居住在美国的每个妇女在8次发育侵袭性乳腺癌的可能性下有大约1。有丝分裂细胞计数是评估乳腺癌侵袭性或等级最常见的测试之一。在该预后,必须通过病理学家使用高分辨率显微镜检查组织病理学图像以计算细胞。不幸的是,可以是一种完整的任务,可重复性差,特别是对于非专家来说。最近深入学习网络适用于能够自动定位这些感兴趣区域的医学应用。然而,这些基于区域的网络缺乏利用通常用作唯一检测方法的完整图像CNN产生的分割特征的能力。因此,所提出的方法利用更快的RCNN进行对象检测,同时使用RGB图像特征的UNET产生的分割特征,以实现在Mitos-Atypia 2014分数上的F分数为0.508,计数数据集,优于最先进的攻击方法。
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Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.
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组织学图像中核和腺体的实例分割是用于癌症诊断,治疗计划和生存分析的计算病理学工作流程中的重要一步。随着现代硬件的出现,大规模质量公共数据集的最新可用性以及社区组织的宏伟挑战已经看到了自动化方法的激增,重点是特定领域的挑战,这对于技术进步和临床翻译至关重要。在这项调查中,深入分析了过去五年(2017-2022)中发表的原子核和腺体实例细分的126篇论文,进行了深入分析,讨论了当前方法的局限性和公开挑战。此外,提出了潜在的未来研究方向,并总结了最先进方法的贡献。此外,还提供了有关公开可用数据集的概括摘要以及关于说明每种挑战的最佳性能方法的巨大挑战的详细见解。此外,我们旨在使读者现有研究的现状和指针在未来的发展方向上开发可用于临床实践的方法,从而可以改善诊断,分级,预后和癌症的治疗计划。据我们所知,以前没有工作回顾了朝向这一方向的组织学图像中的实例细分。
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We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We introduce Deep Sliding Shapes, a 3D ConvNet formulation that takes a 3D volumetric scene from a RGB-D image as input and outputs 3D object bounding boxes. In our approach, we propose the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D. In particular, we handle objects of various sizes by training an amodal RPN at two different scales and an ORN to regress 3D bounding boxes. Experiments show that our algorithm outperforms the state-of-the-art by 13.8 in mAP and is 200× faster than the original Sliding Shapes. Source code and pre-trained models are available.
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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.
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State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with "attention" mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.
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近年来,使用计算机的运动捕捉技术迅速发展。由于其高效率和优异的性能,它取代了许多传统方法,并且广泛用于许多领域。我们的项目是关于街景视频人体运动捕获和分析。该项目的主要目标是在视频中捕获人类运动,并实时使用3D动画(人类)的运动信息。我们应用了一个神经网络进行运动捕获,并在街景场景下的团结中实现。通过分析运动数据,我们将更好地估计街道状况,这对于自动驾驶汽车等其他高科技应用有用。
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Fast R-CNN
Ross Girshick
分类:
2015-04-30
This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9× faster than R-CNN, is 213× faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3× faster, tests 10× faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https: //github.com/rbgirshick/fast-rcnn.
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自动检测武器对于改善个人的安全性和福祉是重要的,仍然是由于各种尺寸,武器形状和外观,这是一项艰巨的任务。查看点变化和遮挡也是使这项任务更加困难的原因。此外,目前的物体检测算法处理矩形区域,但是一个细长和长的步枪可以真正地覆盖区域的一部分区域,其余部分可能包含未经紧的细节。为了克服这些问题,我们提出了一种用于定向意识武器检测的CNN架构,其提供具有改进的武器检测性能的面向边界框。所提出的模型不仅通过将角度作为分类问题的角度分成8个类而且提供方向,而是作为回归问题。对于培训我们的武器检测模型,包括总6400件武器图像的新数据集从网上收集,然后用面向定向的边界框手动注释。我们的数据集不仅提供导向的边界框作为地面真相,还提供了水平边界框。我们还以多种现代对象探测器提供我们的数据集,用于在该领域进一步研究。所提出的模型在该数据集上进行评估,并且与搁板对象检测器的比较分析产生了卓越的拟议模型的性能,以标准评估策略测量。数据集和模型实现在此链接上公开可用:https://bit.ly/2tyzicf。
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Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia. A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternative to reduce the limitations of traditional methods. However, the main problem of the semantic segmentation technique is raised since large parasites are dominant, and the tiny parasites are suppressed. In addition, the amount and variance of data are important influences in establishing their models. In this study, we conduct two contributions. First, we collect 559 microscopic images containing 691 malaria parasites of thin blood smears. The dataset is named PlasmoID, and most data comes from rural Indonesia. PlasmoID also provides ground truth for parasite detection and segmentation purposes. Second, this study proposes a malaria parasite segmentation and detection scheme by combining Faster RCNN and a semantic segmentation technique. The proposed scheme has been evaluated on the PlasmoID dataset. It has been compared with recent studies of semantic segmentation techniques, namely UNet, ResFCN-18, DeepLabV3, DeepLabV3plus and ResUNet-18. The result shows that our proposed scheme can improve the segmentation and detection of malaria parasite performance compared to original semantic segmentation techniques.
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由于缺乏自动注释系统,大多数发展城市的城市机构都是数字未标记的。因此,在此类城市中,位置和轨迹服务(例如Google Maps,Uber等)仍然不足。自然场景图像中的准确招牌检测是从此类城市街道检索无错误的信息的最重要任务。然而,开发准确的招牌本地化系统仍然是尚未解决的挑战,因为它的外观包括文本图像和令人困惑的背景。我们提出了一种新型的对象检测方法,该方法可以自动检测招牌,适合此类城市。我们通过合并两种专业预处理方法和一种运行时效高参数值选择算法来使用更快的基于R-CNN的定位。我们采用了一种增量方法,通过使用我们构造的SVSO(Street View Signboard对象)签名板数据集,通过详细评估和与基线进行比较,以达到最终提出的方法,这些方法包含六个发展中国家的自然场景图像。我们在SVSO数据集和Open Image数据集上展示了我们提出的方法的最新性能。我们提出的方法可以准确地检测招牌(即使图像包含多种形状和颜色的多种嘈杂背景的招牌)在SVSO独立测试集上达到0.90 MAP(平均平均精度)得分。我们的实施可在以下网址获得:https://github.com/sadrultoaha/signboard-detection
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Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-consuming and inefficient. With the development of deep learning methods, some models with good performance have emerged, but the generalization ability should be further strengthened. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation performance with a high recall rate is achieved by the proposed depthwise separable convolution residual block and channel-spatial attention gate. Then, a classification network is cascaded to further improve the detection performance of mitosis nuclei. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained compared with the current state-of-the-art algorithms. In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper. The code will be available at: https://github.com/antifen/mitosis-nuclei-segmentation.
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通过丘陵形成的现场制备是一种常用的造林治疗,通过机械地创建称为丘的植物植物物质来改善树木生长条件。在现场准备之后,下一个关键步骤是计算土墩的数量,该堆积的数量为森林经理提供了对给定种植园块所需的幼苗数量的精确估计。计算土墩数量通常是通过林业工人的手动现场调查来进行的,林业工人昂贵且容易出错,尤其是在大面积地区。为了解决这个问题,我们提出了一个新颖的框架,利用无人机成像和计算机视觉的进步,以准确估计种植块上的土墩数量。提出的框架包括两个主要组件。首先,我们利用基于深度学习算法的视觉识别方法来通过基于像素的分割来进行多个对象检测。这使得可见的土墩以及其他经常看到的物体(例如树木,碎屑,水的积累)的初步计数可用于表征种植块。其次,由于视觉识别可能会受到几个扰动因子(例如丘陵侵蚀,遮挡)的限制,因此我们采用机器学习估计功能,该功能可预测基于第一阶段提取的局部块属性的最终数量。我们在新的无人机数据集上评估了所提出的框架,该数据集代表具有不同功能的众多种植块。所提出的方法在相对计数精度方面优于手动计数方法,表明它在困难情况下具有有利和有效的潜力。
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Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.
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深神网络的对象探测器正在不断发展,并用于多种应用程序,每个应用程序都有自己的要求集。尽管关键安全应用需要高准确性和可靠性,但低延迟任务需要资源和节能网络。不断提出了实时探测器,在高影响现实世界中是必需的,但是它们过分强调了准确性和速度的提高,而其他功能(例如多功能性,鲁棒性,资源和能源效率)则被省略。现有网络的参考基准不存在,设计新网络的标准评估指南也不存在,从而导致比较模棱两可和不一致的比较。因此,我们对广泛的数据集进行了多个实时探测器(基于锚点,关键器和变压器)的全面研究,并报告了一系列广泛指标的结果。我们还研究了变量,例如图像大小,锚固尺寸,置信阈值和架构层对整体性能的影响。我们分析了检测网络的鲁棒性,以防止分配变化,自然腐败和对抗性攻击。此外,我们提供了校准分析来评估预测的可靠性。最后,为了强调现实世界的影响,我们对自动驾驶和医疗保健应用进行了两个独特的案例研究。为了进一步衡量关键实时应用程序中网络的能力,我们报告了在Edge设备上部署检测网络后的性能。我们广泛的实证研究可以作为工业界对现有网络做出明智选择的指南。我们还希望激发研究社区的设计和评估网络的新方向,该网络着重于更大而整体的概述,以实现深远的影响。
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海洋生态系统及其鱼类栖息地越来越重要,因为它们在提供有价值的食物来源和保护效果方面的重要作用。由于它们的偏僻且难以接近自然,因此通常使用水下摄像头对海洋环境和鱼类栖息地进行监测。这些相机产生了大量数字数据,这些数据无法通过当前的手动处理方法有效地分析,这些方法涉及人类观察者。 DL是一种尖端的AI技术,在分析视觉数据时表现出了前所未有的性能。尽管它应用于无数领域,但仍在探索其在水下鱼类栖息地监测中的使用。在本文中,我们提供了一个涵盖DL的关键概念的教程,该教程可帮助读者了解对DL的工作原理的高级理解。该教程还解释了一个逐步的程序,讲述了如何为诸如水下鱼类监测等挑战性应用开发DL算法。此外,我们还提供了针对鱼类栖息地监测的关键深度学习技术的全面调查,包括分类,计数,定位和细分。此外,我们对水下鱼类数据集进行了公开调查,并比较水下鱼类监测域中的各种DL技术。我们还讨论了鱼类栖息地加工深度学习的新兴领域的一些挑战和机遇。本文是为了作为希望掌握对DL的高级了解,通过遵循我们的分步教程而为其应用开发的海洋科学家的教程,并了解如何发展其研究,以促进他们的研究。努力。同时,它适用于希望调查基于DL的最先进方法的计算机科学家,以进行鱼类栖息地监测。
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Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.
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Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.
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我们介绍了一种名为RobustAbnet的新表检测和结构识别方法,以检测表的边界并从异质文档图像中重建每个表的细胞结构。为了进行表检测,我们建议将Cornernet用作新的区域建议网络来生成更高质量的表建议,以更快的R-CNN,这显着提高了更快的R-CNN的定位准确性以进行表检测。因此,我们的表检测方法仅使用轻巧的RESNET-18骨干网络,在三个公共表检测基准(即CTDAR TRACKA,PUBLAYNET和IIIT-AR-13K)上实现最新性能。此外,我们提出了一种新的基于分裂和合并的表结构识别方法,其中提出了一个新型的基于CNN的新空间CNN分离线预测模块将每个检测到的表分为单元格,并且基于网格CNN的CNN合并模块是应用用于恢复生成细胞。由于空间CNN模块可以有效地在整个表图像上传播上下文信息,因此我们的表结构识别器可以坚固地识别具有较大的空白空间和几何扭曲(甚至弯曲)表的表。得益于这两种技术,我们的表结构识别方法在包括SCITSR,PubTabnet和CTDAR TrackB2-Modern在内的三个公共基准上实现了最先进的性能。此外,我们进一步证明了我们方法在识别具有复杂结构,大空间以及几何扭曲甚至弯曲形状的表上的表格上的优势。
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人类生理学中的各种结构遵循特异性形态,通常在非常细的尺度上表达复杂性。这种结构的例子是胸前气道,视网膜血管和肝血管。可以观察到可以观察到可以观察到可以观察到可以观察到空间排列的磁共振成像(MRI),计算机断层扫描(CT),光学相干断层扫描(OCT)等医学成像模式(MRI),计算机断层扫描(CT),可以观察到空间排列的大量2D和3D图像的集合。这些结构在医学成像中的分割非常重要,因为对结构的分析提供了对疾病诊断,治疗计划和预后的见解。放射科医生手动标记广泛的数据通常是耗时且容易出错的。结果,在过去的二十年中,自动化或半自动化的计算模型已成为医学成像的流行研究领域,迄今为止,许多计算模型已经开发出来。在这项调查中,我们旨在对当前公开可用的数据集,细分算法和评估指标进行全面审查。此外,讨论了当前的挑战和未来的研究方向。
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