Effect of low-degree astigmatism on the objective visual quality

AIM:To evaluate the effect of low-degree astigmatism on objective visual quality through the Optical Quality Analysis System(OQAS).METHODS:This study enrolled 46 participants(aged 23 to 30y,90 eyes)with normal or corrected-to-normal vision.The cylindrical lenses(0,0.5,0.75,1.0,and 1.25 D)were placed at the axial direction(180°,45°,90°,and 135°)in front of the eyes with the best correction to form 16 types of regular low-degree astigmatism.OQAS was used to detect the objective visual quality,recorded as the objective scattering index(OSI),OQAS values at contrasts of 100%,20%,and 9%predictive visual acuity(OV100%,OV20%,and OV9%),modulation transfer function cut-off(MTFcut-off)and Strehl ratio(SR).The mixed effect linear model was used to compare objective visual quality differences between groups and examine associations between astigmatic magnitude and objective visual quality parameters.RESULTS:Apparent negative relationships between the magnitude of low astigmatism and objective visual quality were observed.The increase of OSI per degree of astigmatism at 180°,45°,90°,and 135°axis were 0.38(95%CI:0.35,0.42),0.50(95%CI:0.46,0.53),0.49(95%CI:0.45,0.54)and 0.37(95%CI:0.34,0.41),respectively.The decrease of MTFcut-off per degree of astigmatism at 180°,45°,90°,and 135°axis were-10.30(95%CI:-11.43,-9.16),-12.73(95%CI:-13.62,-11.86),-12.75(95%CI:-13.79,-11.70),and-9.97(95%CI:-10.92,-9.03),respectively.At the same astigmatism degree,OSI at 45°and 90°axis were higher than that at 0°and 135°axis,while MTFcut-off were lower.CONCLUSION:Low astigmatism of only 0.50 D can significantly reduce the objective visual quality.

Visual SLAM Based on Object Detection Network:A Review

Visual simultaneous localization and mapping(SLAM)is crucial in robotics and autonomous driving.However,traditional visual SLAM faces challenges in dynamic environments.To address this issue,researchers have proposed semantic SLAM,which combines object detection,semantic segmentation,instance segmentation,and visual SLAM.Despite the growing body of literature on semantic SLAM,there is currently a lack of comprehensive research on the integration of object detection and visual SLAM.Therefore,this study aims to gather information from multiple databases and review relevant literature using specific keywords.It focuses on visual SLAM based on object detection,covering different aspects.Firstly,it discusses the current research status and challenges in this field,highlighting methods for incorporating semantic information from object detection networks into mileage measurement,closed-loop detection,and map construction.It also compares the characteristics and performance of various visual SLAM object detection algorithms.Lastly,it provides an outlook on future research directions and emerging trends in visual SLAM.Research has shown that visual SLAM based on object detection has significant improvements compared to traditional SLAM in dynamic point removal,data association,point cloud segmentation,and other technologies.It can improve the robustness and accuracy of the entire SLAM system and can run in real time.With the continuous optimization of algorithms and the improvement of hardware level,object visual SLAM has great potential for development.

Application of 3D Scanned Big Data of Large-scale Cultural Heritage Objects Based on Noise-robust Transparent Visualization

Three-dimensional(3D) scanning technology has undergone remarkable developments in recent years.Data acquired by 3D scanning have the form of 3D point clouds.The 3D scanned point clouds have data sizes that can be considered big data.They also contain measurement noise inherent in measurement data.These properties of 3D scanned point clouds make many traditional CG/visualization techniques difficult.This paper reviewed our recent achievements in developing varieties of high-quality visualizations suitable for the visual analysis of 3D scanned point clouds.We demonstrated the effectiveness of the method by applying the visualizations to various cultural heritage objects.The main visualization targets used in this paper are the floats in the Gion Festival in Kyoto(the float parade is on the UNESCO Intangible Cultural Heritage List) and Borobudur Temple in Indonesia(a UNESCO World Heritage Site).

Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged people.Object detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so on.The test images include all these variants as well.These detection models alert them about their surroundings when they want to walk independently.This study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition applications.Based on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed.To further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the work.Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization approach.Additionally,Inception V3 can handle images of different sizes.This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

Visual impairment is one of the major problems among people of all age groups across the globe.Visually Impaired Persons(VIPs)require help from others to carry out their day-to-day tasks.Since they experience several problems in their daily lives,technical intervention can help them resolve the challenges.In this background,an automatic object detection tool is the need of the hour to empower VIPs with safe navigation.The recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it possible.The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help VIPs.The primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to them.For data acquisition,IoT devices are used in this study.Then,the Lightweight RetinaNet(LWR)model is applied to detect objects accurately.Next,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR model.Finally,the Long Short-Term Memory(LSTM)model is exploited for classifying objects.The performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct aspects.The comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs.

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption.To cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed.To reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system.Experiment with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes.

Visual Object Tracking and Servoing Control of a Nano-Scale Quadrotor:System,Algorithms,and Experiments

There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems.In this paper,we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor.A lightweight visual module is integrated to enable object tracking capabilities,and a micro positioning deck is mounted to provide accurate pose estimation.In order to be robust against object appearance variations,a novel object tracking algorithm,denoted by RMCTer,is proposed,which integrates a powerful short-term tracking module and an efficient long-term processing module.In particular,the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner.Furthermore,a positionbased visual servoing control method is proposed for the quadrotor,where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques.Stable and accurate object tracking is achieved even under disturbances.Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.

A Visual Attention Model for Robot Object Tracking

Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 × 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.

Effective method for tracking multiple objects in real-time visual surveillance systems

An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.

The IRIS Development Platform and Proposed Object-Oriented Data Base

Various code development platforms, such as the ATHENA Framework [1] of the ATLAS [2] experiment encounter lengthy compilation/linking times. To augment this situation, the IRIS Development Platform was built as a software development framework acting as compiler, cross-project linker and data fetcher, which allow hot-swaps in order to compare various versions of software under test. The flexibility fostered by IRIS allowed modular exchange of software libraries among developers, making it a powerful development tool. The IRIS platform used input data ROOT-ntuples [3];however a new data model is sought, in line with the facilities offered by IRIS. The schematic of a possible new data structuring—as a user implemented object oriented data base, is presented.

Dual Attribute Adversarial Camouflage toward camouflaged object detection

The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong prediction.It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance.Motivated by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and humans.Generating DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC.The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features.Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital domain.This work can serve as the reference for crafting the adversarial camouflage in the physical world.

VISUAL SIMULATION OF COLD ROLL-FORMING BASED ON OBJECT ORIENTED PROGRAMMING

To simulate the process of cold roll-forming process, a new method isadopted. The theoretical foundation of this method is an elastic-plastic large deformation splinefinite strip method based on object-oriented programming. Combined with the computer graphicstechnology, the visual simulation of cold roll-forming is completed and the system is established.By analyzing common channel steel, the process is shown and explained including theory method, modeland result display. So the simulation system is already a kind of mature and effective tool toanalyze the process of cold roll forming.

A Visualizing Software Developing Environment Based Object Model

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Enhancing the Robustness of Visual Object Tracking via Style Transfer

The performance and accuracy of computer vision systems are affected by noise in different forms.Although numerous solutions and algorithms have been presented for dealing with every type of noise,a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing.In this paper,we have focused on the stability and robustness of one computer vision branch(i.e.,visual object tracking).We have demonstrated that,without imposing a heavy computational load on a model or changing its algorithms,the drop in the performance and accuracy of a system when it is exposed to an unseen noise-laden test dataset can be prevented by simply applying the style transfer technique on the train dataset and training the model with a combination of these and the original untrained data.To verify our proposed approach,it is applied on a generic object tracker by using regression networks.This method’s validity is confirmed by testing it on an exclusive benchmark comprising 50 image sequences,with each sequence containing 15 types of noise at five different intensity levels.The OPE curves obtained show a 40%increase in the robustness of the proposed object tracker against noise,compared to the other trackers considered.

A secure visual framework for multi-index protection evaluation in networks

Mining the core value of Industrial Internet of Things(IIoT)data safely and reducing the risk of malicious attacks are the inherent requirements of industrial data visualization.Visualization technology has become the main tool for data aggregation,mining and analysis of IIoT data through graphical representation.However,visualization technology still has two shortcomings in big data calculation and analysis scenarios.On the one hand,visual results will lead to the disclosure of sensitive privacy.On the other hand,most visualization tools can't provide an interactive framework for users to select the suitable solutions.To address these problems,we present an open accessible Visual framework based on Differential Privacy theory(VisDP),which provides Multi-index Quantitative comprehensive Evaluation technology(MQE)for data mining results.Considering the advantages of interactive mechanism,VisDP provides rich optional schemes,including the operating web,calling API and the downloading SDK.Finally,we verify the availability and privacy of MQE through mathematical proofs,analyze the hospital medical waste detection system that actually applies the framework,and the experimental results have showed the effectiveness and practicality of the proposed platform.

MFV-Class： a multi-faceted visualization tool of object classes

Classes are key software components in an object-oriented software system. In many industrial OO software systems, there are some classes that have complicated structure and relationships. So in the processes of software maintenance, testing, software reengineering, software reuse and software restructure, it is a challenge for software engineers to understand these classes thoroughly. This paper proposes a class comprehension model based on constructivist learning theory, and implements a software visualization tool (MFV-Class) to help in the comprehension of a class. The tool provides multiple views of class to uncover manifold facets of class contents. It enables visualizing three object-oriented metrics of classes to help users focus on the understanding process. A case study was conducted to evaluate our approach and the toolkit.

A survey of occlusion detection method for visual object

Occlusion problem is one of the challenging issues in vision field for a long time,and the occlusion phenomenon of visual object will be involved in many vision research fields. Once the occlusion occurs in a visual system,it will affect the effects of object recognition,tracking,observation and operation,so detecting occlusion autonomously should be one of the abilities for an intelligent vision system. The research on occlusion detection method for visual object has increasingly attracted attentions of scholars. First,the definition and classification of the occlusion problem are presented.Then,the characteristics and deficiencies of the occlusion detection methods based on the intensity image and the depth image are analyzed respectively,and the existing occlusion detection methods are compared. Finally,the problems of existing occlusion detection methods and possible research directions are pointed out.

3D Object Visual Tracking for the 220 kV/330 kV High-Voltage Live-Line Insulator Cleaning Robot

The 3D object visual tracking problem is studied for the robot vision system of the 220kV/330kV high-voltage live-line insulator cleaning robot. The SUSAN Edge based Scale Invariant Feature (SESIF) algorithm based 3D objects visual tracking is achieved in three stages: the first frame stage,tracking stage,and recovering stage. An SESIF based objects recognition algorithm is proposed to find initial location at both the first frame stage and recovering stage. An SESIF and Lie group based visual tracking algorithm is used to track 3D object. Experiments verify the algorithm's robustness. This algorithm will be used in the second generation of the 220kV/330kV high-voltage live-line insulator cleaning robot.

Prioritized MPEG-4 Audio-Visual Objects Streaming over the DiffServ

The object-based scalable coding in MPEG-4 is investigated, and a prioritized transmission scheme of MPEG-4 audio-visual objects （AVOs） over the DiffServ network with the QoS guarantee is proposed. MPEG-4 AVOs are extracted and classified into different groups according to their priority values and scalable layers （visual importance）. These priority values are mapped to the 1P DiffServ per hop behaviors （PHB）. This scheme can selectively discard packets with low importance, in order to avoid the network congestion. Simulation results show that the quality of received video can gracefully adapt to network state, as compared with the ‘best-effort＇ manner. Also, by allowing the content provider to define prioritization of each audio-visual object, the adaptive transmission of object-based scalable video can be customized based on the content.

面向目标检测的视觉注意机制波段选择研究

近年来,波段选择在高光谱图像降维处理中得到了广泛地应用,然而常用的数据降维方法并没能将与人类视觉系统相关的信息进行有效利用,如果将人类与生俱来的视觉注意机制能力应用到高光谱图像中目标的视觉显著性特征的增强或识别,对于高光谱图像的目标检测研究无疑会产生相当的促进作用。研究提出引入视觉注意机制理论应用于波段选择研究,构建面向目标检测应用的视觉注意机制波段选择模型。通过分析计算波段图幅的目标与背景的可识别程度,量化所在波段对地物目标与背景的判别能力,提出了基于目标视觉可识别度的波段选择方法;利用LC显著性算法进行空间域的视觉显著性目标分析,计算背景与目标的显著性差异绝对值,提出基于LC显著目标结构分布的波段选择方法。将这两种方法结合提出的改进子空间划分方法,建立面向目标检测的视觉注意机制波段选择模型,并经高光谱遥感AVIRIS San Diego公开数据集进行目标检测实验验证,结果表明所提出的基于视觉注意机制的波段选择模型对于目标检测应用具有较好的检测效果,实现了数据降维和高效的计算处理。