在深海勘探领域,声纳目前是唯一有效的长距离传感装置。复杂的水下环境,如噪声干扰,低目标强度或背景动态,对声纳成像带来了许多负面影响。其中,非线性强度的问题非常普遍。它也被称为声学传感器成像的各向异性,即当自主水下车辆(AUV)携带声纳从不同角度检测到相同的目标时,图像对之间的强度变化有时非常大,这使得传统匹配算法成为了传统的匹配算法几乎无效。但是,图像匹配是诸如导航,定位和映射等综合任务的基础。因此,获得稳健和准确的匹配结果是非常有价值的。本文提出了一种基于相位信息和深卷积特征的组合匹配方法。它具有两个出色的优势:一个是深度卷积特征可用于衡量声纳图像的本地和全球位置的相似性;另一种是可以在声纳图像的关键目标位置执行本地特征匹配。该方法不需要复杂的手动设计,并以关闭端到端的方式完成非线性强度声纳图像的匹配任务。特征匹配实验在AUV捕获的深海声纳图像上进行,结果表明我们的提议具有卓越的匹配精度和鲁棒性。
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在深海勘探领域,声纳目前是唯一有效的长距离传感装置。复杂的水下环境,如噪声干扰,低目标强度或背景动态,对声纳成像带来了许多负面影响。其中,非线性强度的问题非常普遍。它也被称为声学成像的各向异性,即,当AUV携带声纳从不同角度检测到相同的目标时,图像对之间的强度差值有时非常大,这使得传统的匹配算法几乎无效。但是,图像匹配是诸如导航,定位和映射等综合任务的基础。因此,获得稳健和准确的匹配结果是非常有价值的。本文提出了一种基于相位信息和深卷积特征的组合匹配方法。它有两个出色的优势:一个是,可以使用深度卷积功能来衡量声纳图像的本地和全球位置的相似性;另一种是可以在声纳图像的关键目标位置执行本地特征匹配。该方法不需要复杂的手动设计,并以关闭端到端的方式完成非线性强度声纳图像的匹配任务。特征匹配实验在AUV捕获的深海声纳图像上进行,结果表明我们的建议具有良好的匹配准确性和鲁棒性。
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Automatically identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method, named R2FD2, that is robust to radiation and rotation differences.Our R2FD2 is conducted in two critical contributions, consisting of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor, which consists of two components: fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to depict a more discriminative feature representation, which improves RMLGs resistance to radiation and rotation variances.
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兴趣点检测是计算机视觉和图像处理中最根本,最关键的问题之一。在本文中,我们对图像特征信息(IFI)提取技术进行了全面综述,以进行利益点检测。为了系统地介绍现有的兴趣点检测方法如何从输入图像中提取IFI,我们提出了IFI提取技术的分类学检测。根据该分类法,我们讨论了不同类型的IFI提取技术以进行兴趣点检测。此外,我们确定了与现有的IFI提取技术有关的主要未解决的问题,以及以前尚未讨论过的任何兴趣点检测方法。提供了现有的流行数据集和评估标准,并评估和讨论了18种最先进方法的性能。此外,还详细阐述了有关IFI提取技术的未来研究方向。
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作为一种引起巨大关注的新兴技术,通过分析继电器表面上的漫反射来重建隐藏物体的非视线(NLOS)成像,具有广泛的应用前景,在自主驾驶,医学成像和医学成像领域防御。尽管信噪比低(SNR)和高不良效率的挑战,但近年来,NLOS成像已迅速发展。大多数当前的NLOS成像技术使用传统的物理模型,通过主动或被动照明构建成像模型,并使用重建算法来恢复隐藏场景。此外,NLOS成像的深度学习算法最近也得到了很多关注。本文介绍了常规和深度学习的NLOS成像技术的全面概述。此外,我们还调查了新的拟议的NLOS场景,并讨论了现有技术的挑战和前景。这样的调查可以帮助读者概述不同类型的NLOS成像,从而加速了在角落周围看到的发展。
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在许多临床应用中,内窥镜图像之间的特征匹配和查找对应关系是从临床序列中进行快速异常定位的许多临床应用中的关键步骤。尽管如此,由于内窥镜图像中存在较高的纹理可变性,稳健和准确的特征匹配的发展成为一项具有挑战性的任务。最近,通过卷积神经网络(CNN)提取的深度学习技术已在各种计算机视觉任务中获得了吸引力。但是,他们都遵循一个有监督的学习计划,其中需要大量注释的数据才能达到良好的性能,这通常不总是可用于医疗数据数据库。为了克服与标记的数据稀缺性有关的限制,自我监督的学习范式最近在许多应用程序中表现出了巨大的成功。本文提出了一种基于深度学习技术的内窥镜图像匹配的新型自我监督方法。与标准手工制作的本地功能描述符相比,我们的方法在精度和召回方面优于它们。此外,与选择基于精度和匹配分数的基于最先进的基于深度学习的监督方法相比,我们的自我监管的描述符提供了竞争性能。
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Visual perception plays an important role in autonomous driving. One of the primary tasks is object detection and identification. Since the vision sensor is rich in color and texture information, it can quickly and accurately identify various road information. The commonly used technique is based on extracting and calculating various features of the image. The recent development of deep learning-based method has better reliability and processing speed and has a greater advantage in recognizing complex elements. For depth estimation, vision sensor is also used for ranging due to their small size and low cost. Monocular camera uses image data from a single viewpoint as input to estimate object depth. In contrast, stereo vision is based on parallax and matching feature points of different views, and the application of deep learning also further improves the accuracy. In addition, Simultaneous Location and Mapping (SLAM) can establish a model of the road environment, thus helping the vehicle perceive the surrounding environment and complete the tasks. In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RDBG sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.
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Underwater automatic target recognition (UATR) has been a challenging research topic in ocean engineering. Although deep learning brings opportunities for target recognition on land and in the air, underwater target recognition techniques based on deep learning have lagged due to sensor performance and the size of trainable data. This letter proposed a framework for learning the visual representation of underwater acoustic imageries, which takes a transformer-based style transfer model as the main body. It could replace the low-level texture features of optical images with the visual features of underwater acoustic imageries while preserving their raw high-level semantic content. The proposed framework could fully use the rich optical image dataset to generate a pseudo-acoustic image dataset and use it as the initial sample to train the underwater acoustic target recognition model. The experiments select the dual-frequency identification sonar (DIDSON) as the underwater acoustic data source and also take fish, the most common marine creature, as the research subject. Experimental results show that the proposed method could generate high-quality and high-fidelity pseudo-acoustic samples, achieve the purpose of acoustic data enhancement and provide support for the underwater acoustic-optical images domain transfer research.
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本文的目的是通过提出一种新颖的方案,提高图像取证中复制伪造检测(CMFD)的准确性,主要贡献正在不断发展循环域覆盖(ECDC)算法。该方案集成了基于块和基于关键点的伪造检测方法。首先,从整个图像中提取逻辑极性空间和比例不变特征变换(SIFT)中的加速强大功能(SURF)。其次,采用广义2最近邻(G2NN)来获得大规模匹配的对。然后,采用随机样本共识(RANSAC)算法来滤除不匹配的对,从而允许粗略地位伪造区域。要更准确地展示这些伪造地区,我们提出了高效准确的ECDC算法呈现它们。该算法可以通过从联合演化的圆形域中提取块特征来找到满意的阈值区域,这些域在匹配对上以匹配的对。最后,应用形态学操作来优化检测到的伪造区域。实验结果表明,与其他最先进的CMFD方案相比,所提出的CMFD方案可以在各种攻击下实现更好的检测性能。
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We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description. While previous works have successfully tackled each one of these problems individually, we show how to learn to do all three in a unified manner while preserving end-to-end differentiability. We then demonstrate that our Deep pipeline outperforms state-of-the-art methods on a number of benchmark datasets, without the need of retraining.
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Sparse local feature extraction is usually believed to be of important significance in typical vision tasks such as simultaneous localization and mapping, image matching and 3D reconstruction. At present, it still has some deficiencies needing further improvement, mainly including the discrimination power of extracted local descriptors, the localization accuracy of detected keypoints, and the efficiency of local feature learning. This paper focuses on promoting the currently popular sparse local feature learning with camera pose supervision. Therefore, it pertinently proposes a Shared Coupling-bridge scheme with four light-weight yet effective improvements for weakly-supervised local feature (SCFeat) learning. It mainly contains: i) the \emph{Feature-Fusion-ResUNet Backbone} (F2R-Backbone) for local descriptors learning, ii) a shared coupling-bridge normalization to improve the decoupling training of description network and detection network, iii) an improved detection network with peakiness measurement to detect keypoints and iv) the fundamental matrix error as a reward factor to further optimize feature detection training. Extensive experiments prove that our SCFeat improvement is effective. It could often obtain a state-of-the-art performance on classic image matching and visual localization. In terms of 3D reconstruction, it could still achieve competitive results. For sharing and communication, our source codes are available at https://github.com/sunjiayuanro/SCFeat.git.
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尽管提取了通过手工制作和基于学习的描述符实现的本地特征的进步,但它们仍然受到不符合非刚性转换的不变性的限制。在本文中,我们提出了一种计算来自静止图像的特征的新方法,该特征对于非刚性变形稳健,以避免匹配可变形表面和物体的问题。我们的变形感知当地描述符,命名优惠,利用极性采样和空间变压器翘曲,以提供旋转,尺度和图像变形的不变性。我们通过将等距非刚性变形应用于模拟环境中的对象作为指导来提供高度辨别的本地特征来培训模型架构端到端。该实验表明,我们的方法优于静止图像中的实际和现实合成可变形对象的不同数据集中的最先进的手工制作,基于学习的图像和RGB-D描述符。描述符的源代码和培训模型在https://www.verlab.dcc.ufmg.br/descriptors/neUrips2021上公开可用。
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视觉同时定位和映射(VSLAM)在计算机视觉和机器人社区中取得了巨大进展,并已成功用于许多领域,例如自主机器人导航和AR/VR。但是,VSLAM无法在动态和复杂的环境中实现良好的定位。许多出版物报告说,通过与VSLAM结合语义信息,语义VSLAM系统具有近年来解决上述问题的能力。然而,尚无关于语义VSLAM的全面调查。为了填补空白,本文首先回顾了语义VSLAM的发展,并明确着眼于其优势和差异。其次,我们探讨了语义VSLAM的三个主要问题:语义信息的提取和关联,语义信息的应用以及语义VSLAM的优势。然后,我们收集和分析已广泛用于语义VSLAM系统的当前最新SLAM数据集。最后,我们讨论未来的方向,该方向将为语义VSLAM的未来发展提供蓝图。
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位置识别是可以协助同时定位和映射(SLAM)进行循环闭合检测和重新定位以进行长期导航的基本模块。在过去的20美元中,该地点认可社区取得了惊人的进步,这吸引了在计算机视觉和机器人技术等多个领域的广泛研究兴趣和应用。但是,在复杂的现实世界情景中,很少有方法显示出有希望的位置识别性能,在复杂的现实世界中,长期和大规模的外观变化通常会导致故障。此外,在最先进的方法之间缺乏集成框架,可以应对所有挑战,包括外观变化,观点差异,对未知区域的稳健性以及现实世界中的效率申请。在这项工作中,我们调查针对长期本地化并讨论未来方向和机会的最先进方法。首先,我们研究了长期自主权中的位置识别以及在现实环境中面临的主要挑战。然后,我们回顾了最新的作品,以应对各种位置识别挑战的不同传感器方式和当前的策略的认可。最后,我们回顾了现有的数据集以进行长期本地化,并为不同的方法介绍了我们的数据集和评估API。本文可以成为该地点识别界新手的研究人员以及关心长期机器人自主权的研究人员。我们还对机器人技术中的常见问题提供了意见:机器人是否需要准确的本地化来实现长期自治?这项工作以及我们的数据集和评估API的摘要可向机器人社区公开,网址为:https://github.com/metaslam/gprs。
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快速移动对象的检测和跟踪在许多领域都具有广泛的实用性。但是,由于复杂的计算和有限的数据处理能力,使用基于图像的技术满足快速有效检测和跟踪的这种需求是有问题的。为了解决这个问题,我们提出了一种无图像的方法,以实现快速移动对象的实时检测和跟踪。它采用Hadamard模式通过空间光调节器来照亮快速移动对象,其中单像素检测器收集所得的光信号。单像素测量值直接用于无需图像重建而无需重建位置信息。此外,一种新的采样方法用于优化实现超低采样率的模式投影方法。与最先进的方法相比,我们的方法不仅能够处理实时检测和跟踪,而且还具有少量计算和高效率。我们在实验上证明,使用22kHz数字微型摩尔设备的提出方法可以在跟踪时以1.28%的采样速率实现105FPS帧速率。我们的方法突破了传统的跟踪方式,可以在无图像重建的情况下实现对象实时跟踪。
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近年来,卷积神经网络(CNN)在合成孔径雷达(SAR)目标识别方面表现出巨大的潜力。 SAR图像具有强烈的粒度感,并且具有不同的纹理特征,例如斑点噪声,目标优势散射器和目标轮廓,这些轮廓很少在传统的CNN模型中被考虑。本文提出了两个残留块,即具有多尺度接收场(RFS)的EMC2A块,基于多型结构,然后设计了有效的同位素体系结构深CNN(DCNN),EMC2A-net。 EMC2A阻止使用不同的扩张速率利用平行的扩张卷积,这可以有效地捕获多尺度上下文特征而不会显着增加计算负担。为了进一步提高多尺度功能融合的效率,本文提出了多尺度特征跨通道注意模块,即EMC2A模块,采用了局部的多尺度特征交互策略,而无需降低维度。该策略通过有效的一维(1D) - 圆形卷积和Sigmoid函数适应每个通道的权重,以指导全球通道明智的关注。 MSTAR数据集上的比较结果表明,EMC2A-NET优于相同类型的现有模型,并且具有相对轻巧的网络结构。消融实验结果表明,仅使用一些参数和适当的跨渠道相互作用,EMC2A模块可显着提高模型的性能。
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Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' interest in recent years. The current multi-modal fusion methods mainly focus on camera data and LiDAR data, but pay little attention to the kinematic information provided by the bottom sensors of the vehicle, such as acceleration, vehicle speed, angle of rotation. These information are not affected by complex external scenes, so it is more robust and reliable. In this paper, we introduce the existing application fields of vehicle bottom information and the research progress of related methods, as well as the multi-modal fusion methods based on bottom information. We also introduced the relevant information of the vehicle bottom information data set in detail to facilitate the research as soon as possible. In addition, new future ideas of multi-modal fusion technology for autonomous driving tasks are proposed to promote the further utilization of vehicle bottom information.
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信号处理是几乎任何传感器系统的基本组件,具有不同科学学科的广泛应用。时间序列数据,图像和视频序列包括可以增强和分析信息提取和量化的代表性形式的信号。人工智能和机器学习的最近进步正在转向智能,数据驱动,信号处理的研究。该路线图呈现了最先进的方法和应用程序的关键概述,旨在突出未来的挑战和对下一代测量系统的研究机会。它涵盖了广泛的主题,从基础到工业研究,以简明的主题部分组织,反映了每个研究领域的当前和未来发展的趋势和影响。此外,它为研究人员和资助机构提供了识别新前景的指导。
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多光谱和多模式图像处理在计算机视觉和计算摄影社区中很重要。由于所获得的多级和多模式数据通常由于图像设备的交替或移动而被误报,因此需要图像登记过程。由于非线性强度和梯度变化,多光谱或多模式图像的登记是具有挑战性的。为了应对这一挑战,我们提出了相变网络(PCNet),该网络(PCNET)能够增强结构相似性并减轻非线性强度和梯度变化。然后可以使用网络产生的相似性增强特征对齐图像。 PCNET在先前的相一致性的指导下构建。根据相中理论,网络包含三个可用的学习Gabor内核附带的三层可训练层。由于先前的知识,PCNet非常轻量。可以将PCNET视为完全卷积的,因此可以取消任意尺寸。一旦接受训练,PCNet适用于各种多级和多模式数据,如RGB / NIR和Flash / No-Flash图像,而无需额外的进一步调整。实验结果验证了PCNet优于当前最先进的登记算法。
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In this paper, we present a novel scale-and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.
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