For decades visual field defects were considered irreversible because it was thought that in the visual system the regeneration potential of the neuronal tissues is low.Nevertheless,there is always some potential for ...For decades visual field defects were considered irreversible because it was thought that in the visual system the regeneration potential of the neuronal tissues is low.Nevertheless,there is always some potential for partial recovery of the visual field defect that can be achieved through induction of neuroplasticity.Neuroplasticity refers to the ability of the brain to change its own functional architecture by modulating synaptic efficacy.It is maintained throughout life and just as neurological rehabilitation can improve motor coordination,visual field defects in glaucoma,diabetic retinopathy or optic neuropathy can be improved by inducing neuroplasticity.In ophthalmology many new treatment paradigms have been tested that can induce neuroplastic changes,including non-invasive alternating current stimulation.Treatment with alternating current stimulation(e.g.,30 minutes,daily for 10 days using transorbital electrodes and^10 Hz)activates the entire retina and parts of the brain.Electroencephalography and functional magnetic resonance imaging studies revealed local activation of the visual cortex,global reorganization of functional brain networks,and enhanced blood flow,which together activate neurons and their networks.The future of low vision is optimistic because vision loss is indeed,partially reversible.展开更多
This paper presents a method for structured scene modeling using micro stereo vision system with large field of view. The proposed algorithm includes edge detection with Canny detector, line fitting with principle axi...This paper presents a method for structured scene modeling using micro stereo vision system with large field of view. The proposed algorithm includes edge detection with Canny detector, line fitting with principle axis based approach, finding corresponding lines using feature based matching method, and 3D line depth computation.展开更多
In this study, we develop a mixed reality game system to investigate characteristics ofjudgrnents of individual players in an evacuation process. The characteristics of judgments of the players that are inferred from ...In this study, we develop a mixed reality game system to investigate characteristics ofjudgrnents of individual players in an evacuation process. The characteristics of judgments of the players that are inferred from the performance of the game are then incorporated into a multi-agent simulation as rules. The behavior of evacuees is evaluated in approximations of real situations, by using the agent simulation including different judgments of evacuees. Using the results of the simulation, effective methods are discussed for achieving the escape of the evacuees within a short time.展开更多
单目三维视觉测量在视觉测量领域具有低成本、简便性、结构紧凑等优势,是以智能化、网络化制造为特征的先进制造典型技术之一。经过不断发展,单目三维视觉测量技术已成功应用于无人机导航、智能机器人、工业检测、医疗健康等领域,如今...单目三维视觉测量在视觉测量领域具有低成本、简便性、结构紧凑等优势,是以智能化、网络化制造为特征的先进制造典型技术之一。经过不断发展,单目三维视觉测量技术已成功应用于无人机导航、智能机器人、工业检测、医疗健康等领域,如今呈现出精准化、快捷化、微型化、自动化、动态化等发展趋势。以孔径数量为标准,将单目三维视觉测量技术分为单孔径及多孔径两大类,分别综述两类方法的研究现状和发展历程,重点论述了应用较广的运动恢复结构法(Structure From Motion,SFM)和光场三维测量方法,并对单目三维视觉测量技术的未来方向进行了展望。展开更多
为解决光线遮蔽、藻萍干扰以及稻叶尖形状相似等复杂环境导致稻田杂草识别效果不理想问题,该研究提出一种基于组合深度学习的杂草识别方法。引入MSRCP(multi-scale retinex with color preservation)对图像进行增强,以提高图像亮度及对...为解决光线遮蔽、藻萍干扰以及稻叶尖形状相似等复杂环境导致稻田杂草识别效果不理想问题,该研究提出一种基于组合深度学习的杂草识别方法。引入MSRCP(multi-scale retinex with color preservation)对图像进行增强,以提高图像亮度及对比度;加入ViT分类网络去除干扰背景,以提高模型在复杂环境下对小目标杂草的识别性能。在YOLOv7模型中主干特征提取网络替换为GhostNet网络,并引入CA注意力机制,以增强主干特征提取网络对杂草特征提取能力及简化模型参数计算量。消融试验表明:改进后的YOLOv7模型平均精度均值为88.2%,较原YOLOv7模型提高了3.3个百分点,参数量减少10.43 M,计算量减少66.54×109次/s。识别前先经过MSRCP图像增强后,与原模型相比,改进YOLOv7模型的平均精度均值提高了2.6个百分点,光线遮蔽、藻萍干扰以及稻叶尖形状相似的复杂环境下平均精度均值分别提高5.3、3.6、3.1个百分点,加入ViT分类网络后,较原模型平均精度均值整体提升了4.4个百分点,光线遮蔽、藻萍干扰一级稻叶尖形状相似的复杂环境下的平均精度均值较原模型整体提升了6.2、6.1、5.7个百分点。ViT-改进YOLOv7模型的平均精度均值为92.6%,相比于YOLOv5s、YOLOXs、MobilenetV3-YOLOv7、YOLOv8和改进YOLOv7分别提高了11.6、10.1、5.0、4.2、4.4个百分点。研究结果可为稻田复杂环境的杂草精准识别提供支撑。展开更多
In recent years,self-supervised learning which does not require a large number of manual labels generate supervised signals through the data itself to attain the characterization learning of samples.Self-supervised le...In recent years,self-supervised learning which does not require a large number of manual labels generate supervised signals through the data itself to attain the characterization learning of samples.Self-supervised learning solves the problem of learning semantic features from unlabeled data,and realizes pre-training of models in large data sets.Its significant advantages have been extensively studied by scholars in recent years.There are usually three types of self-supervised learning:"Generative,Contrastive,and GeneTative-Contrastive."The model of the comparative learning method is relatively simple,and the performance of the current downstream task is comparable to that of the supervised learning method.Therefore,we propose a conceptual analysis framework:data augmentation pipeline,architectures,pretext tasks,comparison methods,semisupervised fine-tuning.Based on this conceptual framework,we qualitatively analyze the existing comparative self-supervised learning methods for computer vision,and then further analyze its performance at different stages,and finally summarize the research status of sei supervised comparative learning methods in other fields.展开更多
文摘For decades visual field defects were considered irreversible because it was thought that in the visual system the regeneration potential of the neuronal tissues is low.Nevertheless,there is always some potential for partial recovery of the visual field defect that can be achieved through induction of neuroplasticity.Neuroplasticity refers to the ability of the brain to change its own functional architecture by modulating synaptic efficacy.It is maintained throughout life and just as neurological rehabilitation can improve motor coordination,visual field defects in glaucoma,diabetic retinopathy or optic neuropathy can be improved by inducing neuroplasticity.In ophthalmology many new treatment paradigms have been tested that can induce neuroplastic changes,including non-invasive alternating current stimulation.Treatment with alternating current stimulation(e.g.,30 minutes,daily for 10 days using transorbital electrodes and^10 Hz)activates the entire retina and parts of the brain.Electroencephalography and functional magnetic resonance imaging studies revealed local activation of the visual cortex,global reorganization of functional brain networks,and enhanced blood flow,which together activate neurons and their networks.The future of low vision is optimistic because vision loss is indeed,partially reversible.
文摘This paper presents a method for structured scene modeling using micro stereo vision system with large field of view. The proposed algorithm includes edge detection with Canny detector, line fitting with principle axis based approach, finding corresponding lines using feature based matching method, and 3D line depth computation.
文摘In this study, we develop a mixed reality game system to investigate characteristics ofjudgrnents of individual players in an evacuation process. The characteristics of judgments of the players that are inferred from the performance of the game are then incorporated into a multi-agent simulation as rules. The behavior of evacuees is evaluated in approximations of real situations, by using the agent simulation including different judgments of evacuees. Using the results of the simulation, effective methods are discussed for achieving the escape of the evacuees within a short time.
文摘单目三维视觉测量在视觉测量领域具有低成本、简便性、结构紧凑等优势,是以智能化、网络化制造为特征的先进制造典型技术之一。经过不断发展,单目三维视觉测量技术已成功应用于无人机导航、智能机器人、工业检测、医疗健康等领域,如今呈现出精准化、快捷化、微型化、自动化、动态化等发展趋势。以孔径数量为标准,将单目三维视觉测量技术分为单孔径及多孔径两大类,分别综述两类方法的研究现状和发展历程,重点论述了应用较广的运动恢复结构法(Structure From Motion,SFM)和光场三维测量方法,并对单目三维视觉测量技术的未来方向进行了展望。
文摘为解决光线遮蔽、藻萍干扰以及稻叶尖形状相似等复杂环境导致稻田杂草识别效果不理想问题,该研究提出一种基于组合深度学习的杂草识别方法。引入MSRCP(multi-scale retinex with color preservation)对图像进行增强,以提高图像亮度及对比度;加入ViT分类网络去除干扰背景,以提高模型在复杂环境下对小目标杂草的识别性能。在YOLOv7模型中主干特征提取网络替换为GhostNet网络,并引入CA注意力机制,以增强主干特征提取网络对杂草特征提取能力及简化模型参数计算量。消融试验表明:改进后的YOLOv7模型平均精度均值为88.2%,较原YOLOv7模型提高了3.3个百分点,参数量减少10.43 M,计算量减少66.54×109次/s。识别前先经过MSRCP图像增强后,与原模型相比,改进YOLOv7模型的平均精度均值提高了2.6个百分点,光线遮蔽、藻萍干扰以及稻叶尖形状相似的复杂环境下平均精度均值分别提高5.3、3.6、3.1个百分点,加入ViT分类网络后,较原模型平均精度均值整体提升了4.4个百分点,光线遮蔽、藻萍干扰一级稻叶尖形状相似的复杂环境下的平均精度均值较原模型整体提升了6.2、6.1、5.7个百分点。ViT-改进YOLOv7模型的平均精度均值为92.6%,相比于YOLOv5s、YOLOXs、MobilenetV3-YOLOv7、YOLOv8和改进YOLOv7分别提高了11.6、10.1、5.0、4.2、4.4个百分点。研究结果可为稻田复杂环境的杂草精准识别提供支撑。
文摘In recent years,self-supervised learning which does not require a large number of manual labels generate supervised signals through the data itself to attain the characterization learning of samples.Self-supervised learning solves the problem of learning semantic features from unlabeled data,and realizes pre-training of models in large data sets.Its significant advantages have been extensively studied by scholars in recent years.There are usually three types of self-supervised learning:"Generative,Contrastive,and GeneTative-Contrastive."The model of the comparative learning method is relatively simple,and the performance of the current downstream task is comparable to that of the supervised learning method.Therefore,we propose a conceptual analysis framework:data augmentation pipeline,architectures,pretext tasks,comparison methods,semisupervised fine-tuning.Based on this conceptual framework,we qualitatively analyze the existing comparative self-supervised learning methods for computer vision,and then further analyze its performance at different stages,and finally summarize the research status of sei supervised comparative learning methods in other fields.
