Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features

Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight.Herein,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features.Compared with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc datasets.Although the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time demand.The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.

Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

A ROBUST ADAPTIVE VIDEO ENCODER BASED ON HUMAN VISUAL MODEL

A Robust Adaptive Video Encoder (RAVE) based on human visual model is proposed. The encoder combines the best features of Fine Granularity Scalable (FGS) coding, framedropping coding, video redundancy coding, and human visual model. According to packet loss and available bandwidth of the network, the encoder adjust the output bit rate by jointly adapting quantization step-size instructed by human visual model, rate shaping, and periodically inserting key frame. The proposed encoder is implemented based on MPEG-4 encoder and is compared with the case of a conventional FGS algorithm. It is shown that RAVE is a very efficient robust video encoder that provides improved visual quality for the receiver and consumes equal or less network resource. Results are confirmed by subjective tests and simulation tests.

New algorithm for infrared small target image enhancement based on wavelet transform and human visual properties

The key to the wavelet based denoising teehniquea is how to manipulate the wavelet coefficients. By referring to the idea of Inclusive-OR in the design of circuits, this paper proposes a new algorithm called wavelet domain Inclusive-OR denoising algorithm（WDIDA）, which distinguishes the wavelet coefficients belonging to image or noise by considering their phases and modulus maxima simultaneously. Using this new algorithm, the denoising effects are improved and the computation time is reduced. Furthermore, in order to enhance the edges of the image but not magnify noise, a contrast nonlinear enhancing algorithm is presented according to human visual properties. Compared with traditional enhancing algorithms, the algorithm that we proposed has a better noise reducing performanee , preserving edges and improving the visual quality of images.

Magnetic resonance diffusion tensor imaging and diffusion tensor tractography of human visual pathway

AIM: To investigate the visual pathway in normal subjects and patients with lesion involved by diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT). METHODS: Thirty normal volunteers, 3 subjects with orbital tumors involved the optic nerve (ON) and 33 subjects with occipital lobe tumors involved the optic radiation (OR) (10 gliomas, 6 meningiomas and 17 cerebral metastases) undertook routine cranium magnetic resonance imaging (MRI), DTI and DTT. Visual pathway fibers were analyzed by DTI and DTT images. Test fractional anisotropy (FA) and mean diffusivity (MD) values in different part of the visual pathway. RESULTS: The whole visual pathway but optic chiasm manifested as hyperintensity in FA maps and homogenous green signal in the direction encoded color maps. The optic chiasm did not display clearly. There was no significant difference between the bilateral FA values and MD values of normal visual pathway but optic chiasm, which the FA values tested were much too low (all P>0.05). The ONs of subjects with orbital tumors were compressed and displaced. Only one subject had lower FA values and higher MD values. OR of 9 gliomas subjects were infiltrated, with displacement in 2 and disruption in 7 subjects. All OR in 6 meniongiomas subjects were displaced. OR in 17 cerebral metastases subjects all developed displacement while 7 of them had disruption also. CONCLUSION: MR-DTI is highly sensitive in manifesting visual pathway. Visual pathway can be analyzed quantitatively in FA and MD values. DTT supplies accurate three dimensional conformations of visual pathway. But optic chiasm's manifestation still needs to improve.

Human Visual Acuity in Analysis of Intervention in the Landscape of Coxilha Rica/Brazil

The Drovers’ Paths are remnants of important land access roads from Rio Grande do Sul to São Paulo at the time of Colonial Brazil. They were built and used between the 18th and 20th centuries, particularly in the region of Coxilha Rica. The main objective of this research is to develop a method for decision-making applied to the territorial landscape management in the Coxilha Rica. The method consisted of generating criteria to map the visibility spot reached from the main selected points;define the human visual acuity, realize bibliographic research, use cartographic and historical documents, inter-views, as well as field surveys that enabled the identification, characterization and mapping of historical farms and drovers’ paths. After data processing, the information was entered into the cartographic database;the data were cross-checked and analysis was made of the visibility of the surrounding farms and stone-walled corridors. Quality assessments showed that, with the visibility polygons, and through the use of cartographic tools, we could cross-check between different levels of information and analyze landscape intervention alternatives in order to minimize environmental impacts. When applying the method in the Coxilha Rica it was possible mapping the visibility polygon, taking human visual acuity into consideration, based on historical farms and stone-walled corridors;and making spatial analyses to explore alternatives to intervention (installation of power transmission systems) in order to preserve the scenic environment of the region. In the end, the decision was by does not construct the system.

Local field potentials,spiking activity,and receptive fields in human visual cortex

The concept of receptive field(RF) is central to sensory neuroscience. Neuronal RF properties have been substantially studied in animals,while those in humans remain nearly unexplored. Here, we measured neuronal RFs with intracranial local field potentials(LFPs) and spiking activity in human visual cortex(V1/V2/V3). We recorded LFPs via macro-contacts and discovered that RF sizes estimated from lowfrequency activity(LFA, 0.5–30 Hz) were larger than those estimated from low-gamma activity(LGA, 30–60 Hz) and high-gamma activity(HGA, 60–150 Hz). We then took a rare opportunity to record LFPs and spiking activity via microwires in V1 simultaneously. We found that RF sizes and temporal profiles measured from LGA and HGA closely matched those from spiking activity. In sum, this study reveals that spiking activity of neurons in human visual cortex could be well approximated by LGA and HGA in RF estimation and temporal profile measurement, implying the pivotal functions of LGA and HGA in early visual information processing.

Exploring the Association between Climate Change and Human Development: A Visual Analytics Study

This study explores the complex relationship between climate change and human development. The aim is to understand how climate change affects human development across countries, regions, and the global population. Visual analytics were used to examine the impact of various climate change indicators on different aspects of human development. The study highlights the urgent need for climate change action and encourages policymakers to make decisive moves. Climate change adversely affects numerous aspects of daily life, leading to significant consequences that must be addressed through policy changes and global governance recommendations. Key findings include that regions with higher CO2 emissions experience a significantly higher incidence of life-threatening diseases compared to regions with lower emissions. Additionally, higher CO2 emissions correlate with consistent death rates. Increased pollution exposure is associated with a higher prevalence of life-threatening diseases and higher rates of malnutrition. Moreover, greater mineral depletion is linked to more frequent life-threatening diseases, suggesting that industrialization contributes to adverse health effects. These results provide valuable insights for policy and decision-making aimed at mitigating the impact of climate change on human development.

Evaluating Methods of Visual Assistance for Workers to Improve Quality and Usability in Individualized Kitchen Cabinet Assembly

In times of digitalisation, visual assistance systems in assembly are increasingly important. The design of these assembly systems needs to be highly complex to meet the requirements. Due to the increasing number of variants in production processes, as well as shorter innovation and product life cycles, assistance systems should improve quality and reduce complexity of assembly processes. However, many large kitchen manufacturers still assemble kitchen cabinets manually, due to the high variety of components, such as rails and fittings. This paper focuses on the analysis and evaluation of virtual assistance systems to improve quality and usability in individualised kitchen cabinet assembly processes at a large German manufacturer. A solution is identified and detailed.

A Novel Color Image Compression Algorithm Using the Human Visual Contrast Sensitivity Characteristics

In order to achieve higher image compression ratio and improve visual perception of the decompressed image, a novel color image compression scheme based on the contrast sensitivity characteristics of the human visual system （HVS） is proposed. In the proposed scheme, firstly the image is converted into the YCrCb color space and divided into sub-blocks. Afterwards, the discrete cosine transform is carried out for each sub-block, and three quantization matrices are built to quantize the frequency spectrum coefficients of the images by combining the contrast sensitivity characteristics of HVS. The Huffman algorithm is used to encode the quantized data. The inverse process involves decompression and matching to reconstruct the decompressed color image. And simulations are carried out for two color images. The results show that the average structural similarity index measurement （SSIM） and peak signal to noise ratio （PSNR） under the approximate compression ratio could be increased by 2.78% and 5.48%, respectively, compared with the joint photographic experts group （JPEG） compression. The results indicate that the proposed compression algorithm in the text is feasible and effective to achieve higher compression ratio under ensuring the encoding and image quality, which can fully meet the needs of storage and transmission of color images in daily life.

Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

Rendering algorithms for aberrated human vision simulation

Vision-simulated imagery―the process of generating images that mimic the human visual system―is a valuable tool with a wide spectrum of possible applications, including visual acuity measurements, personalized planning of corrective lenses and surgeries, vision-correcting displays, vision-related hardware development, and extended reality discomfort reduction. A critical property of human vision is that it is imperfect because of the highly influential wavefront aberrations that vary from person to person. This study provides an overview of the existing computational image generation techniques that properly simulate human vision in the presence of wavefront aberrations. These algorithms typically apply ray tracing with a detailed description of the simulated eye or utilize the point-spread func-tion of the eye to perform convolution on the input image. Based on the description of the vision simulation tech-niques, several of their characteristic features have been evaluated and some potential application areas and research directions have been outlined.

Human melanopsin-AAV2/8 transfection to retina transiently restores visual function in rd1 mice

AIM： To explore whether ectopic expression of human melanopsin can effectively and safely restore visual function in rd1 mice.· METHODS： Hematoxylin-eosin staining of retinal sections from rd1 mice was used to detect the thickness of the outer nuclear layer to determine the timing of surgery. We constructed a human melanopsinAAV2/8 viral vector and injected it into the subretinal space of rd1 mice. The Phoenix Micron IV system was used to exclude the aborted injections, and immunohistochemistry was used to validate the ectopic expression of human melanopsin. Furthermore, visual electrophysiology and behavioral tests were used to detect visual function 30 and 45 d after the injection. The structure of the retina was compared between the human melanopsin-injected group and phosphate buffer saline（PBS）-injected group.·RESULTS： Retinas of rd1 mice lost almost all of their photoreceptors on postnatal day 28（P28）. We therefore injected the human melanopsin-adeno-associated virus（AAV） 2/8 viral vector into P30 rd1 mice. After excluding aborted injections, we used immunohistochemistry of the whole mount retina to confirm the ectopic expression of human melanopsin by co-expression of human melanopsin and YFP that was carried by a viral vector. At30 d post-injection, visual electrophysiology and the behavioral test significantly improved. However,restoration of vision disappeared 45 d after human melanopsin injection. Notably, human melanopsin-injected mice did not show any structural differences in their retinas compared with PBS-injected mice.·CONCLUSION： Ectopic expression of human melanopsin effectively and safely restores visual function in rd1

雾气下心电监护仪界面视觉搜索效率研究

目的研究护目镜产生雾气情况下心电监护仪界面的字符大小及颜色一致性对信息视觉搜索效率的影响。方法通过模拟护目镜在不同雾气程度下的场景,控制不同字符大小、颜色一致性,考察三者对医护人员视觉搜索效率的影响。实验中37位被试依次完成眼动实验,记录其行为数据和眼动数据,深入探讨三种变量因素的交互效应。结果使用心电监护仪时不同雾气程度、字符大小、颜色一致性三种因素的交互效应显著,其中行为数据的三种因素交互效应显著P<0.001,眼动数据的三种因素交互效应显著P<0.05。未产生雾气与低雾气场景下,大字符、颜色不一致视觉搜索效率最高;中雾气与高雾气场景下,中字符、颜色不一致的视觉搜索效率最高。结论三种因素在不同条件下,交互效应的视觉搜索效率不同,设计人员可根据护目镜产生的雾气程度,调整心电监护仪界面字符大小与颜色一致性,以提升医护人员的视觉搜索效率。

医学人文教育教学改革课题多维度可视化分析研究

本文采用统计、分词、可视化等分析工具,从课题负责人、研究主题、研究趋势等多个维度,对首都医科大学医学人文学院近5年获批的教改课题进行分析,结合医学人文教改实践成效,探讨了新时代教育新理念和新要求对医学人文教育教学改革的促进,医学人文教育平台提供多学科交叉融合,教育教学改革实践促进教师教学能力发展,为推进医学人文教育教学改革创新提供了思路。

AI+BCI硅基碳基融合新智能的开始

我们正迎来人类发展的第四次浪潮,正处于从信息社会向人类社会-物理世界-信息空间融合的智能社会的关键转型期。近年来,计算和信息技术飞速发展,深度学习的空前普及和成功将人工智能(AI)确立为人类探索机器智能的前沿领域。与此同时,得益于器件的革命性进展和人工智能(AI)的发展,脑机接口(BCI)植入技术同样快速落地,这意味着BCI+AI碳基硅基融合的开始,然而,硅基和碳基运算的底层逻辑存在根本差异,脑的智能机制仍有待进一步探索。本研究提出的视觉认知引导的孪生AI深度网络,是由个人意识驱动的深度网络技术,通过捕捉并解析个体的思维模式和创意灵感,为每个用户量身打造独特的视觉世界。在这样的环境中,每个人都成为自己创造世界的视觉主导者,打破物质和意识的壁垒,得以展现丰富的个性和创造力。

数字人文视域下馆藏清代土尔扈特档案文献可视化开发

清乾隆年间,土尔扈特部回归祖国事件形成的档案文献是中华民族共同体演进发展的档案佐证。数字人文环境下,对其档案进行可视化开发,可深入挖掘档案文本中的民族交往交流交融价值,提升档案馆知识服务效能,满足多元利用需求。据此,在阐释土尔扈特档案文献内涵外延、资源特征及其可视化开发理论与价值的基础上,提出以项目实施为依托的“多元共建共享”土尔扈特档案文献可视化开发方案。从档案的数据化整合、语义化处理与可视化服务等流程建构可视化开发路径,对于发掘土尔扈特档案文献中蕴含的爱国主义精神、维护新疆民族地区稳定发展,铸牢中华民族共同体意识具有重要现实意义。

基于贪婪算法的云计算数据块节能存储仿真

针对云数据储存能量消耗大的问题,提出基于贪婪算法的云计算数据块节能存储方法。建立具有用户访问层、核心服务层和服务管理层的云计算架构,了解数据块产生过程和储存环境;将物理机利用率、能源消耗量和主机储存能力作为节能储存的约束条件,将待储存的数据块封装为虚拟机,利用贪婪算法描述虚拟机部署问题,构建贪婪算法下虚拟机分配环境;计算单个物理机和整个数据中心的数据块储存能力和资源请求能力,综合考虑虚拟机分配的相关向量,运算数据储存时的能量消耗;以总体能量最小为目标函数,将虚拟机分为主模块与备用模块,通过设置虚拟机状态转换规则来减少储存开销,实现节能储存。实验结果表明,上述方法在数据储存过程中能够有效减少服务器开启数量,节省储存功率,达到节能目的。

雷达双层双模健康监视架构设计

雷达设备健康监视主要实现雷达故障预测与健康管理系统的数据解析和显示。传统的监视架构为单一架构层次、面向单一对象的监视架构,并且软件实现与设备紧耦合,其开发和维护成本巨大。文中首次将模型-视图-代理(MVD)模式应用到雷达状态监视架构设计中,提出一种基于MVD的双层双模架构。该架构无需代码定义接口结构体,利用模型工具软件直接编辑设备编号、响应策略,利用高效的封装基类将内置测试设备的状态数据与显示实现挂接;采用“全面+重点”双布局、“用户+专家”双模式设计,可实现目标态势与健康态势同时掌握,并满足用户/专家等不同对象的显示需求。通过工程验证,相比于传统的健康监视架构,该架构提供多样化作战视图,提高了软件可读性、可维护性,节约了软件开发成本,提高了开发效率。

基于双层局部能量因子的红外小目标检测方法

红外小目标的检测一直是红外追踪系统的关键技术,针对现有红外小目标检测方法在复杂背景下易造成虚警、检测速度慢的不足,从人类视觉系统的角度出发,参考了多尺度局部能量因子检测方法(multiscale local contrast measure using a local energy factor,MLCM-LEF),提出了一种基于双层局部能量因子的红外小目标检测方法.从局部能量差异与局部亮度差异两个角度进行目标检测,使用双层局部能量因子从能量角度描述小目标与背景的相异程度,同时采取加权亮度差因子从亮度角度对图像进行目标检测,通过二维高斯融合上述二者的处理结果,最终利用图像均值和标准差进行自适应阈值分割,提取红外小目标.经过公开数据集实验测试,该方法在抑制背景噪声、减低虚警概率的表现上比主流的检测方法有所提升,与MLCM-LEF算法相比,基于双层局部能量因子的方法将单帧检测时间降低至三分之一.