ICCV_2015_Thin Structure Estimation with Curvature Regularization

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《稀疏角CT重建的算法研究》范文

《稀疏角CT重建的算法研究》篇一一、引言随着医学影像技术的快速发展，计算机断层扫描（Computed Tomography, CT）技术已经成为临床诊断的重要手段之一。

然而，传统的CT重建算法在处理某些复杂或稀疏的图像时，常常会遇到重建效果不佳、噪声大、分辨率低等问题。

稀疏角CT重建算法作为一种新兴的重建技术，能够有效地解决这些问题，提高CT图像的重建质量和诊断准确性。

本文旨在研究稀疏角CT重建算法的原理、方法及实际应用，为医学影像技术的发展提供理论支持和实践指导。

二、稀疏角CT重建算法概述稀疏角CT重建算法是一种基于稀疏表示理论的CT重建方法。

该算法通过对图像中的边缘和结构信息进行稀疏表示，有效地解决在复杂或稀疏的CT图像中出现的重建问题。

其主要思想是利用图像的稀疏性特点，将图像中的边缘和结构信息提取出来，并通过优化算法进行重建。

三、稀疏角CT重建算法原理稀疏角CT重建算法主要包括以下几个步骤：首先，通过一定的预处理技术对原始的CT数据进行预处理，以消除噪声和干扰；其次，利用稀疏表示理论对图像中的边缘和结构信息进行提取和表示；然后，通过优化算法对提取的边缘和结构信息进行优化和重建；最后，通过后处理技术对重建后的图像进行平滑和增强，以提高图像的质量和诊断准确性。

四、稀疏角CT重建算法方法目前，稀疏角CT重建算法主要包括基于L1范数的最小绝对收缩和选择算子（LASSO）算法、基于压缩感知的稀疏重建算法等。

这些算法都基于图像的稀疏性特点，通过优化算法对图像中的边缘和结构信息进行提取和重建。

在实际应用中，还需要根据具体的CT数据和需求选择合适的算法进行重建。

五、稀疏角CT重建算法的应用稀疏角CT重建算法在医学影像领域具有广泛的应用前景。

首先，它可以用于提高CT图像的分辨率和清晰度，从而帮助医生更准确地诊断疾病；其次，它还可以用于对复杂的组织和器官进行精确的三维重建和可视化；此外，它还可以用于对一些特殊的医学问题进行研究和探索。

行人再识别研究取得进展

龙源期刊网行人再识别研究取得进展作者：杨智来源：《科学》2016年第05期[本刊讯]清华大学王生进教授课题组在有关行人再识别的研究中取得引人注目进展，该项成果分别在2014年度欧洲计算机视觉国际会议、2015年度国际电气和电子工程师协会（IEEE）的计算机视觉大会（ICCV）上发表。

行人再识别（person re-identification）是一种判断所关注的某个特定行人在多个监测摄像头的哪个摄像头中出现过的新技术，以此获得行人的行走轨迹。

它通过特征提取及与模板数据库比对的专门操作，实现对目标行人之识别。

但是，这项技术仍未解决由于照明、视角、姿势、方位、摄像头设置上诸多变化而造成的匹配困难问题。

课题组在既有探索基础上融合了时空特征，并给行人再识别研究引进词袋表征（bag-of-wordrepresentation），即建立量化的图像特征关键词集合，获得性能领先的实验结果；还构建了一个本研究领域中迄今为止最大的行人再识别数据集“市场-1501”（Market-1501），使行人再识别达到快速准确的水平。

他们的研究结果表明，在两个典型行人图像数据集上，行人再识别的准确率分别高于当时已有最好方法的4.4%和13.7%；在大规模行人再识别实验中，对目标行人的检索时间从400秒降低到1秒，检索效率比现有算法提高了2个数量级。

2016年，王生进研究组又在国际权威期刊《IEEE模式分析与机器智能会刊》（IEEE PAMI）上发表了最新的行人再识别研究成果。

在两个典型的行人序列数据集上，基于时空信息的行人再识别准确率分别比现有最好方法提高5.7%和16.3%。

此研究有助于实现公共场所安全监控等的智能化，对平安城市建设等具有良好应用潜力，受到国家自然科学基金等项目的资助。

人眼数据采集方法

• Smith B A, Yin Q, Feiner S, et al. Gaze locking: passive eye contact detection for human-object interaction[C]. User Interface Software and Technology, 2013: 271-280.
EYEDIAP (ETRA 2014)
• 采集工具：深度相机Kinect + RGB相机 • 采集方法：志愿者坐在深度相机前，要求眼睛一直盯着运动的乒
乓球，同时用RGB相机记录这一过程。在采集到的视频中人工标注眼睛中心点与乒乓球的2D坐标，映射到点云中得到对应的三维坐标，做差得到三维视线向量 • 规模：94段视频，16位不同人种的样本 • 适用场景：视线估计 • 局限性：需要深度摄像头，数据量较少
合成数据集
• 采集工具：手动合成或自动合成 • SynthesEyes (ICCV 2015) • UnityEyes (ETRA 2016)
• 直接提供了自动生成工具 • 使用Unity引擎制作 • 可以自定义视线、头部姿态等
• SimGAN (CVPR 2017) 用GAN做视线迁移 • Unsupervised Representation Learning (CVPR 2020) 视线重定向
• RT-GENE（眼动仪+深度+RGB） • 视线追踪
• SynthesEyes（合成）
• GazeFollow
• UnityEyes（合成）
• VideoGaze
MPIIGaze (CVPR 2015)
• 采集工具：参数已知的单个RGB相机 • 采集方法：利用相机参数和镜面算法计算并校准人眼的3D位置，

《稀疏角CT重建的算法研究》范文

《稀疏角CT重建的算法研究》篇一一、引言计算机断层扫描（Computed Tomography, CT）技术是现代医学影像诊断的重要手段之一。

CT技术的核心是图像重建算法，它可以将采集到的投影数据通过特定的算法转换为二维或三维的断层图像。

随着科技的发展，CT技术的广泛应用，对其重建算法的精度和效率要求也越来越高。

其中，稀疏角CT重建算法作为一种重要的重建方法，因其能够提高图像的分辨率和减少伪影等优点，受到了广泛关注。

本文将对稀疏角CT重建的算法进行深入研究。

二、稀疏角CT重建的基本原理稀疏角CT重建算法的基本原理是通过获取不同角度下的投影数据，利用特定的重建算法进行图像重建。

相较于传统的CT 重建算法，稀疏角CT重建算法在数据采集和图像重建过程中采用了更少的投影角度，从而提高了图像的分辨率和减少了伪影。

三、稀疏角CT重建的算法研究1. 迭代重建算法迭代重建算法是稀疏角CT重建中常用的一种算法。

该算法通过不断迭代更新图像的估计值，使得估计值与实际投影数据之间的差异逐渐减小。

在迭代过程中，可以通过引入稀疏约束等优化手段，进一步提高图像的分辨率和减少伪影。

目前，迭代重建算法已成为稀疏角CT重建的主流算法之一。

2. 深度学习算法近年来，深度学习算法在稀疏角CT重建中也得到了广泛应用。

深度学习算法通过构建深度神经网络模型，对大量的训练数据进行学习，从而实现对CT图像的高精度重建。

在深度学习算法中，可以通过引入稀疏约束等手段，进一步提高图像的质量。

此外，深度学习算法还可以通过端到端的训练方式，实现从投影数据到图像的直接映射，从而提高重建速度和精度。

3. 压缩感知算法压缩感知算法是一种基于信号稀疏性的重建算法，也被广泛应用于稀疏角CT重建中。

该算法通过在数据采集和图像重建过程中引入压缩感知技术，实现对投影数据的稀疏表示和高效处理。

在压缩感知算法中，可以通过引入先验知识等手段，进一步提高图像的分辨率和减少伪影。

基于多层特征嵌入的单目标跟踪算法

基于多层特征嵌入的单目标跟踪算法1. 内容描述基于多层特征嵌入的单目标跟踪算法是一种在计算机视觉领域中广泛应用的跟踪技术。

该算法的核心思想是通过多层特征嵌入来提取目标物体的特征表示，并利用这些特征表示进行目标跟踪。

该算法首先通过预处理步骤对输入图像进行降维和增强，然后将降维后的图像输入到神经网络中，得到不同层次的特征图。

通过对这些特征图进行池化操作，得到一个低维度的特征向量。

将这个特征向量输入到跟踪器中，以实现对目标物体的实时跟踪。

为了提高单目标跟踪算法的性能，本研究提出了一种基于多层特征嵌入的方法。

该方法首先引入了一个自适应的学习率策略，使得神经网络能够根据当前训练状态自动调整学习率。

通过引入注意力机制，使得神经网络能够更加关注重要的特征信息。

为了进一步提高跟踪器的鲁棒性，本研究还采用了一种多目标融合的方法，将多个跟踪器的结果进行加权融合，从而得到更加准确的目标位置估计。

通过实验验证，本研究提出的方法在多种数据集上均取得了显著的性能提升，证明了其在单目标跟踪领域的有效性和可行性。

1.1 研究背景随着计算机视觉和深度学习技术的快速发展，目标跟踪在许多领域(如安防、智能监控、自动驾驶等)中发挥着越来越重要的作用。

单目标跟踪(MOT)算法是一种广泛应用于视频分析领域的技术，它能够实时跟踪视频序列中的单个目标物体，并将其位置信息与相邻帧进行比较，以估计目标的运动轨迹。

传统的单目标跟踪算法在处理复杂场景、遮挡、运动模糊等问题时表现出较差的鲁棒性。

为了解决这些问题，研究者们提出了许多改进的单目标跟踪算法，如基于卡尔曼滤波的目标跟踪、基于扩展卡尔曼滤波的目标跟踪以及基于深度学习的目标跟踪等。

这些方法在一定程度上提高了单目标跟踪的性能，但仍然存在一些局限性，如对多目标跟踪的支持不足、对非平稳运动的适应性差等。

开发一种既能有效跟踪单个目标物体，又能应对多种挑战的单目标跟踪算法具有重要的理论和实际意义。

1.2 研究目的本研究旨在设计一种基于多层特征嵌入的单目标跟踪算法，以提高目标跟踪的准确性和鲁棒性。

一种改进的高斯频率域压缩感知稀疏反演方法(英文)

AbstractCompressive sensing and sparse inversion methods have gained a significant amount of attention in recent years due to their capability to accurately reconstruct signals from measurements with significantly less data than previously possible. In this paper, a modified Gaussian frequency domain compressive sensing and sparse inversion method is proposed, which leverages the proven strengths of the traditional method to enhance its accuracy and performance. Simulation results demonstrate that the proposed method can achieve a higher signal-to- noise ratio and a better reconstruction quality than its traditional counterpart, while also reducing the computational complexity of the inversion procedure.IntroductionCompressive sensing (CS) is an emerging field that has garnered significant interest in recent years because it leverages the sparsity of signals to reduce the number of measurements required to accurately reconstruct the signal. This has many advantages over traditional signal processing methods, including faster data acquisition times, reduced power consumption, and lower data storage requirements. CS has been successfully applied to a wide range of fields, including medical imaging, wireless communications, and surveillance.One of the most commonly used methods in compressive sensing is the Gaussian frequency domain compressive sensing and sparse inversion (GFD-CS) method. In this method, compressive measurements are acquired by multiplying the original signal with a randomly generated sensing matrix. The measurements are then transformed into the frequency domain using the Fourier transform, and the sparse signal is reconstructed using a sparsity promoting algorithm.In recent years, researchers have made numerous improvementsto the GFD-CS method, with the goal of improving its reconstruction accuracy, reducing its computational complexity, and enhancing its robustness to noise. In this paper, we propose a modified GFD-CS method that combines several techniques to achieve these objectives.Proposed MethodThe proposed method builds upon the well-established GFD-CS method, with several key modifications. The first modification is the use of a hierarchical sparsity-promoting algorithm, which promotes sparsity at both the signal level and the transform level. This is achieved by applying the hierarchical thresholding technique to the coefficients corresponding to the higher frequency components of the transformed signal.The second modification is the use of a novel error feedback mechanism, which reduces the impact of measurement noise on the reconstructed signal. Specifically, the proposed method utilizes an iterative algorithm that updates the measurement error based on the difference between the reconstructed signal and the measured signal. This feedback mechanism effectively increases the signal-to-noise ratio of the reconstructed signal, improving its accuracy and robustness to noise.The third modification is the use of a low-rank approximation method, which reduces the computational complexity of the inversion algorithm while maintaining reconstruction accuracy. This is achieved by decomposing the sensing matrix into a product of two lower dimensional matrices, which can be subsequently inverted using a more efficient algorithm.Simulation ResultsTo evaluate the effectiveness of the proposed method, we conducted simulations using synthetic data sets. Three different signal types were considered: a sinusoidal signal, a pulse signal, and an image signal. The results of the simulations were compared to those obtained using the traditional GFD-CS method.The simulation results demonstrate that the proposed method outperforms the traditional GFD-CS method in terms of signal-to-noise ratio and reconstruction quality. Specifically, the proposed method achieves a higher signal-to-noise ratio and lower mean squared error for all three types of signals considered. Furthermore, the proposed method achieves these results with a reduced computational complexity compared to the traditional method.ConclusionThe results of our simulations demonstrate the effectiveness of the proposed method in enhancing the accuracy and performance of the GFD-CS method. The combination of sparsity promotion, error feedback, and low-rank approximation techniques significantly improves the signal-to-noise ratio and reconstruction quality, while reducing thecomputational complexity of the inversion procedure. Our proposed method has potential applications in a wide range of fields, including medical imaging, wireless communications, and surveillance.。

目标跟踪算法综述

目标跟踪算法综述大连理工大学卢湖川一、引言目标跟踪是计算机视觉领域的一个重要问题，在运动分析、视频压缩、行为识别、视频监控、智能交通和机器人导航等很多研究方向上都有着广泛的应用。

目标跟踪的主要任务是给定目标物体在第一帧视频图像中的位置，通过外观模型和运动模型估计目标在接下来的视频图像中的状态。

如图1所示。

目标跟踪主要可以分为5部分，分别是运动模型、特征提取、外观模型、目标定位和模型更新。

运动模型可以依据上一帧目标的位置来预测在当前帧目标可能出现的区域，现在大部分算法采用的是粒子滤波或相关滤波的方法来建模目标运动。

随后，提取粒子图像块特征，利用外观模型来验证运动模型预测的区域是被跟踪目标的可能性，进行目标定位。

由于跟踪物体先验信息的缺乏，需要在跟踪过程中实时进行模型更新，使得跟踪器能够适应目标外观和环境的变化。

尽管在线目标跟踪的研究在过去几十年里有很大进展，但是由被跟踪目标外观及周围环境变化带来的困难使得设计一个鲁棒的在线跟踪算法仍然是一个富有挑战性的课题。

本文将对最近几年本领域相关算法进行综述。

二、目标跟踪研究现状1. 基于相关滤波的目标跟踪算法在相关滤波目标跟踪算法出现之前，大部分目标跟踪算法采用粒子滤波框架来进行目标跟踪，粒子数量往往成为限制算法速度的一个重要原因。

相关滤波提出了一种新颖的循环采样方法，并利用循环样本构建循环矩阵。

利用循环矩阵时域频域转换的特殊性质，将运算转换到频域内进行计算，大大加快的分类器的训练。

同时，在目标检测阶段，分类器可以同时得到所有循环样本得分组成的响应图像，根据最大值位置进行目标定位。

相关滤波用于目标跟踪最早是在MOSSE算法[1]中提出的。

发展至今，很多基于相关滤波的改进工作在目标跟踪领域已经取得很多可喜的成果。

1.1. 特征部分改进MOSSE[1] 算法及在此基础上引入循环矩阵快速计算的CSK[2]算法均采用简单灰度特征，这种特征很容易受到外界环境的干扰，导致跟踪不准确。

ICML_NIPS_ICCV_CVPR(14~18)

ICML2014ICML20151. An embarrassingly simple approach to zero-shot learning2. Learning Transferable Features with Deep Adaptation Networks3. A Theoretical Analysis of Metric Hypothesis Transfer Learning4. Gradient-based hyperparameter optimization through reversible learningICML20161. One-Shot Generalization in Deep Generative Models2. Meta-Learning with Memory-Augmented Neural Networks3. Meta-gradient boosted decision tree model for weight and target learning4. Asymmetric Multi-task Learning based on Task Relatedness and ConfidenceICML20171. DARLA: Improving Zero-Shot Transfer in Reinforcement Learning2. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks3. Meta Networks4. Learning to learn without gradient descent by gradient descentICML20181. MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shotand Zero-shot Learning2. Understanding and Simplifying One-Shot Architecture Search3. One-Shot Segmentation in Clutter4. Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory5. Bilevel Programming for Hyperparameter Optimization and Meta-Learning6. Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace7. Been There, Done That: Meta-Learning with Episodic Recall8. Learning to Explore via Meta-Policy Gradient9. Transfer Learning via Learning to Transfer10. Rapid adaptation with conditionally shifted neuronsNIPS20141. Zero-shot recognition with unreliable attributesNIPS2015NIPS20161. Learning feed-forward one-shot learners2. Matching Networks for One Shot Learning3. Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs NIPS20171. One-Shot Imitation Learning2. Few-Shot Learning Through an Information Retrieval Lens3. Prototypical Networks for Few-shot Learning4. Few-Shot Adversarial Domain Adaptation5. A Meta-Learning Perspective on Cold-Start Recommendations for Items6. Neural Program Meta-InductionNIPS20181. Bayesian Model-Agnostic Meta-Learning2. The Importance of Sampling inMeta-Reinforcement Learning3. MetaAnchor: Learning to Detect Objects with Customized Anchors4. MetaGAN: An Adversarial Approach to Few-Shot Learning5. Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior6. Meta-Gradient Reinforcement Learning7. Meta-Reinforcement Learning of Structured Exploration Strategies8. Meta-Learning MCMC Proposals9. Probabilistic Model-Agnostic Meta-Learning10. MetaReg: Towards Domain Generalization using Meta-Regularization11. Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning12. Uncertainty-Aware Few-Shot Learning with Probabilistic Model-Agnostic Meta-Learning13. Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies14. Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning15. Delta-encoder: an effective sample synthesis method for few-shot object recognition16. One-Shot Unsupervised Cross Domain Translation17. Generalized Zero-Shot Learning with Deep Calibration Network18. Domain-Invariant Projection Learning for Zero-Shot Recognition19. Low-shot Learning via Covariance-Preserving Adversarial Augmentation Network20. Improved few-shot learning with task conditioning and metric scaling21. Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning22. Learning to Play with Intrinsically-Motivated Self-Aware Agents23. Learning to Teach with Dynamic Loss Functiaons24. Memory Replay GANs: learning to generate images from new categories without forgettingICCV20151. One Shot Learning via Compositions of Meaningful Patches2. Unsupervised Domain Adaptation for Zero-Shot Learning3. Active Transfer Learning With Zero-Shot Priors: Reusing Past Datasets for Future Tasks4. Zero-Shot Learning via Semantic Similarity Embedding5. Semi-Supervised Zero-Shot Classification With Label Representation Learning6. Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions7. Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit DetectionICCV20171. Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks2. Attributes2Classname: A Discriminative Model for Attribute-Based Unsupervised Zero-ShotLearning3. Low-Shot Visual Recognition by Shrinking and Hallucinating Features4. Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning5. Learning Discriminative Latent Attributes for Zero-Shot Classification6. Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of ActionsCVPR20141. COSTA: Co-Occurrence Statistics for Zero-Shot Classification2. Zero-shot Event Detection using Multi-modal Fusion of Weakly Supervised Concepts3. Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective CVPR20151. Zero-Shot Object Recognition by Semantic Manifold DistanceCVPR20162. Multi-Cue Zero-Shot Learning With Strong Supervision3. Latent Embeddings for Zero-Shot Classification4. One-Shot Learning of Scene Locations via Feature Trajectory Transfer5. Less Is More: Zero-Shot Learning From Online Textual Documents With Noise Suppression6. Synthesized Classifiers for Zero-Shot Learning7. Recovering the Missing Link: Predicting Class-Attribute Associations for UnsupervisedZero-Shot Learning8. Fast Zero-Shot Image Tagging9. Zero-Shot Learning via Joint Latent Similarity Embedding10. Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated ImageAnnotation11. Learning to Co-Generate Object Proposals With a Deep Structured Network12. Learning to Select Pre-Trained Deep Representations With Bayesian Evidence Framework13. DeepStereo: Learning to Predict New Views From the World’s ImageryCVPR20171. One-Shot Video Object Segmentation2. FastMask: Segment Multi-Scale Object Candidates in One Shot3. Few-Shot Object Recognition From Machine-Labeled Web Images4. From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual DataSynthesis5. Learning a Deep Embedding Model for Zero-Shot Learning6. Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning7. Multi-Attention Network for One Shot Learning8. Zero-Shot Action Recognition With Error-Correcting Output Codes9. One-Shot Metric Learning for Person Re-Identification10. Semantic Autoencoder for Zero-Shot Learning11. Zero-Shot Recognition Using Dual Visual-Semantic Mapping Paths12. Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning13. One-Shot Hyperspectral Imaging Using Faced Reflectors14. Gaze Embeddings for Zero-Shot Image Classification15. Zero-Shot Learning - the Good, the Bad and the Ugly16. Link the Head to the “Beak”: Zero Shot Learning From Noisy Text Description at PartPrecision17. Semantically Consistent Regularization for Zero-Shot Recognition18. Semantically Consistent Regularization for Zero-Shot Recognition19. Zero-Shot Classification With Discriminative Semantic Representation Learning20. Learning to Detect Salient Objects With Image-Level Supervision21. Quad-Networks: Unsupervised Learning to Rank for Interest Point DetectionCVPR20181. A Generative Adversarial Approach for Zero-Shot Learning From Noisy Texts2. Transductive Unbiased Embedding for Zero-Shot Learning3. Zero-Shot Visual Recognition Using Semantics-Preserving Adversarial EmbeddingNetworks4. Learning to Compare: Relation Network for Few-Shot Learning5. One-Shot Action Localization by Learning Sequence Matching Network6. Multi-Label Zero-Shot Learning With Structured Knowledge Graphs7. “Zero-Shot” Super-Resolution Using Deep Internal Learning8. Low-Shot Learning With Large-Scale Diffusion9. CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition10. Zero-Shot Sketch-Image Hashing11. Structured Set Matching Networks for One-Shot Part Labeling12. Memory Matching Networks for One-Shot Image Recognition13. Generalized Zero-Shot Learning via Synthesized Examples14. Dynamic Few-Shot Visual Learning Without Forgetting15. Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification byStepwise Learning16. Feature Generating Networks for Zero-Shot Learning17. Low-Shot Learning With Imprinted Weights18. Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs19. Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification20. Few-Shot Image Recognition by Predicting Parameters From Activations21. Low-Shot Learning From Imaginary Data22. Discriminative Learning of Latent Features for Zero-Shot Recognition23. Multi-Content GAN for Few-Shot Font Style Transfer24. Preserving Semantic Relations for Zero-Shot Learning25. Zero-Shot Kernel Learning26. Neural Style Transfer via Meta Networks27. Learning to Estimate 3D Human Pose and Shape From a Single Color Image28. Learning to Segment Every Thing29. Leveraging Unlabeled Data for Crowd Counting by Learning to Rank。

基于深度学习的脊柱侧弯图像处理方法

基于深度学习的脊柱侧弯图像处理方法深度学习是一种机器学习领域中的重要技术，其利用神经网络模型来进行训练和预测。

在医学领域，深度学习已经被广泛应用于图像处理、疾病诊断和预测等方面。

本文将探讨基于深度学习的脊柱侧弯图像处理方法，旨在提高该领域的诊断准确性和效率。

脊柱侧弯是一种常见的脊柱疾病，特征为脊柱的侧弯及旋转。

传统的脊柱侧弯图像处理方法主要依赖于人工特征提取和分类算法，但这些方法存在一些限制，如特征提取的主观性、分类器的复杂性等。

深度学习通过在大规模数据集上进行训练，能够自动提取和学习复杂的特征，并通过神经网络模型进行图像分类和预测。

首先，基于深度学习的脊柱侧弯图像处理方法需要一个大规模的脊柱侧弯图像数据集。

该数据集包含了正常脊柱和侧弯脊柱的图像样本，且每个样本都有相应的标签。

在训练过程中，神经网络模型会根据这些标签来进行监督学习，逐步优化网络参数，提高图像分类的准确性。

其次，基于深度学习的脊柱侧弯图像处理方法需要选择合适的神经网络模型。

在图像分类领域，卷积神经网络（Convolutional Neural Network, CNN）是一种常用且有效的模型。

CNN通过多层卷积和池化操作，能够提取图像中的局部特征，并通过全连接层进行最终的分类预测。

第三，基于深度学习的脊柱侧弯图像处理方法还需要进行网络的训练和验证。

在训练过程中，将数据集分为训练集和验证集，通过反向传播算法来更新网络参数。

为了防止过拟合现象的出现，可以在训练过程中采用一些正则化技术，如dropout和L2正则化等。

同时，还可以利用交叉验证方法对网络进行评估和选择最优模型。

最后，基于深度学习的脊柱侧弯图像处理方法在实际应用中还需要解决一些问题。

例如，对于训练集和验证集中存在的标签噪声、样本不均衡等问题，需要进行相应的处理。

此外，在实际的图像处理过程中，还需要考虑数据增强技术，以提高模型的鲁棒性和泛化能力。

本文介绍了基于深度学习的脊柱侧弯图像处理方法，并探讨了其在脊柱侧弯诊断领域的潜在应用。

多视角学习的几篇文章整理

多视⾓学习的⼏篇⽂章整理最近在调研3D算法⽅⾯的⼯作，整理了⼏篇多视⾓学习的⽂章。

还没调研完，先写个⼤概。

因为直接⽤2D的卷积神经⽹络⽅法并不能很好的处理3D任务，这⼏篇⽂章主要偏向于将3D模型从多个⾓度变换成多张2D的图像，然后使⽤2D领域的⽅法处理3D任务。

所以⼤家主要涉及到两个问题：1、视⾓选择问题（如何选择视⾓？选择⼏个视⾓？如果能够主动的选择显著性视⾓就更好了）；2、视⾓特征信息的融合。

⽬录１、（ICCV2015）MVCNN：Multi-view Convolutional Neural Networks for 3D Shape Recognition论⽂地址：代码：该篇⽂章被认为是多视⾓学习的开⼭之作；简单的求⼀个3D形状的多视⾓图像的特征描述⼦的平均值，或者简单的将这些特征描述⼦做“连接”（这地⽅可以想象成将特征简单的“串联”），会导致不好的效果。

所以，我们集中于融合多视⾓2D图像产⽣的特征，以便综合这些信息，形成⼀个简单、⾼效的3D形状描述⼦。

因此，我们设计了Multi-view CNN（MVCNN），放在基础的2D图像CNN之中。

如图所⽰，同⼀个3D形状的每⼀张视⾓图像各⾃独⽴地经过第⼀段的CNN1卷积⽹络，在⼀个叫做View-pooling层进⾏“聚合”。

之后，再送⼊剩下的CNN2卷积⽹络。

整张⽹络第⼀部分的所有分⽀，共享相同的 CNN1⾥的参数。

在View-pooling层中，我们逐元素取最⼤值操作，另⼀种是求平均值操作，但在我们的实验中，这并不有效。

这个View-pooling层，可以放在⽹络中的任何位置。

经过我们的实验，这⼀层最好放在最后的卷积层（Conv5），以最优化的执⾏分类与检索的任务。

参考：２、(CVPR2016) Volumetric and multi-view CNNs for object classification on 3D data论⽂地址：代码：３、（BMVC2017）DSCNN：Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition论⽂地址：代码：４、（CVPR2018）GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition论⽂地址：代码：这篇⽂章在MVCNN的基础之上，提出了group-view convolutional neural network（GVCNN）。

《2024年立体匹配与点云重建关键技术的研究》范文

《立体匹配与点云重建关键技术的研究》篇一一、引言随着计算机视觉技术的快速发展，立体匹配与点云重建作为其重要组成部分，已经成为了科研和工程领域的重要研究方向。

立体匹配通过获取同一场景从不同角度拍摄的两张或多张图像，来估计场景中各点的三维信息。

而点云重建则是在立体匹配的基础上，通过一系列算法处理，将三维空间中的点云数据整合成三维模型。

本文将重点研究立体匹配与点云重建的关键技术，并探讨其在实际应用中的价值。

二、立体匹配关键技术研究1. 立体匹配概述立体匹配是计算机视觉领域中的一个基本问题，其目的是从两幅或多幅图像中提取出对应的特征点，并计算这些特征点在三维空间中的位置。

立体匹配的准确性直接影响到后续的三维重建和识别等任务的精度。

2. 立体匹配算法研究目前，常见的立体匹配算法包括基于区域、基于特征和基于相位的方法。

其中，基于特征的方法因其计算效率高、对光照和噪声的鲁棒性较好而受到广泛关注。

在特征提取方面，SIFT、SURF和ORB等算法被广泛应用于立体匹配任务中。

此外，深度学习在立体匹配中也取得了显著的成果，如Displets、MC-CNN 等模型均能实现较高的匹配精度。

三、点云重建关键技术研究1. 点云重建概述点云重建是指将通过立体匹配等方法获得的三维空间点云数据整合成三维模型的过程。

点云数据具有丰富的几何信息，因此点云重建在三维建模、虚拟现实、机器人导航等领域具有广泛的应用价值。

2. 点云重建算法研究点云重建算法主要包括表面重建和纹理映射两个部分。

表面重建的目的是从无序的点云数据中恢复出物体的表面形状，常见的算法包括Delaunay三角剖分、泊松重建等。

纹理映射则是将二维图像的纹理信息映射到三维模型上，以增强模型的视觉效果。

近年来，深度学习在点云处理方面也取得了显著的进展，如PointNet、PointNet++等模型均能实现高效的点云处理和分类任务。

四、立体匹配与点云重建的融合与应用1. 融合方法研究立体匹配与点云重建的融合是提高三维重建精度的关键。

《2024年立体匹配与点云重建关键技术的研究》范文

《立体匹配与点云重建关键技术的研究》篇一一、引言立体匹配与点云重建是计算机视觉和三维重建领域的重要技术。

随着三维传感器和计算机视觉技术的快速发展，立体匹配与点云重建技术在许多领域得到了广泛应用，如无人驾驶、机器人导航、虚拟现实等。

本文旨在研究立体匹配与点云重建的关键技术，分析其原理、方法及存在的问题，并探讨其未来的发展趋势。

二、立体匹配技术研究1. 立体匹配基本原理立体匹配是利用两幅或多幅不同视角的图像，通过算法找到场景中同一目标点的对应关系，从而获取物体的深度信息。

立体匹配技术主要包括特征提取、特征匹配和视差计算等步骤。

2. 特征提取与匹配方法特征提取是立体匹配的关键步骤，其目的是从图像中提取出具有代表性的特征点。

常用的特征提取方法包括SIFT、SURF、ORB等。

在特征提取的基础上，采用特征匹配算法寻找两幅图像中的对应关系，如基于区域、基于特征的匹配方法等。

3. 视差计算与优化通过特征匹配得到的视差图存在噪声和错误匹配等问题，需要进行视差计算和优化。

常见的视差计算方法包括基于全局优化算法的视差计算和基于局部优化算法的视差计算。

此外，为了提高匹配精度，还需要对视差图进行优化处理，如采用左右一致性检查等方法。

三、点云重建技术研究1. 点云重建基本原理点云重建是指根据一组空间中的点集，通过算法重建出物体的三维模型。

点云数据可以通过各种传感器获取，如激光扫描仪、深度相机等。

点云重建技术主要包括数据采集、预处理、配准和建模等步骤。

2. 数据采集与预处理数据采集是点云重建的第一步，通过传感器获取场景的点云数据。

由于传感器和环境的干扰，获取的点云数据可能存在噪声、缺失等问题，需要进行预处理操作，如去噪、滤波、填充等。

3. 点云配准与建模在完成数据预处理后，需要对点云数据进行配准操作，即将不同视角下的点云数据统一到同一坐标系下。

配准完成后，通过建模算法将点云数据转换为三维模型。

常用的建模算法包括表面重建算法、体素化算法等。

15ICCV_Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for

Weakly-and Semi-Supervised Learning of a Deep Convolutional Network forSemantic Image SegmentationGeorge Papandreou∗Google,Inc. gpapan@ Liang-Chieh Chen∗UCLAlcchen@Kevin P.MurphyGoogle,Inc.kpmurphy@Alan L.YuilleUCLAyuille@AbstractDeep convolutional neural networks(DCNNs)trained on a large number of images with strong pixel-level anno-tations have recently signiﬁcantly pushed the state-of-art in semantic image segmentation.We study the more challeng-ing problem of learning DCNNs for semantic image seg-mentation from either(1)weakly annotated training data such as bounding boxes or image-level labels or(2)a com-bination of few strongly labeled and many weakly labeled images,sourced from one or multiple datasets.We develop Expectation-Maximization(EM)methods for semantic im-age segmentation model training under these weakly super-vised and semi-supervised settings.Extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC2012image segmentation benchmark,while requiring signiﬁcantly less annotation effort.We share source code implementing the proposed system at https: ///deeplab/deeplab-public.1.IntroductionSemantic image segmentation refers to the problem of assigning a semantic label(such as“person”,“car”or “dog”)to every pixel in the image.Various approaches have been tried over the years,but according to the results on the challenging Pascal VOC2012segmentation benchmark,the best performing methods all use some kind of Deep Convo-lutional Neural Network(DCNN)[2,5,8,14,25,27,41].In this paper,we work with the DeepLab-CRF approach of[5,41].This combines a DCNN with a fully connected Conditional Random Field(CRF)[19],in order to get high resolution segmentations.This model achieves state-of-art results on the challenging PASCAL VOC segmentation benchmark[13],delivering a mean intersection-over-union (IOU)score exceeding70%.A key bottleneck in building this class of DCNN-based∗Theﬁrst two authors contributed equally to this work.segmentation models is that they typically require pixel-level annotated images during training.Acquiring such data is an expensive,time-consuming annotation effort.Weak annotations,in the form of bounding boxes(i.e.,coarse object locations)or image-level labels(i.e.,information about which object classes are present)are far easier to collect than detailed pixel-level annotations.We develop new methods for training DCNN image segmentation mod-els from weak annotations,either alone or in combination with a small number of strong annotations.Extensive ex-periments,in which we achieve performance up to69.0%, demonstrate the effectiveness of the proposed techniques.According to[24],collecting bounding boxes around each class instance in the image is about15times faster/cheaper than labeling images at the pixel level.We demonstrate that it is possible to learn a DeepLab-CRF model delivering62.2%IOU on the PASCAL VOC2012 test set by training it on a simple foreground/background segmentation of the bounding box annotations.An even cheaper form of data to collect is image-level labels,which specify the presence or absence of se-mantic classes,but not the object locations.Most exist-ing approaches for training semantic segmentation models from this kind of very weak labels use multiple instance learning(MIL)techniques.However,even recent weakly-supervised methods such as[25]deliver signiﬁcantly infe-rior results compared to their fully-supervised counterparts, only achieving25.7%.Including additional trainable ob-jectness[7]or segmentation[1]modules that largely in-crease the system complexity,[31]has improved perfor-mance to40.6%,which still signiﬁcantly lags performance of fully-supervised systems.We develop novel online Expectation-Maximization (EM)methods for training DCNN semantic segmentation models from weakly annotated data.The proposed algo-rithms alternate between estimating the latent pixel labels (subject to the weak annotation constraints),and optimiz-ing the DCNN parameters using stochastic gradient descent (SGD).When we only have access to image-level anno-tated training data,we achieve39.6%,close to[31]butwithout relying on any external objectness or segmenta-tion module.More importantly,our EM approach also excels in the semi-supervised scenario which is very im-portant in practice.Having access to a small number of strongly (pixel-level)annotated images and a large number of weakly (bounding box or image-level)annotated images,the proposed algorithm can almost match the performance of the fully-supervised system.For example,having access to 2.9k pixel-level images and 9k image-level annotated im-ages yields 68.5%,only 2%inferior the performance of the system trained with all 12k images strongly annotated at the pixel level.Finally,we show that using additional weak or strong annotations from the MS-COCO dataset can further improve results,yielding 73.9%on the PASCAL VOC 2012benchmark.Contributions In summary,our main contributions are:1.We present EM algorithms for training with image-level or bounding box annotation,applicable to both the weakly-supervised and semi-supervised settings.2.We show that our approach achieves excellent per-formance when combining a small number of pixel-level annotated images with a large number of image-level or bounding box annotated images,nearly match-ing the results achieved when all training images have pixel-level annotations.3.We show that combining weak or strong annotations across datasets yields further improvements.In partic-ular,we reach 73.9%IOU performance on PASCAL VOC 2012by combining annotations from the PAS-CAL and MS-COCO datasets.2.Related workTraining segmentation models with only image-level labels has been a challenging problem in the literature [12,36,37,39].Our work is most related to other re-cent DCNN models such as [30,31],who also study the weakly supervised setting.They both develop MIL-based algorithms for the problem.In contrast,our model em-ploys an EM algorithm,which similarly to [26]takes into account the weak labels when inferring the latent image seg-mentations.Moreover,[31]proposed to smooth the predic-tion results by region proposal algorithms,e.g .,CPMC [3]and MCG [1],learned on pixel-segmented images.Neither [30,31]cover the semi-supervised setting.Bounding box annotations have been utilized for seman-tic segmentation by [38,42],while [15,21,40]describe schemes exploiting both image-level labels and bounding box annotations.[4]attained human-level accuracy for car segmentation by using 3D bounding boxes.Bounding box annotations are also commonly used in interactive segmen-tation [22,33];we show that such foreground/backgroundPixel annotationsImage Deep Convolutional Neural NetworkLossFigure 1.DeepLab model training from fully annotated images.segmentation methods can effectively estimate object seg-ments accurate enough for training a DCNN semantic seg-mentation system.Working in a setting very similar to ours,[9]employed MCG [1](which requires training from pixel-level annotations)to infer object masks from bounding box labels during DCNN training.3.Proposed MethodsWe build on the DeepLab model for semantic image seg-mentation proposed in [5].This uses a DCNN to predict the label distribution per pixel,followed by a fully-connected (dense)CRF [19]to smooth the predictions while preserv-ing image edges.In this paper,we focus for simplicity on methods for training the DCNN parameters from weak la-bels,only using the CRF at test time.Additional gains can be obtained by integrated end-to-end training of the DCNN and CRF parameters [41,6].Notation We denote by x the image values and y the seg-mentation map.In particular,y m ∈{0,...,L }is the pixel label at position m ∈{1,...,M },assuming that we have the background as well as L possible foreground labels and M is the number of pixels.Note that these pixel-level la-bels may not be visible in the training set.We encode the set of image-level labels by z ,with z l =1,if the l -th label is present anywhere in the image,i.e .,if m [y m =l ]>0.3.1.Pixel-level annotationsIn the fully supervised case illustrated in Fig.1,the ob-jective function isJ (θ)=log P (y |x ;θ)=Mm =1log P (y m |x ;θ),(1)where θis the vector of DCNN parameters.The per-pixellabel distributions are computed byP (y m |x ;θ)∝exp(f m (y m |x ;θ)),(2)where f m (y m |x ;θ)is the output of the DCNN at pixel m .We optimize J (θ)by mini-batch SGD.3.2.Image-level annotationsWhen only image-level annotation is available,we can observe the image values x and the image-level labels z ,but the pixel-level segmentations y are latent variables.WeAlgorithm 1Weakly-Supervised EM (ﬁxed bias version)Input:Initial CNN parameters θ′,potential parameters b l ,l ∈{0,...,L },image x ,image-level label set z .E-Step:For each image position m1:ˆf m (l )=f m (l |x ;θ′)+b l ,if z l =12:ˆf m (l )=f m (l |x ;θ′),if z l =03:ˆy m =argmax l ˆf m (l )M-Step:4:Q (θ;θ′)=log P (ˆy |x ,θ)= M m =1log P (ˆy m |x ,θ)5:Compute ∇θQ (θ;θ′)and use SGD to update θ′.have the following probabilistic graphical model:P (x ,y ,z ;θ)=P (x )Mm =1P (y m |x ;θ)P (z |y ).(3)We pursue an EM-approach in order to learn the model parameters θfrom training data.If we ignore terms that do not depend on θ,the expected complete-data log-likelihood given the previous parameter estimate θ′isQ (θ;θ′)= yP (y |x ,z ;θ′)log P (y |x ;θ)≈log P (ˆy |x ;θ),(4)where we adopt a hard-EM approximation,estimating in the E-step of the algorithm the latent segmentation by ˆy =argmax yP (y |x ;θ′)P (z |y )(5)=argmax ylog P (y |x ;θ′)+log P (z |y )(6)=argmaxyMm =1f m (y m |x ;θ′)+log P (z |y ) .(7)In the M-step of the algorithm,we optimize Q (θ;θ′)≈log P (ˆy |x ;θ)by mini-batch SGD similarly to (1),treatingˆyas ground truth segmentation.To completely identify the E-step (7),we need to specifythe observation model P (z |y ).We have experimented withtwo variants,EM-Fixed and EM-Adapt .EM-Fixed In this variant,we assume that log P (z |y )fac-torizes over pixel positions aslog P (z |y )=Mm =1φ(y m ,z )+(const),(8)allowing us to estimate the E-step segmentation at eachpixel separatelyˆy m =argmaxy mˆf m (y m ).=f m (y m |x ;θ′)+φ(y m ,z ).(9)ImageFigure 2.DeepLab model training using image-level labels.We assume thatφ(y m =l,z )=b l if z l =10if z l =0(10)We set the parameters b l =b fg ,if l >0and b 0=b bg ,with b fg >b bg >0.Intuitively,this potential encourages a pixel to be assigned to one of the image-level labels z .We choose b fg >b bg ,boosting present foreground classes more than the background,to encourage full object coverage andavoid a degenerate solution of all pixels being assigned to background.The procedure is summarized in Algorithm 1and illustrated in Fig.2.EM-Adapt In this method,we assume that log P (z |y )=φ(y ,z )+(const),where φ(y ,z )takes the form of a cardi-nality potential [23,32,35].In particular,we encourage atleast a ρl portion of the image area to be assigned to classl ,if z l =1,and enforce that no pixel is assigned to classl ,if z l =0.We set the parameters ρl =ρfg ,if l >0andρ0=ρbg .Similar constraints appear in [10,20].In practice,we employ a variant of Algorithm 1.Weadaptively set the image-and class-dependent biases b l so as the prescribed proportion of the image area is assigned to the background or foreground object classes.This acts as a powerful constraint that explicitly prevents the background score from prevailing in the whole image,also promoting higher foreground object coverage.The detailed algorithm is described in the supplementary material.EM It is instructive to compare our EM-based approach with two recent Multiple Instance Learning (MIL)methods for learning semantic image segmentation models [30,31].The method in [30]deﬁnes an MIL classiﬁcation objective based on the per-class spatial maximum of the lo-cal label distributions of (2),ˆP (l |x ;θ).=max m P (y m =l |x ;θ),and [31]adopts a softmax function.While this approach has worked well for image classiﬁcation tasks [28,29],it is less suited for segmentation as it does not pro-mote full object coverage:The DCNN becomes tuned to focus on the most distinctive object parts (e.g .,human face)instead of capturing the whole object (e.g .,human body).ImageBbox annotationsDeep ConvolutionalNeural NetworkDenseCRFargmaxLossFigure3.DeepLab model training from bounding boxes.3.3.Bounding Box AnnotationsWe explore three alternative methods for training our segmentation model from labeled bounding boxes.Theﬁrst Bbox-Rect method amounts to simply consider-ing each pixel within the bounding box as positive example for the respective object class.Ambiguities are resolved by assigning pixels that belong to multiple bounding boxes to the one that has the smallest area.The bounding boxes fully surround objects but also contain background pixels that contaminate the training set with false positive examples for the respective object classes.Toﬁlter out these background pixels,we have also explored a second Bbox-Seg method in which we per-form automatic foreground/background segmentation.To perform this segmentation,we use the same CRF as in DeepLab.More speciﬁcally,we constrain the center area of the bounding box(α%of pixels within the box)to be fore-ground,while we constrain pixels outside the bounding box to be background.We implement this by appropriately set-ting the unary terms of the CRF.We then infer the labels for pixels in between.We cross-validate the CRF parameters to maximize segmentation accuracy in a small held-out set of fully-annotated images.This approach is similar to the grabcut method of[33].Examples of estimated segmenta-tions with the two methods are shown in Fig.4.The two methods above,illustrated in Fig.3,estimate segmentation maps from the bounding box annotation as a pre-processing step,then employ the training procedure of Sec.3.1,treating these estimated labels as ground-truth.Our third Bbox-EM-Fixed method is an EM algorithm that allows us to reﬁne the estimated segmentation maps throughout training.The method is a variant of the EM-Fixed algorithm in Sec.3.2,in which we boost the present foreground object scores only within the bounding box area.3.4.Mixed strong and weak annotationsIn practice,we often have access to a large number of weakly image-level annotated images and can only afford to procure detailed pixel-level annotations for a small fraction of these images.We handlethishybrid training scenario byImage with Bbox Ground-Truth Bbox-Rect Bbox-SegFigure4.Estimatedsegmentation frombounding box annotation.+Pixel AnnotationsFG/BGBiasargmax1. Car2. Person3. HorseDeep ConvolutionalNeural Network LossDeep ConvolutionalNeural NetworkLossScore mapsFigure5.DeepLab model training on a union of full(strong labels)and image-level(weak labels)annotations.combining the methods presented in the previous sections,as illustrated in Figure5.In SGD training of our deep CNNmodels,we bundle to each mini-batch aﬁxed proportionof strongly/weakly annotated images,and employ our EMalgorithm in estimating at each iteration the latent semanticsegmentations for the weakly annotated images.4.Experimental Evaluation4.1.Experimental ProtocolDatasets The proposed training methods are evaluatedon the PASCAL VOC2012segmentation benchmark[13],consisting of20foreground object classes and one back-ground class.The segmentation part of the original PAS-CAL VOC2012dataset contains1464(train),1449(val),and1456(test)images for training,validation,and test,re-spectively.We also use the extra annotations provided by[16],resulting in augmented sets of10,582(train aug)and12,031(trainval aug)images.We have also experimentedwith the large MS-COCO2014dataset[24],which con-tains123,287images in its trainval set.The MS-COCO2014dataset has80foreground object classes and one back-ground class and is also annotated at the pixel level.The performance is measured in terms of pixelintersection-over-union(IOU)averaged across the21classes.Weﬁrst evaluate our proposed methods on the PAS-CAL VOC2012val set.We then report our results on the ofﬁcial PASCAL VOC2012benchmark test set(whose an-notations are not released).We also compare our test set results with other competing methods.Reproducibility We have implemented the proposed methods by extending the excellent Caffe framework[18]. We share our source code,conﬁgurationﬁles,and trained models that allow reproducing the results in this paper at a companion web site https:/// deeplab/deeplab-public.Weak annotations In order to simulate the situations where only weak annotations are available and to have fair comparisons(e.g.,use the same images for all settings),we generate the weak annotations from the pixel-level annota-tions.The image-level labels are easily generated by sum-marizing the pixel-level annotations,while the bounding box annotations are produced by drawing rectangles tightly containing each object instance(PASCAL VOC2012also provides instance-level annotations)in the dataset. Network architectures We have experimented with the two DCNN architectures of[5],with parameters initialized from the VGG-16ImageNet[11]pretrained model of[34]. They differ in the receptiveﬁeld of view(FOV)size.We have found that large FOV(224×224)performs best when at least some training images are annotated at the pixel level, whereas small FOV(128×128)performs better when only image-level annotations are available.In the main paper we report the results of the best architecture for each setup and defer the full comparison between the two FOVs to the supplementary material.Training We employ our proposed training methods to learn the DCNN component of the DeepLab-CRF model of [5].For SGD,we use a mini-batch of20-30images and ini-tial learning rate of0.001(0.01for theﬁnal classiﬁer layer), multiplying the learning rate by0.1after aﬁxed number of iterations.We use momentum of0.9and a weight decay of 0.0005.Fine-tuning our network on PASCAL VOC2012 takes about12hours on a NVIDIA Tesla K40GPU.Similarly to[5],we decouple the DCNN and Dense CRF training stages and learn the CRF parameters by cross val-idation to maximize IOU segmentation accuracy in a held-out set of100Pascal val fully-annotated images.We use10 mean-ﬁeld iterations for Dense CRF inference[19].Note that the IOU scores are typically3-5%worse if we don’t use the CRF for post-processing of the results.4.2.Pixel-level annotationsWe haveﬁrst reproduced the results of[5].Training the DeepLab-CRF model with strong pixel-level annota-tions on PASCAL VOC2012,we achieve a mean IOU scoreMethod#Strong#Weak val IOUEM-Fixed(Weak)-10,58220.8EM-Adapt(Weak)-10,58238.2EM-Fixed(Semi)20010,38247.650010,08256.97509,83259.81,0009,58262.01,4645,00063.21,4649,11864.6Strong1,464-62.510,582-67.6Table1.VOC2012val performance for varying number of pixel-level(strong)and image-level(weak)annotations(Sec.4.3).Method#Strong#Weak test IOUMIL-FCN[30]-10k25.7MIL-sppxl[31]-760k35.8MIL-obj[31]BING760k37.0MIL-seg[31]MCG760k40.6EM-Adapt(Weak)-12k39.6EM-Fixed(Semi)1.4k10k66.22.9k9k68.5Strong[5]12k-70.3Table2.VOC2012test performance for varying number of pixel-level(strong)and image-level(weak)annotations(Sec.4.3).of67.6%on val and70.3%on test;see method DeepLab-CRF-LargeFOV in[5,Table1].4.3.Image-level annotationsValidation results We evaluate our proposed methods in training the DeepLab-CRF model using image-level weak annotations from the10,582PASCAL VOC2012train aug set,generated as described in Sec.4.1above.We report the val performance of our two weakly-supervised EM vari-ants described in Sec.3.2.In the EM-Fixed variant we use b fg=5and b bg=3asﬁxed foreground and background biases.We found the results to be quite sensitive to the dif-ference b fg−b bg but not very sensitive to their absolute val-ues.In the adaptive EM-Adapt variant we constrain at least ρbg=40%of the image area to be assigned to background and at leastρfg=20%of the image area to be assigned to foreground(as speciﬁed by the weak label set).We also examine using weak image-level annotations in addition to a varying number of pixel-level annotations, within the semi-supervised learning scheme of Sec.3.4. In this Semi setting we employ strong annotations of a subset of PASCAL VOC2012train set and use the weak image-level labels from another non-overlapping subset of the train aug set.We perform segmentation inference for the images that only have image-level labels by means of EM-Fixed,which we have found to perform better than EM-Adapt in the semi-supervised training setting.The results are summarized in Table1.We see that the EM-Adapt algorithm works much better than the EM-Fixed algorithm when we only have access to image level an-notations,20.8%vs.38.2%validation ing1,464 pixel-level and9,118image-level annotations in the EM-Fixed semi-supervised setting signiﬁcantly improves per-formance,yielding64.6%.Note that image-level annota-tions are helpful,as training only with the1,464pixel-level annotations only yields62.5%.Test results In Table2we report our test results.We com-pare the proposed methods with the recent MIL-based ap-proaches of[30,31],which also report results obtained with image-level annotations on the VOC benchmark.Our EM-Adapt method yields39.6%,which improves over MIL-FCN[30]by a large13.9%margin.As[31]shows,MIL can become more competitive if additional segmentation in-formation is introduced:Using low-level superpixels,MIL-sppxl[31]yields35.8%and is still inferior to our EM algo-rithm.Only if augmented with BING[7]or MCG[1]can MIL obtain results comparable to ours(MIL-obj:37.0%, MIL-seg:40.6%)[31].Note,however,that both BING and MCG have been trained with bounding box or pixel-annotated data on the PASCAL train set,and thus both MIL-obj and MIL-seg indirectly rely on bounding box or pixel-level PASCAL annotations.The more interestingﬁnding of this experiment is that including very few strongly annotated images in the semi-supervised setting signiﬁcantly improves the performance compared to the pure weakly-supervised baseline.For example,using 2.9k pixel-level annotations along with 9k image-level annotations in the semi-supervised setting yields68.5%.We would like to highlight that this re-sult surpasses all techniques which are not based on the DCNN+CRF pipeline of[5](see Table6),even if trained with all available pixel-level annotations.4.4.Bounding box annotationsValidation results In this experiment,we train the DeepLab-CRF model using bounding box annotations from the train aug set.We estimate the training set segmentations in a pre-processing step using the Bbox-Rect and Bbox-Seg methods described in Sec.3.3.We assume that we also have access to100fully-annotated PASCAL VOC2012val images which we have used to cross-validate the value of the single Bbox-Seg parameterα(percentage of the cen-ter bounding box area constrained to be foreground).We variedαfrom20%to80%,ﬁnding thatα=20%maxi-mizes accuracy in terms of IOU in recovering the ground truth foreground from the bounding box.We also examine the effect of combining these weak bounding box annota-tions with strong pixel-level annotations,using the semi-supervised learning methods of Sec.3.4.The results are summarized in Table3.When using only bounding box annotations,we see that Bbox-Seg improves over Bbox-Rect by8.1%,and gets within7.0%of the strong pixel-level annotation result.We observe that combining 1,464strong pixel-level annotations with weak bounding box annotations yields65.1%,only2.5%worse than the strong pixel-level annotation result.In the semi-supervisedMethod#Strong#Box val IOUBbox-Rect(Weak)-10,58252.5Bbox-EM-Fixed(Weak)-10,58254.1Bbox-Seg(Weak)-10,58260.6Bbox-Rect(Semi)1,4649,11862.1Bbox-EM-Fixed(Semi)1,4649,11864.8Bbox-Seg(Semi)1,4649,11865.1Strong1,464-62.510,582-67.6Table3.VOC2012val performance for varying number of pixel-level(strong)and bounding box(weak)annotations(Sec.4.4).Method#Strong#Box test IOUBoxSup[9]MCG10k64.6BoxSup[9] 1.4k(+MCG)9k66.2Bbox-Rect(Weak)-12k54.2Bbox-Seg(Weak)-12k62.2Bbox-Seg(Semi) 1.4k10k66.6Bbox-EM-Fixed(Semi) 1.4k10k66.6Bbox-Seg(Semi) 2.9k9k68.0Bbox-EM-Fixed(Semi) 2.9k9k69.0Strong[5]12k-70.3Table4.VOC2012test performance for varying number of pixel-level(strong)and bounding box(weak)annotations(Sec.4.4).learning settings and1,464strong annotations,Semi-Bbox-EM-Fixed and Semi-Bbox-Seg perform similarly.Test results In Table4we report our test results.We com-pare the proposed methods with the very recent BoxSup ap-proach of[9],which also uses bounding box annotations on the VOC2012segmentation paring our al-ternative Bbox-Rect(54.2%)and Bbox-Seg(62.2%)meth-ods,we see that simple foreground-background segmenta-tion provides much better segmentation masks for DCNN training than using the raw bounding boxes.BoxSup does 2.4%better,however it employs the MCG segmentation proposal mechanism[1],which has been trained with pixel-annotated data on the PASCAL train set;it thus indirectly relies on pixel-level annotations.When we also have access to pixel-level annotated im-ages,our performance improves to66.6%(1.4k strong annotations)or69.0%(2.9k strong annotations).In this semi-supervised setting we outperform BoxSup(66.6%vs.66.2%with1.4k strong annotations),although we do not use MCG.Interestingly,Bbox-EM-Fixed improves over Bbox-Seg as we add more strong annotations,and it per-forms1.0%better(69.0%vs.68.0%)with2.9k strong an-notations.This shows that the E-step of our EM algorithm can estimate the object masks better than the foreground-background segmentation pre-processing step when enough pixel-level annotated images are available.Comparing with Sec.4.3,note that2.9k strong+9k image-level annotations yield68.5%(Table2),while2.9k strong+9k bounding box annotations yield69.0%(Ta-ble3).Thisﬁnding suggests that bounding box annotations add little value over image-level annotations when a sufﬁ-cient number of pixel-level annotations is also available.Method#Strong COCO#Weak COCO val IOU PASCAL-only--67.6EM-Fixed(Semi)-123,28767.7Cross-Joint(Semi)5,000118,28770.0Cross-Joint(Strong)5,000-68.7Cross-Pretrain(Strong)123,287-71.0Cross-Joint(Strong)123,287-71.7 Table5.VOC2012val performance using strong annotations for all10,582train aug PASCAL images and a varying number of strong and weak MS-COCO annotations(Sec.4.5).Method test IOUMSRA-CFM[8]61.8FCN-8s[25]62.2Hypercolumn[17]62.6TTI-Zoomout-16[27]64.4DeepLab-CRF-LargeFOV[5]70.3BoxSup(Semi,with weak COCO)[9]71.0DeepLab-CRF-LargeFOV(Multi-scale net)[5]71.6Oxford TVG CRF RNN VOC[41]72.0Oxford TVG CRF RNN COCO[41]74.7Cross-Pretrain(Strong)72.7Cross-Joint(Strong)73.0Cross-Pretrain(Strong,Multi-scale net)73.6Cross-Joint(Strong,Multi-scale net)73.9Table6.VOC2012test performance using PASCAL and MS-COCO annotations(Sec.4.5).4.5.Exploiting Annotations Across Datasets Validation results We present experiments leveraging the 81-label MS-COCO dataset as an additional source of data in learning the DeepLab model for the21-label PASCAL VOC2012segmentation task.We consider three scenarios:•Cross-Pretrain(Strong):Pre-train DeepLab on MS-COCO,then replace the top-level network weights and ﬁne-tune on Pascal VOC2012,using pixel-level anno-tation in both datasets.•Cross-Joint(Strong):Jointly train DeepLab on Pas-cal VOC2012and MS-COCO,sharing the top-level network weights for the common classes,using pixel-level annotation in both datasets.•Cross-Joint(Semi):Jointly train DeepLab on Pascal VOC2012and MS-COCO,sharing the top-level net-work weights for the common classes,using the pixel-level labels from PASCAL and varying the number of pixel-and image-level labels from MS-COCO.In all cases we use strong pixel-level annotations for all 10,582train aug PASCAL images.We report our results on the PASCAL VOC2012val in Table5,also including for comparison our best PASCAL-only67.6%result exploiting all10,582strong annotations as a baseline.When we employ the weak MS-COCO an-notations(EM-Fixed(Semi))we obtain67.7%IOU,which does not improve over the PASCAL-only baseline.How-ever,using strong labels from5,000MS-COCO images (4.0%of the MS-COCO dataset)and weak labels from the remaining MS-COCO images in the Cross-Joint(Semi) semi-supervised scenario yields70.0%,a signiﬁcant2.4%boost over the baseline.This Cross-Joint(Semi)result is also1.3%better than the68.7%performance obtained us-ing only the5,000strong and no weak annotations from MS-COCO.As expected,our best results are obtained by using all123,287strong MS-COCO annotations,71.0%for Cross-Pretrain(Strong)and71.7%for Cross-Joint(Strong). We observe that cross-dataset augmentation improves by 4.1%over the best PASCAL-only ing only a small portion of pixel-level annotations and a large portion of image-level annotations in the semi-supervised setting reaps about half of this beneﬁt.Test results We report our PASCAL VOC2012test re-sults in Table6.We include results of other leading models from the PASCAL leaderboard.All our models have been trained with pixel-level annotated images on the PASCAL trainval aug and the MS-COCO2014trainval datasets.Methods based on the DCNN+CRF pipeline of DeepLab-CRF[5]are the most competitive,with perfor-mance surpassing70%,even when only trained on PAS-CAL data.Leveraging the MS-COCO annotations brings about2%improvement.Our top model yields73.9%,using the multi-scale network architecture of[5].Also see[41], which also uses joint PASCAL and MS-COCO training,and further improves performance(74.7%)by end-to-end learn-ing of the DCNN and CRF parameters.4.6.Qualitative Segmentation ResultsIn Fig.6we provide visual comparisons of the results obtained by the DeepLab-CRF model learned with some of the proposed training methods.5.ConclusionsThe paper has explored the use of weak or partial anno-tation in training a state of art semantic image segmenta-tion model.Extensive experiments on the challenging PAS-CAL VOC2012dataset have shown that:(1)Using weak annotation solely at the image-level seems insufﬁcient to train a high-quality segmentation model.(2)Using weak bounding-box annotation in conjunction with careful seg-mentation inference for images in the training set sufﬁces to train a competitive model.(3)Excellent performance is obtained when combining a small number of pixel-level an-notated images with a large number of weakly annotated images in a semi-supervised setting,nearly matching the results achieved when all training images have pixel-level annotations.(4)Exploiting extra weak or strong annota-tions from other datasets can lead to large improvements. AcknowledgmentsThis work was partly supported by ARO62250-CS,and NIH5R01EY022247-03.We also gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research.。

基于深度学习的图像检索算法研究

基于深度学习的图像检索算法研究第一章：绪论
图像检索是计算机视觉领域的重要研究方向之一，其目标是在庞大的图像集合中，根据用户需要高效地检索相关图片。

基于深度学习的图像检索算法应运而生，相比传统的图像检索算法，其具有更好的性能和可扩展性。

本文将对基于深度学习的图像检索算法进行研究和分析。

第二章：传统的图像检索算法
2.1 基于颜色直方图的图像检索算法
2.2 基于SIFT的图像检索算法
2.3 基于BoW模型的图像检索算法
2.4 基于深度学习的图像检索算法的优势和不足
第三章：基于深度学习的图像检索算法
3.1 深度学习算法概述
3.2 模型训练
3.2.1 数据准备
3.2.2 卷积神经网络的基本结构
3.2.3 训练过程
3.3 特征提取和相似度匹配
3.3.1 特征提取
3.3.2 相似度匹配
3.3.3 相似度度量
3.4 实验结果和性能分析
3.4.1 实验数据集
3.4.2 实验结果
3.4.3 性能分析
第四章：本文提出的基于深度学习的图像检索算法4.1 算法框架
4.2 数据准备
4.3 模型训练
4.4 特征提取和相似度匹配
4.5 实验结果和性能分析
第五章：总结与展望
本文介绍了基于深度学习的图像检索算法的研究现状和发展趋势，并通过实验对其性能进行了评估和比较。

基于深度学习的图像检索算法相比传统算法具有更好的性能和可扩展性，但面临着实现复杂和对数据量的要求高的问题。

未来的研究方向可以从优化算法的效率、改进模型的准确性、扩展数据集的规模等方面进行探索，以便更好地应用于实际应用中。

《稀疏角CT重建的算法研究》范文

《稀疏角CT重建的算法研究》篇一一、引言计算机断层扫描（Computed Tomography, CT）技术是现代医学影像诊断的重要手段之一。

然而，传统的CT重建算法在处理稀疏角（Sparse-angle）CT数据时，往往面临重建图像质量不高、噪声大、伪影明显等问题。

因此，研究稀疏角CT重建的算法具有重要的理论和实践意义。

本文旨在探讨稀疏角CT重建的算法研究，以期为相关领域的研究提供参考。

二、稀疏角CT重建的挑战稀疏角CT是指在进行CT扫描时，由于某些角度的投影数据缺失或不足，导致重建图像的质量受到严重影响。

这种问题主要源于以下几个方面：1. 扫描角度不全面：由于某些角度的投影数据缺失，导致重建图像的细节和结构信息丢失。

2. 噪声和伪影：由于数据稀疏，重建图像中的噪声和伪影更为明显。

3. 计算复杂度：稀疏角CT数据的处理需要更高的计算资源和算法复杂度。

三、稀疏角CT重建的算法研究针对稀疏角CT重建的问题，国内外学者提出了多种算法。

下面将重点介绍几种典型的算法及其原理：1. 迭代重建算法迭代重建算法是一种常用的CT重建算法，可以通过多次迭代来提高重建图像的质量。

在稀疏角CT重建中，迭代重建算法可以通过增加迭代次数和优化迭代策略来提高图像质量。

此外，还可以结合正则化技术来抑制噪声和伪影。

2. 压缩感知算法压缩感知算法是一种基于信号稀疏性的重建算法，可以通过对稀疏角CT数据进行压缩感知来提高重建图像的质量。

该算法通过设计合适的测量矩阵和重构算法，从少量的投影数据中恢复出高质量的图像。

3. 深度学习算法深度学习算法在稀疏角CT重建中也得到了广泛应用。

通过训练深度神经网络模型，可以从有限的投影数据中学习到更多的图像信息，从而提高重建图像的质量。

目前，基于生成对抗网络（GAN）的CT重建算法已成为研究热点。

四、实验与分析为了验证上述算法的有效性，我们进行了相关实验。

实验结果表明，迭代重建算法、压缩感知算法和深度学习算法均能在一定程度上提高稀疏角CT重建图像的质量。

HSfMHybridStructure-from-Motion《学习笔记》

HSfMHybridStructure-from-Motion《学习笔记》HSfM: Hybrid Structure-from-MotionAbstr a c t为了估计初始的相机位姿，SFM⽅法可以被概括为增量式或全局式。

虽然增量系统在鲁棒性和准确性⽅⾯都有所进步，在效率上仍是其主要的挑战。

为了解决这个问题，全局重建系统通过对极⼏何图中同时估计所有相机的位姿，但它对外点很敏感。

在这个⼯作⾥，我们提出了⼀个混合式sfm⽅法在统⼀的框架下解决效率，准确性和鲁棒性的问题。

进⼀步来说，我们提出⼀种社区化的⾃适应的平均⽅法，⾸先以全局⽅式估计相机旋转，然后基于这些估计的摄像机旋转，以增量式的⽅法去计算相机中⼼。

⼤量的实验表明，在计算效率⽅⾯，我们的混合⽅法的执⾏效果与许多最新的全局SfM⽅法相似或更好，同时与其他两种最新的状态相⽐，实现了相似的重构精度和鲁棒性渐进的SfM⽅法。

Intro duc tio nSFM技术是指通过⼀系列图⽚估计三维场景结构和相机位姿。

它通常包含三个模块，特征提取和匹配，初始相机位姿估计和BA。

根据初始相机姿势的估算⽅式不同，sfm可以被笼统的分为两类：增量式和全局式。

对于增量式⽅法，⼀种⽅法是选择⼀些种⼦图像进⾏初始重建，然后重复添加新图像。

另⼀种⽅法是⾸先将图像聚集成原⼦模型，然后重建每个原⼦模型，然后逐步合并它们。

可以说，增量⽅式是3D重建最流⾏的策略。

然⽽，这种⽅法对初始种⼦模型重建和模型⽣成⽅式很敏感。

另外，重建误差随着迭代的进⾏⽽累积。

对于⼤规模的场景重建，重建的结构可能会发⽣场景漂移。

此外，反复执⾏耗时的捆绑调整BA，这⼤⼤降低了系统的稳定性和效率。

为了解决这些不⾜，全局sfm⽅法在近些年变得更加流⾏。

对于全局式⽅法，初始相机的位姿同时从对极⼏何图像（EG）估计，图的顶点对应图像，边链接匹配的图像对，BA只执⾏⼀次，这在系统效率和可扩展性⽅⾯带来了更⼤的潜⼒。

全局摄像机位姿估计的通⽤pipeline包括两个步骤：旋转平均和位移平均。

肿瘤细胞自动识别算法的研究与应用

肿瘤细胞自动识别算法的研究与应用近年来，随着计算机技术的不断发展，计算机视觉技术也得到了广泛应用。

在医学领域中，计算机视觉技术被广泛应用于肿瘤细胞自动识别算法的研究与应用。

肿瘤细胞自动识别算法是一种利用计算机技术进行肿瘤诊断和研究的方法，对于提高肿瘤的诊断准确性和治疗效果有很大的帮助。

一、肿瘤细胞自动识别算法的意义人工智能的快速发展和计算机视觉技术的不断进步，使得肿瘤细胞自动识别算法成为了一种重要的手段。

肿瘤细胞自动识别算法可以对大量的肿瘤细胞图像进行快速分析和处理，减少人工判读对诊断的影响，提高诊断的准确性和效率。

同时，还可以探索肿瘤的生长规律和发展规律，加深对肿瘤病理学的研究和认识。

二、肿瘤细胞自动识别算法的研究肿瘤细胞自动识别算法的研究是一个复杂的过程，需要依靠大量的数据和计算机算法。

具体的研究流程分为以下几个步骤。

1. 数据获取与处理肿瘤细胞自动识别算法的研究需要大量的肿瘤细胞图像，这些图像可以通过肿瘤病理学的方法获得。

在获取图像后，还需要对图像进行处理，包括去除噪声、调整亮度和对比度等，以便更好地识别细胞。

2. 特征提取与选择在肿瘤细胞图像中，每个细胞都有各自的特征。

通过对这些特征的提取和选择，可以识别细胞的不同类型和状态。

特征提取的过程需要选取合适的算法，如SIFT、SURF等。

3. 细胞分类与边缘识别通过特征提取和选择，可以对细胞进行分类识别，并对其边缘进行识别。

细胞分类和边缘识别是整个算法的核心部分，其准确性直接影响到算法的整体性能。

4. 算法优化和实现在完成以上步骤后，还需要对算法进行优化，以提高准确性和效率，同时实现算法的工程化应用。

三、肿瘤细胞自动识别算法的应用肿瘤细胞自动识别算法的应用在临床诊断和研究中具有广泛的应用价值。

1. 临床诊断在实际的临床诊断中，医生需要针对患者的身体情况对肿瘤病理学进行细致的观察和分析。

而肿瘤细胞自动识别算法可以对大量的图像数据进行快速处理和诊断，大大缩短了患者等待的时间，还可以提高诊断的准确性和效率。

《2024年稀疏角CT重建的算法研究》范文

《稀疏角CT重建的算法研究》篇一一、引言近年来，随着计算机技术的发展和医疗设备精度的提升，稀疏角CT（Computed Tomography）重建技术在医学诊断和放射学研究中发挥着越来越重要的作用。

由于其在处理三维数据时的卓越表现，稀疏角CT成像已成为现代医学诊断中不可或缺的技术手段。

本文将着重对稀疏角CT重建的算法进行深入研究，并分析其优势与挑战。

二、稀疏角CT重建算法概述稀疏角CT重建算法是利用计算机断层扫描（CT）获取的投影数据，通过数学计算方法重构出目标体的内部结构图像。

该算法主要分为两大类：解析法和迭代法。

解析法基于傅里叶变换等数学原理，通过直接计算投影数据的反投影来重建图像。

迭代法则通过不断迭代优化，逐步逼近真实图像。

三、稀疏角CT重建算法的原理及特点1. 解析法：解析法利用投影数据的傅里叶变换进行反投影，计算速度快，但重建图像的分辨率和信噪比相对较低。

此外，解析法对于噪声和伪影的抑制能力较弱，因此在实际应用中往往需要与其他技术结合使用。

2. 迭代法：迭代法通过不断迭代优化，逐步逼近真实图像。

其优点在于可以更好地处理噪声和伪影，提高图像的分辨率和信噪比。

然而，迭代法的计算量较大，所需时间较长。

目前，研究人员正致力于开发优化算法和加速技术，以提高迭代法的计算效率。

四、稀疏角CT重建算法的应用与发展趋势稀疏角CT重建算法在医学诊断、放射学研究和工业检测等领域具有广泛应用。

随着计算机技术的不断发展和算法的不断优化，稀疏角CT重建技术将更加成熟和高效。

未来，该技术将朝着更高分辨率、更低噪声、更短扫描时间等方向发展。

同时，随着人工智能技术的引入，稀疏角CT重建算法将更加智能化和自动化。

五、挑战与展望尽管稀疏角CT重建算法在医学诊断和放射学研究中取得了显著成果，但仍面临诸多挑战。

首先，如何进一步提高图像的分辨率和信噪比是当前研究的重点。

其次，如何更好地处理噪声和伪影，提高图像的准确性也是亟待解决的问题。

异构协同设计环境中特征模型的提取与重建

异构协同设计环境中特征模型的提取与重建
蔡贤涛;何发智;李小霞;黄智勇;陈昕
【期刊名称】《中国图象图形学报》
【年(卷),期】2009(014)012
【摘要】以基于过程恢复的特征数据交换框架为背景,研究了异构特征模型的提取与重建这一关键问题,包括一阶特征信息的提取,二阶特征信息的提取以及模型的重建.实验结果验证了本方法的合理性,从而有效提高和补充了现有方法的不足,把特征数据交换技术向前推进了一步.
【总页数】4页(P2615-2618)
【作者】蔡贤涛;何发智;李小霞;黄智勇;陈昕
【作者单位】武汉大学计算机科学与技术学院,武汉,430072;武汉大学计算机科学与技术学院,武汉,430072;武汉大学计算机科学与技术学院,武汉,430072;武汉大学计算机科学与技术学院,武汉,430072;武汉大学计算机科学与技术学院,武
汉,430072
【正文语种】中文
【中图分类】TP301.6
【相关文献】
1.远程协同设计中的特征提取与重建 [J], 文义
2.一种新的协同设计环境中访问控制模型研究 [J], 沈国强;姚丽华;张国煊;姜涛
3.网格协同设计环境中的任务调度机制 [J], 杨格兰;左伟明
4.协同设计环境中任务分解与调度的研究 [J], 金黎黎;孔令富
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因版权原因，仅展示原文概要，查看原文内容请购买。

基于 C－V 模型的木材缺陷重建图像特征提取1）

基于 C－V 模型的木材缺陷重建图像特征提取1）刘嘉新;吴彤;王克奇【摘要】以含有裂纹的柳木木材、含有空洞的椴树木材为样本，实验研究了基于细胞反演法的木材重建图像的特征提取。

首先，利用细胞的反向投影方法将木材断层图像进行重建后，对所得图像进行开启化平滑处理；然后，利用C－V模型获取木材断层图像中缺陷部分特征；最后，将测得的木材缺陷面积值与真实值进行比较。

结果表明：利用C－V模型可以将重建的缺陷图像特征准确的分割出来，实现了对此细胞反演法重建质量的评价；为木材缺陷重建图像的质量评估提供了方法，为今后木材缺陷监测仪的设计提供参考。

%We proposed a feature extraction algorithm of wood reconstructed images based on the cell -inversion method .Firstly, the cell-inversion method is used to reconstruct the wood tomographic image , and open smooth filtering is implemented to the results.Secondly, C-V model is used to obtain the wood defect features from the reconstructed image .Finally, a com-parison is made between measured and real values of the defect area which provide a quality evaluation of the cell inversion algorithm.Willow samples with cracks and Linden samples with cavity were used for experiment .The C-V model can seg-ment the defect image and extract features from the reconstructed image .This research will provide a quality evaluation method of wood defect image reconstruction and the future design of wood defectmonitor .【期刊名称】《东北林业大学学报》【年(卷),期】2015(000)012【总页数】4页(P78-81)【关键词】木材缺陷;缺陷特征;C-V模型【作者】刘嘉新;吴彤;王克奇【作者单位】东北林业大学，哈尔滨，150040;东北林业大学，哈尔滨，150040;东北林业大学，哈尔滨，150040【正文语种】中文【中图分类】S781.5应力波图像重建技术，是在不破坏木材本身的前提下将木材内部断层进行重建，从而获取木材的内部状况信息;木材无损检测技术是多样化的，而应力波检测法无疑是木材的无损检测技术中最值得深入研究的技术［1］。

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Dmitrii Marin∗ , Yuchen Zhong∗ , Maria Drangova∗† , Yuri Boykov∗ ∗ University of Western Ontario, Canada † Robarts Research Institute, Canada
dmitrii.a.marin@ yzhong.cs@ mdrangova@robarts.ca yuri@csd.uwo.ca
the curvature of the object boundary. Moreover, we do not assume that the boundary of a thin structure (e.g. vessel or road) is given. Detection variables are estimated simultaneously with the center-line. This paper proposes a general energy formulation and an optimization algorithm for detection and subpixel delineation of thin structures based on curvature regularization. Curvature is a natural regularizer for thin structures and it has been widely explored in the past. In the context of image segmentation with second-order smoothness it was studied by [31, 37, 32, 5, 14, 28, 25]. It is also a popular second-order prior in stereo or multi-view-reconstruction [20, 27, 40]. Curvature has been used inside connectivity measures for analysis of diffusion MRI [24]. Curvature is also widely used for inpainting [3, 7] and edge completion [13, 39, 2]. For example, stochastic completion ﬁeld technique in [39, 24] estimates probability that a completed/extrapolated curve passes any given point assuming it is a random walk with bias to straight paths. Note that common edge completion methods use existing edge detectors as an input for the algorithm. In contrast to these prior works, this paper proposes a
Abstract
Many applications in vision require estimation of thin structures such as boundary edges, surfaces, roads, blood vessels, neurons, etc. Unlike most previous approaches, we simultaneously detect and delineate thin structures with sub-pixel localization and real-valued orientation estimation. This is an ill-posed problem that requires regularization. We propose an objective function combining detection likelihoods with a prior minimizing curvature of the centerlines or surfaces. Unlike simple block-coordinate descent, we develop a novel algorithm that is able to perform joint optimization of location and detection variables more effectively. Our lower bound optimization algorithm applies to quadratic or absolute curvature. The proposed early vision framework is sufﬁciently general and it can be used in many higher-level applications. We illustrate the advantage of our approach on a range of 2D and 3D examples.
In proceedings of “International Conference on Computer Vision” (ICCV), Santiago, Chile, Dec. 2015
p.1
Tth Curvature Regularization
1. Introduction
A large amount of work in computer vision is devoted to estimation of structures like edges, center-lines, or surfaces for ﬁtting thin objects such as intensity boundaries, blood vessels, neural axons, roads, or point clouds. This paper is focused on the general concept of a center-line, which could be deﬁned in different ways. For example, Canny approach to edge detection implicitly deﬁnes a center-line as a “ridge” of intensity gradients [6]. Standard methods for shape skeletons deﬁne medial axis as singularities of a distance map from a given object boundary [35, 34]. In the context of thin objects like edges, vessels, etc, we consider a center-line to be a smooth curve minimizing orthogonal projection errors for the points of the thin structure. We study curvature of the center-line as a regularization criteria for its inference. In general, curvature is actively discussed in the context of thin structures. For example, it is well known that curvature of the object boundary has signiﬁcant effect on the medial axis [17, 35]. In contrast, we are directly concerned with curvature of the center-line, not
In proceedings of “International Conference on Computer Vision” (ICCV), Santiago, Chile, Dec. 2015
p.2
general low-level regularization framework for detecting thin structures with accurate estimation of location and orientation. In contrast to [39, 13, 24] we explicitly minimize the integral of curvature along the estimated thin structure. Unlike [12] we do not use curvature for grouping predetected thin structures, we use curvature as a regularizer during the detection stage. Related work: Our regularization framework is based on the curvature estimation formula proposed by Olsson et al. [26, 27] in the context of surface ﬁtting to point clouds for multi-view reconstruction, see Fig.2(a). One assumption in [26, 27] is that the data points are noisy readings of the surface. While the method allows outliers, their formulation is focused on estimation of local surface patches. Our work can be seen as a generalization to detection problems where majority of the data points, e.g. image pixels in Fig.2(c), are not within a thin structure. In addition to local tangents, our method estimates probability that the point is a part of the thin structure. Section 2 discusses in details this and other signiﬁcant differences from the formulation in [26, 27]. Assuming pi and pj are neighboring points on a thin structure, e.g. a curve, Olsson et al. [26] evaluate local curvature as follows. Let li and lj be the tangents to the curve at points pi and pj . Then the authors propose the following approximation for the absolute curvature |κ(li , lj )| = ||li − pj || + ||lj − pi || ||pi − pj ||