Clothes Keypoints Detection with Cascaded Pyramid Network

With the development of the society,people's requirements for clothing matching are constantly increasing when developing clothing recommendation system.This requires that the algorithm for understanding the clothing images should be sufficiently efficient and robust.Therefore,we detect the keypoints in clothing accurately to capture the details of clothing images.Since the joint points of the garment are similar to those of the human body,this paper utilizes a kind of deep neural network called cascaded pyramid network(CPN)about estimating the posture of human body to solve the problem of keypoints detection in clothing.In this paper,we first introduce the structure and characteristic of this neural network when detecting keypoints.Then we evaluate the results of the experiments and verify effectiveness of detecting keypoints of clothing with CPN,with normalized error about 5%7%.Finally,we analyze the influence of different backbones when detecting keypoints in this network.

Skeleton Keypoints Extraction Method Combined with Object Detection

Big data is a comprehensive result of the development of the Internet of Things and information systems.Computer vision requires a lot of data as the basis for research.Because skeleton data can adapt well to dynamic environment and complex background,it is used in action recognition tasks.In recent years,skeleton-based action recognition has received more and more attention in the field of computer vision.Therefore,the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human.This paper proposes a skeleton point extraction method combined with object detection,which can focus on the extraction of skeleton keypoints.After a large number of experiments,our model can be combined with object detection for skeleton points extraction,and the detection efficiency is improved.

Copy-Move Forgeries Detection and Localization Using Two Levels of Keypoints Extraction

Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.

Sports Events Recognition Using Multi Features and Deep Belief Network

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or conditions.This recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial intelligence.This research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those people.Common approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton points.Moreover,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or silhouettes.The integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire procedure.These extracted features are passed to the early fusion and DBscan for feature fusion and optimization.Then deep belief,network is employed for recognition.Experimental results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the Information Content Criterion

The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy.

Lightweight Multi-Resolution Network for Human Pose Estimation

Human pose estimation aims to localize the body joints from image or video data.With the development of deeplearning,pose estimation has become a hot research topic in the field of computer vision.In recent years,humanpose estimation has achieved great success in multiple fields such as animation and sports.However,to obtainaccurate positioning results,existing methods may suffer from large model sizes,a high number of parameters,and increased complexity,leading to high computing costs.In this paper,we propose a new lightweight featureencoder to construct a high-resolution network that reduces the number of parameters and lowers the computingcost.We also introduced a semantic enhancement module that improves global feature extraction and networkperformance by combining channel and spatial dimensions.Furthermore,we propose a dense connected spatialpyramid pooling module to compensate for the decrease in image resolution and information loss in the network.Finally,ourmethod effectively reduces the number of parameters and complexitywhile ensuring high performance.Extensive experiments show that our method achieves a competitive performance while dramatically reducing thenumber of parameters,and operational complexity.Specifically,our method can obtain 89.9%AP score on MPIIVAL,while the number of parameters and the complexity of operations were reduced by 41%and 36%,respectively.

DAUNet: Detail-Aware U-Shaped Network for 2D Human Pose Estimation

Human pose estimation is a critical research area in the field of computer vision,playing a significant role in applications such as human-computer interaction,behavior analysis,and action recognition.In this paper,we propose a U-shaped keypoint detection network(DAUNet)based on an improved ResNet subsampling structure and spatial grouping mechanism.This network addresses key challenges in traditional methods,such as information loss,large network redundancy,and insufficient sensitivity to low-resolution features.DAUNet is composed of three main components.First,we introduce an improved BottleNeck block that employs partial convolution and strip pooling to reduce computational load and mitigate feature loss.Second,after upsampling,the network eliminates redundant features,improving the overall efficiency.Finally,a lightweight spatial grouping attention mechanism is applied to enhance low-resolution semantic features within the feature map,allowing for better restoration of the original image size and higher accuracy.Experimental results demonstrate that DAUNet achieves superior accuracy compared to most existing keypoint detection models,with a mean PCKh@0.5 score of 91.6%on the MPII dataset and an AP of 76.1%on the COCO dataset.Moreover,real-world experiments further validate the robustness and generalizability of DAUNet for detecting human bodies in unknown environments,highlighting its potential for broader applications.

Keypoints and Descriptors Based on Cross-Modality Information Fusion for Camera Localization

To address the problem that traditional keypoint detection methods are susceptible to complex backgrounds and local similarity of images resulting in inaccurate descriptor matching and bias in visual localization, keypoints and descriptors based on cross-modality fusion are proposed and applied to the study of camera motion estimation. A convolutional neural network is used to detect the positions of keypoints and generate the corresponding descriptors, and the pyramid convolution is used to extract multi-scale features in the network. The problem of local similarity of images is solved by capturing local and global feature information and fusing the geometric position information of keypoints to generate descriptors. According to our experiments, the repeatability of our method is improved by 3.7%, and the homography estimation is improved by 1.6%. To demonstrate the practicability of the method, the visual odometry part of simultaneous localization and mapping is constructed and our method is 35% higher positioning accuracy than the traditional method.

Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

A severe problem in modern information systems is Digital media tampering along with fake information.Even though there is an enhancement in image development,image forgery,either by the photographer or via image manipulations,is also done in parallel.Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically;thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments.However,high complexity affects the developed methods.Presently,it is complicated to resolve the issue of the speed-accuracy trade-off.For tackling these challenges,this article put forward a quick and effective Copy-Move Forgery Detection(CMFD)system utilizing a novel Quad-sort Moth Flame(QMF)Light Gradient Boosting Machine(QMF-Light GBM).Utilizing Borel Transform(BT)-based Wiener Filter(BWF)and resizing,the input images are initially pre-processed by eliminating the noise in the proposed system.After that,by utilizing the Orientation Preserving Simple Linear Iterative Clustering(OPSLIC),the pre-processed images,partitioned into a number of grids,are segmented.Next,as of the segmented images,the significant features are extracted along with the feature’s distance is calculated and matched with the input images.Next,utilizing the Union Topological Measure of Pattern Diversity(UTMOPD)method,the false positive matches that took place throughout the matching process are eliminated.After that,utilizing the QMF-Light GBM visualization,the visualization of forged in conjunction with non-forged images is performed.The extensive experiments revealed that concerning detection accuracy,the proposed system could be extremely precise when contrasted to some top-notch approaches.

脊髓病变患者体感诱发电位检测及预后判断的可靠性

目的探讨体感诱发电位(SEP)在脊髓病变中的诊断价值。方法采用丹麦Keypoint肌电/诱发电位仪,对84例脊髓病变患者进行SEP检测。84例均行下肢胫后神经SEP(SEPt)检测,其中34例同时行上肢正中神经SEP(SEPm)检测。结果SEPt检测84例中82例异常(97.6%),SEPm检测34例中21例异常(61%),SEPt检测结果异常率明显高于SEPm。异常表现均以波幅降低为主,SEPt异常以波幅降低为主者占73.1%,SEPm异常以波幅降低为主者占90.5%。结论SEP反映了躯体感觉传导通路、脑干网状结构及大脑皮层的功能状态。脊髓病变SEPt检测敏感性明显大于SEPm,异常表现以电位波幅降低为主。因此,SEP不仅对脊髓病变的定位诊断有参考价值,而且对判断脊髓损伤程度、评估疗效及脊髓功能状态均有重要指导意义。

丹麦肌电诱发电位系统基本原理及常见故障

1仪器基本原理 keypoint仪器的工作原理见图1。临床常用肌电图诱发电位仪主要分为3部分：（1）刺激系统：主要有声音、电流、视觉和磁刺激等类型。声刺激通过耳机来实现．引发听觉诱发电位：电刺激通过手柄刺激器（CC-stimulator）来实现．引发体感诱发电位：视觉刺激通过棋盘格来实现．引发视觉诱发电位：磁刺激通过磁刺激器来实现．引发较深部位的神经诱发电位。

惊恐障碍患者听觉诱发电位的动态检测及临床意义

目的探讨惊恐障碍(PD)患者听觉诱发电位(AEP)的特点。方法应用丹麦Keypoint肌电/诱发电位仪,记录29例PD患者(PD组)和29名健康人(NC组)的AEP。PD组患者服用5-羟色胺再摄取抑制剂4周、12周、24周时予以复查。结果(1)治疗前,PD组AEP-N2潜伏期[(237.88±16.64)ms]长于NC组[(223.48±18.27)ms,P<0.05],N1-P2波幅[(4.29±2.02)μV]低于NC组[(8.39±2.49)μV,P<0.01],P2-N2波幅[(4.05±1.57)μV]低于NC组[(7.65±3.54)μV,P<0.01]。(2)治疗后,PD组患者随情绪和行为改善,HAMA分数降低,AEP-N2潜伏期逐渐缩短,至治疗24周时与治疗前差异有显著性[治疗前(237.88±16.64)μV,治疗24周时(210.52±26.58)μV,P<0.05],余各项指标差异无显著性。结论PD患者AEP变化与临床症状有关,且滞后于临床症状的改善。

Object Detection Using SURF and Superpixels

This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accuracy, the proposed method firstly performs the segmentation of the image, under analysis, using the Simple Linear Iterative Clustering (SLIC) superpixels method. Next the key points inside each superpixel are estimated using the Speed-Up Robust Feature (SURF). These key points are then used to carry out the matching task for every detected keypoints of a scene inside the estimated superpixels. In addition, a probability map is introduced to describe the accuracy of the object detection results. Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme proposed by Mae et al.

Keypoint全功能肌电诱发电位故障维修1例

Keypoint全功能肌电诱发电位是维迪公司的一款性能稳定可靠、使用便捷的台式肌电图,在使用中轻轻点击快速完成数据采集即可将患者从痛苦的检查中解放出来。Keypoint具有保留所有原始的波形提供给医师在报告时阅读分析参考诊断。

基于Keypoint RCNN改进模型的物体抓取检测算法

机器人抓取在工业中的应用有两个难点:如何准确地检测可抓取物体,以及如何从检测出的多个物体中选择最优抓取目标。本文在Keypoint RCNN模型中引入同方差不确定性学习各损失的权重,并在特征提取器中加入注意力模块,构成了Keypoint RCNN改进模型。基于改进模型提出了两阶段物体抓取检测算法,第一阶段用模型预测物体掩码和关键点,第二阶段用掩码和关键点计算物体的抓取描述和重合度,重合度表示抓取时的碰撞程度,根据重合度可以从多个可抓取物体中选择最优抓取目标。对照实验证明,相较原模型,Keypoint RCNN改进模型在目标检测、实例分割、关键点检测上的性能均有提高,在自建数据集上的平均精度分别为85.15%、79.66%、86.63%,机器人抓取实验证明抓取检测算法能够准确计算物体的抓取描述、选择最优抓取,引导机器人无碰撞地抓取目标。

Keypoint Description Using Statistical Descriptor with Similarity-Invariant Regions

This article presents a method for the description of key points using simple statistics for regions controlled by neighboring key points to remedy the gap in existing descriptors.Usually,the existent descriptors such as speeded up robust features(SURF),Kaze,binary robust invariant scalable keypoints(BRISK),features from accelerated segment test(FAST),and oriented FAST and rotated BRIEF(ORB)can competently detect,describe,and match images in the presence of some artifacts such as blur,compression,and illumination.However,the performance and reliability of these descriptors decrease for some imaging variations such as point of view,zoom(scale),and rotation.The intro-duced description method improves image matching in the event of such distor-tions.It utilizes a contourlet-based detector to detect the strongest key points within a specified window size.The selected key points and their neighbors con-trol the size and orientation of the surrounding regions,which are mapped on rec-tangular shapes using polar transformation.The resulting rectangular matrices are subjected to two-directional statistical operations that involve calculating the mean and standard deviation.Consequently,the descriptor obtained is invariant(translation,rotation,and scale)because of the two methods;the extraction of the region and the polar transformation techniques used in this paper.The descrip-tion method introduced in this article is tested against well-established and well-known descriptors,such as SURF,Kaze,BRISK,FAST,and ORB,techniques using the standard OXFORD dataset.The presented methodology demonstrated its ability to improve the match between distorted images compared to other descriptors in the literature.

Classification of Images Based on a System of Hierarchical Features

The results of the development of the new fast-speed method of classification images using a structural approach are presented.The method is based on the system of hierarchical features,based on the bitwise data distribution for the set of descriptors of image description.The article also proposes the use of the spatial data processing apparatus,which simplifies and accelerates the classification process.Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure,for which the sets of descriptors are compared.The introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of classification.The noise immunity of the method to additive noise has been experimentally studied.According to the results of the research,the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit distribution.Computing costs increase proportionally with decreasing bit distribution.The method can be used for application tasks where object identification time is critical.

Two-Fold and Symmetric Repeatability Rates for Comparing Keypoint Detectors

The repeatability rate is an important measure for evaluating and comparing the performance of keypoint detectors.Several repeatability rate measurementswere used in the literature to assess the effectiveness of keypoint detectors.While these repeatability rates are calculated for pairs of images,the general assumption is that the reference image is often known and unchanging compared to other images in the same dataset.So,these rates are asymmetrical as they require calculations in only one direction.In addition,the image domain in which these computations take place substantially affects their values.The presented scatter diagram plots illustrate how these directional repeatability rates vary in relation to the size of the neighboring region in each pair of images.Therefore,both directional repeatability rates for the same image pair must be included when comparing different keypoint detectors.This paper,firstly,examines several commonly utilized repeatability rate measures for keypoint detector evaluations.The researcher then suggests computing a two-fold repeatability rate to assess keypoint detector performance on similar scene images.Next,the symmetric mean repeatability rate metric is computed using the given two-fold repeatability rates.Finally,these measurements are validated using well-known keypoint detectors on different image groups with various geometric and photometric attributes.

Cluster Representation of the Structural Description of Images for Effective Classification

The problem of image recognition in the computer vision systems is being studied.The results of the development of efficient classification methods,given the figure of processing speed,based on the analysis of the segment representation of the structural description in the form of a set of descriptors are provided.We propose three versions of the classifier according to the following principles:“object-etalon”,“object descriptor-etalon”and“vector description of the object-etalon”,which are not similar in level of integration of researched data analysis.The options for constructing clusters over the whole set of descriptions of the etalon database,separately for each of the etalons,as well as the optimal method to compare sets of segment centers for the etalons and object,are implemented.An experimental rating of the efficiency of the created classifiers in terms of productivity,processing time,and classification quality has been realized of the applied.The proposed methods classify the set of etalons without error.We have formed the inference about the efficiency of classification approaches based on segment centers.The time of image processing according to the developedmethods is hundreds of times less than according to the traditional one,without reducing the accuracy.

Multi-Level Feature Aggregation-Based Joint Keypoint Detection and Description

Image keypoint detection and description is a popular method to find pixel-level connections between images,which is a basic and critical step in many computer vision tasks.The existing methods are far from optimal in terms of keypoint positioning accuracy and generation of robust and discriminative descriptors.This paper proposes a new end-to-end selfsupervised training deep learning network.The network uses a backbone feature encoder to extract multi-level feature maps,then performs joint image keypoint detection and description in a forward pass.On the one hand,in order to enhance the localization accuracy of keypoints and restore the local shape structure,the detector detects keypoints on feature maps of the same resolution as the original image.On the other hand,in order to enhance the ability to percept local shape details,the network utilizes multi-level features to generate robust feature descriptors with rich local shape information.A detailed comparison with traditional feature-based methods Scale Invariant Feature Transform(SIFT),Speeded Up Robust Features(SURF)and deep learning methods on HPatches proves the effectiveness and robustness of the method proposed in this paper.