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检索条件"任意字段=5th International Conference on Scale-Space and PDE Methods in Computer Vision"

共 181 条记录，以下是61-70 订阅

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Supervised High-Dimension Endecoder Net: 3D End to End Prediction Network for Mark-less Human Pose Estimation from Single Depth Map 5

Supervised High-Dimension Endecoder Net: 3D End to End Predi...

引用

5th international conference on Control, Automation and Robotics (ICCAR)

作者： Shen, Li Chen, Ying Jiangnan Univ Key Lab Adv Control Light Proc Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781728133263

Most of the existing deep learning-based methods for Mark-less human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of human body joints via 2D convolutional neural networks (CNNs). But depth map is intrinsically 3D data, treat it as 2D images will distort the shape of the actual object through projection from 3D to 2D space, and compels the network to perform perspective distortion-invariant estimation. Moreover, directly regressing 3D coordinates from a 2D image is a highly nonlinear mapping, which causes difficulty in learning procedure. To overcome these problems, a module called Supervised Endecoder is proposed to process 3D convolution data, which can also be stacked through series connection to adapt different size of dataset. Based on the module, a network called Supervised High Dimension Endecoder Network is designed, which can be used to predict key points of markless human in a single depth map in 3D space. Experiments show improved prediction accuracy compared to the state-of-the-art approaches.

关键词： computer vision deep learning 3D-CNN pose estimation depth map

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pde based shape from specularities

PDE based shape from specularities

引用

4th international conference on scale space methods in computer vision, scale space 2003

作者： Solem, Jan Erik Aanæs, Henrik Heyden, Anders School of Technology and Society Malmö University Sweden Informatics and Mathematical Modelling Technical University of Denmark Denmark

ISBN: (纸本)354040368X

When reconstructing surfaces from image data, reflections on specular surfaces are usually viewed as a nuisance that should be avoided. In this paper a different view is taken. Noting that such reflections contain information about the surface, this information could and should be used when estimating the shape of the surface. Specifically, assuming that the position of the light source and the cameras (i.e. the motion) are known, the reflection from a specular surface in a given image constrain the surface normal with respect to the corresponding camera. Here the constraints on the normals, given by the reflections, are used to formulate a partial differential equation (pde) for the surface. A smoothness term is added to this pde and it is solved using a level set framework, thus giving a "shape from specularity" approach. the structure of the pde also allows other properties to be included, e.g. the constraints from pde based stereo. the proposed pde does not fit naturally into a level set framework. To address this issue it is proposed to couple a force field to the level set grid. To demonstrate the viability of the proposed method it has been applied successfully to synthetic data. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Cameras

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Lossless pde-based Compression of 3D Medical Images 8th

Lossless PDE-based Compression of 3D Medical Images

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8th international conference on scale space and Variational methods in computer vision, SSVM 2021

作者： Jumakulyyev, Ikram Schultz, thomas University of Bonn Bonn Germany

ISBN: (纸本)9783030755485

Inpainting with Partial Differential Equations (pdes) has previously been used as a basis for lossy image compression. For medical images, lossless compression is often considered to be safer, given that even subtle details could be diagnostically relevant. In this work, we introduce a pde-based codec that achieves competitive compression rates for lossless image compression. It is based on coding the differences between the original image and its pde-based reconstruction. these differences often have lower entropy than the original image, and can therefore be coded more efficiently. We optimize this idea via an iterative reconstruction scheme, and a separate coding of empty space, which takes up a considerable fraction of the field of view in many 3D medical images. We demonstrate that our pde-based codec compares favorably to previously established lossless codecs. We also investigate the individual benefit from each ingredient of our codec on multiple examples, explore the effect of using homogeneous, edge enhancing, and fourth-order anisotropic diffusion, and discuss the choice of contrast parameters. © 2021, Springer Nature Switzerland AG.

关键词： Image compression

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Active visual navigation using non-metric structure

Active visual navigation using non-metric structure

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Proceedings of the 5th international conference on computer vision

作者： Beardsley, Paul A. Reid, Ian D. Zisserman, Andrew Murray, David W. Univ of Oxford Oxford United Kingdom

this paper demonstrates a method of using non-metric visual information derived from an uncalibrated active vision system to navigate an autonomous vehicle through free-space regions detected in a cluttered environment. the structure of 3-space is recovered modulo an affine transformation using an uncalibrated active stereo head carried by the vehicle. the plane at infinity, necessary for recovering affine structure from projective structure, is found in a novel manner by making controlled rotations of the head. the structure is composed of 3D points obtained by detecting and matching image corners through the stereo image sequence. Considerable care has been taken to ensure that the processing is reliable, robust and automatic. Driveable regions are determined from the projection of the affine structure onto a plane parallel to the ground determined using projective constructs. Two methods of negotiating the regions are explored. the first introduces metric information to allow control of a Euclidean vehicle. the second uses visual servoing of the active head to navigate in the affinely described free-space regions.

关键词： computer vision

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Cross-Layer Feature Fusion vision Transformer for Fine-Grained Visual Classification 5

Cross-Layer Feature Fusion Vision Transformer for Fine-Grain...

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5th international conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Zhang, Ke Cao, Jian Li, Hai-Sheng Cai, Qiang School of Computer Science and Engineering Beijing Technology and Business University Beijing China

ISBN: (纸本)9798350393521

Fine-grained visual classification is an important task in the field of computer vision, where large intra-class differences and small inter-class differences are one of the main challenges. Traditional methods require manual feature extraction and do not handle small differences between classes well, making them unsuitable for large-scale and high-dimensional image data. In recent years, the vision Transformer (ViT) has achieved good results in general image recognition tasks, and its self-attentive mechanism makes it suitable for fine-grained visual classification(FGVC). By effectively capturing fine features in the image through the self-attention mechanism, the need for manual feature extraction is avoided. To effectively mine cross-layer features, the Cross-Layer Fine-grained Feature module(CLFF) is proposed, and the data augmentation method of attention cropping and erasure is introduced to improve the performance of fine-grained classification. We conducted experiments on the NABirds, CUB-200-2011, and Stanford Dogs datasets, and the results show that our method outperforms current state-of-the-art methods in terms of accuracy. © 2023 IEEE.

关键词： Feature extraction

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DSFi-Net: Action recognition with double-stream feature fusion network based on WiFi CSI 5

DSFi-Net: Action recognition with double-stream feature fusi...

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5th international conference on computer vision and Data Mining, ICCVDM 2024

作者： Pan, Heyu Wang, Weidong Pang, Hui School of Computer Science Northeast Electric Power University Jilin China

ISBN: (纸本)9781510682863

In the current wireless communication system, WiFi-based wireless sensing is considered as one of the most promising wireless sensing technologies with its extensive deployment and many applications. However, the existing models and methods have some problems, such as low recognition accuracy and poor robustness. To solve the problem that the extraction of human activity information as features are affected by environmental noise and human body posture, and does not take into account the different particle size effects of different actions in different parts of the human body. this paper proposes an action component extraction method based on CSI signal decomposition to extract dynamic components independent of environment and body posture. Finally, a fusion network based on two-stream feature extraction is designed to integrate multi-scale spatial features and temporal features into multi-space temporal features. A large number of experiments are carried out to evaluate the performance of the proposed method, and the experimental results show that the proposed method has a high recognition accuracy. © 2024 SPIE.

关键词： Wi-Fi

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Research on Compliant Control of Collaborative Robots Based on Fuzzy Iterative Algorithm 5

Research on Compliant Control of Collaborative Robots Based ...

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5th international conference on Robotics and computer vision, ICRCV 2023

作者： Sun, Zejun Zhang, Tao Peng, Tianchen Shanghai Engineering Research Center of CNC Equipment Shanghai Electric Group Central Academe Shanghai China

ISBN: (纸本)9798350326369

In human-robot collaboration, the control system must maintain sufficient stability and high precision and ensure robots have good compliance and quick response. In this paper, research is carried out on the compliance control of a collaborative robot with six rotary joints, so that the robot can quickly and accurately realize position tracking and ensure the compliance of its movements. the impedance control algorithm based on joint space was proposed to realize the variable impedance dragging function of the robot. the kinematics model of the robot is established by using Rodrigues' rotation formula, which simplifies the derivation of the kinematics. the fuzzy algorithm has good nonlinearity and can reduce the convergence times of iterations. Based on the dynamics of the robot, the fuzzy iterative algorithm is designed to correct the position error of the robot in trajectory tracking. During the tracking process of the robot, the effectiveness and position-tracking ability of the algorithm are verified. © 2023 IEEE.

关键词： Iterative methods

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Application research of multi-category commodity classification algorithm based on vision transformer 5

Application research of multi-category commodity classificat...

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5th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Shen, Hongzhang Yang, Chenghui Northwest Minzu University College of Electrical Engineering Lanzhou China

ISBN: (纸本)9798331533991

With the rapid development of technology, people's shopping methods have also undergone earth-shaking changes. In order to quickly respond to and meet customers' increasingly diverse online shopping needs, multi-category product classification technology has therefore become a crucial technology to improve user shopping experience and e-commerce platform operation efficiency, quickly and accurately classifying products from massive amounts of product data. Identifying products that users like has become a pain point that e-commerce platform systems urgently need to solve. In recent years, vision Transformer (ViT) has demonstrated excellent performance in the field of image classification. this paper aims to propose a multi-category product classification algorithm based on ViT to improve the classification accuracy on large-scale product image data sets. Compared with traditional convolutional neural networks (CNN), ViT uses the self-attention mechanism in the Transformer architecture to better capture long-distance dependencies in images, and the recognition effect on large-scale data sets is particularly significant. In order to fully verify the superiority of the proposed method, we constructed a data set containing tens of thousands of product images of different categories, and conducted full experimental analysis based on this data set. Experimental results show that the model achieves significantly better performance than traditional methods on the standard test set. © 2024 IEEE.

关键词： Convolutional neural networks

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VRVideos: A flexible pipeline for Virtual Reality Video Creation 5

VRVideos: A flexible pipeline for Virtual Reality Video Crea...

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5th IEEE international conference on Artificial Intelligence and Virtual Reality (IEEE AIVR)

作者： Dickson, Anthony Shanks, Jeremy Ventura, Jonathan Knott, Alistair Zollmann, Stefanie Univ Otago Dept Comp Sci Dunedin New Zealand Calif Polytech State Univ San Luis Obispo Dept Comp Sci & Software Engn San Luis Obispo CA USA Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9781665457255

Recent advances in Neural Radiance Field (NeRF)based methods have enabled high-fidelity novel view synthesis for video with dynamic elements. However, these methods often require expensive hardware, take days to process a second-long video and do not scale well to longer videos. We create an end-to-end pipeline for creating dynamic 3D video from a monocular video that can be run on consumer hardware in minutes per second of footage, not days. Our pipeline handles the estimation of the camera parameters, depth maps, 3D reconstruction of dynamic foreground and static background elements, and the rendering of the 3D video on a computer or VR headset. We use a state-of-the-art visual transformer model to estimate depth maps which we use to scale COLMAP poses and enable RGB-D fusion with estimated depth data. In our preliminary experiments, we rendered the output in a VR headset and visually compared the method against ground-truth datasets and state-of-the-art NeRF-based methods.

关键词： Virtual Reality Image Processing computer vision Reconstruction Artificial Intelligence vision and Scene nderstanding

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Adaptive Fusion Dual-Branch Convolutional Surface Reconstruction 5

Adaptive Fusion Dual-Branch Convolutional Surface Reconstruc...

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5th international conference on Robotics and computer vision, ICRCV 2023

作者： Meng, Bo Shi, Xuhua Li, Shanzhi Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (纸本)9798350326369

Implicit neural networks based on point convolution and voxel convolution have been successfully used for point cloud surface reconstruction. this paper proposes an adaptive fusion dual-branch convolutional occupancy network, focusing on the inefficiency of searching local surface based on point convolution technology in reconstruction and the roughness of generated volumetric features based on voxel convolution technology. Dual-branch convolution is used during the encoding stage of the point cloud to extract informative volumetric features. Specifically, the point convolution branch calculates the features of each point in the input point cloud, and the voxel convolution branch calculates the features of each voxel in the regular space of the point cloud. the features of the two branches are then combined using an adaptive fusion module. We performed trilinear interpolation on the feature volume. Experiments on the scene-level Synthetic Rooms dataset and the object-level ShapeNet dataset show that our approach outperforms other methods on most classical metrics. © 2023 IEEE.

关键词： Convolution

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