况下无人机导航能力缺失等问题,提出了一种基于改进快速提取旋转描述子(Oriented FAST and Rotated Brief,ORB)图像特征匹配的无人机视觉导航方法。首先,为了实现无人机的绝对定位,提出了一种特征图像基准数据库构建方法;其次,为提取图像数据集的特征点,采用了一种结合尺度不变特征变换(Scale Invariant Feature Transform,SIFT)的尺度空间优化ORB特征提取算法;最后,为了将图像特征与图像基准数据库快速匹配并提高其匹配精度,提出了一种改进ORB特征匹配算法——ORB+GMS+PROSAC算法。通过在ArcGIS中分割图像构建基准数据库并进行实验分析,结果表明,基于ORB+GMS+PROSAC特征匹配算法性能显著提升,其中匹配准确率上升5.05%,匹配时间减少41.61%,明显优于其他传统特征匹配算法。

改进ORB-SLAM在嵌入式系统中实现

针对飞行载体的实时ORB-SLAM实现问题,提出一种在嵌入式系统实现的改进ORB(oriented FAST and rotated BRIEF)单目视觉里程计算法。算法首先对输入图像进行灰度化、高斯滤波预处理实现简化运算和图像去噪,考虑到算法移植及在嵌入式系统实现,将图像预处理和ORB图像特征提取与匹配等功能封装为IP(intellectual property)核,布置到硬件系统中,提高特征提取与匹配的速度和正确率,保证位姿估计实时性。搭建ZYNQ嵌入式系统,开展对比实验,实验结果表明:改进后的算法特征点匹配率提高了3.78倍,特征提取与匹配的耗时缩短为原来的1/8,处理图像的帧率可以达到19 fps,满足实时性要求。

基于词模式规则的轻量级日志模板提取方法

传统基于规则的日志解析方法针对每类日志需单独编写规则,且随着系统更新,出现新的日志模式时,需人工再次干预;基于深度学习的日志解析方法虽准确率高,但计算复杂度高。为解决日志解析方法人力成本和计算复杂度高的问题,文中提出一种基于词模式规则的轻量级日志模板提取方法,该方法由初始规则集生成、词模式规则应用、潜在错误样本发掘三个部分构成。首先,原始日志基于自适应随机抽样获取彼此间相似度较低的代表性日志;然后,基于专家反馈提取初始词模式规则集,在词模式规则应用模块对原始日志进行处理并提取日志模板;最后,在潜在错误样本发掘模块检查生成的日志模板聚类,发现潜在的错误分类样本并对其进行规则集更新。经过实验验证,在16个公开日志数据集上,文中方法的平均准确度达到97.8%,与基于深度学习的日志解析算法准确度基本持平;在计算效率方面,文中方法的单线程解析速度达到每秒20000条,且随着可用内核数量的增加,性能持续提升,满足系统日志的故障诊断和安全分析需求。