发布求助
文献互助 智能选刊 最新文献

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops最新文献

筛选
英文 中文
Synchronization and Calibration of a Camera Network for 3D Event Reconstruction from Live Video 基于实时视频的三维事件重建摄像机网络的同步与标定
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.338
Sudipta N. Sinha, M. Pollefeys
{"title":"Synchronization and Calibration of a Camera Network for 3D Event Reconstruction from Live Video","authors":"Sudipta N. Sinha, M. Pollefeys","doi":"10.1109/CVPR.2005.338","DOIUrl":"https://doi.org/10.1109/CVPR.2005.338","url":null,"abstract":"We present an approach for automatic reconstruction of a dynamic event using multiple video cameras recording from different viewpoints. Our approach recovers all the necessary information by analyzing the motion of the silhouettes in the multiple video streams. The first step consists of computing the calibration and synchronization for pairs of cameras. We compute the temporal offset and epipolar geometry using an efficient RANSAC-based algorithm to search for the epipoles as well as for robustness. In the next stage the calibration and synchronization for the complete camera network is recovered and then refined through maximum likelihood estimation. Finally, a visual hull algorithm is used to the recover the dynamic shape of the observed object.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"62 1","pages":"1196"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86490595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Geodesic Computation for Adaptive Remeshing 自适应网格划分的测地线计算
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.169
G. Peyré, L. Cohen
{"title":"Geodesic Computation for Adaptive Remeshing","authors":"G. Peyré, L. Cohen","doi":"10.1109/CVPR.2005.169","DOIUrl":"https://doi.org/10.1109/CVPR.2005.169","url":null,"abstract":"This video presents an application of geodesic computation on 3D meshes to surface remeshing. The connectivity of the resulting mesh is computed using a geodesic Delaunay triangulation of the sampling points. The user can provide a speed function to conform the remeshing to various contraints such as curvature variation or texture gradient. This remeshing method is fast thanks to the use of the fast marching algorithm. It is simple to implement, robust and can serve as a basis building block for further processing of the surface such as segmentation or flattening.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"1 1","pages":"1193"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77251632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Interactive Pinpoint Image Object Removal 交互式精确图像对象去除
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.193
F. Nielsen, R. Nock
{"title":"Interactive Pinpoint Image Object Removal","authors":"F. Nielsen, R. Nock","doi":"10.1109/CVPR.2005.193","DOIUrl":"https://doi.org/10.1109/CVPR.2005.193","url":null,"abstract":"We present a novel interactive system and its user interface for removing objects in digital pictures. Our system consists of two components: (i) (partially supervised/automatic) image segmentation, and (ii) (guided) texture synthesis.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"14 1","pages":"1191"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87783976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Tracking Multiple Colored Blobs with a Moving Camera 用移动相机跟踪多个彩色斑点
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.348
Antonis A. Argyros, Manolis I. A. Lourakis
{"title":"Tracking Multiple Colored Blobs with a Moving Camera","authors":"Antonis A. Argyros, Manolis I. A. Lourakis","doi":"10.1109/CVPR.2005.348","DOIUrl":"https://doi.org/10.1109/CVPR.2005.348","url":null,"abstract":"This paper concerns a method for tracking multiple blobs exhibiting certain color distributions in images acquired by a possibly moving camera. The method encompasses a collection of techniques that enable modeling and detecting the blobs possessing the desired color distribution(s), as well as inferring their temporal association across image sequences. Appropriately colored blobs are detected with a Bayesian classifier, which is bootstrapped with a small set of training data. Then, an online iterative training procedure is employed to refine the classifier using additional training images. Online adaptation of color probabilities is used to enable the classifier to cope with illumination changes. Tracking over time is realized through a novel technique, which can handle multiple colored blobs. Such blobs may move in complex trajectories and occlude each other in the field of view of a possibly moving camera, while their number may vary over time. A prototype implementation of the developed system running on a conventional Pentium IV processor at 2.5 GHz operates on 320/spl times/240 live video in real time (30Hz). It is worth pointing out that currently, the cycle time of the tracker is determined by the maximum acquisition frame rate that is supported by our IEEE 1394 camera, rather than the latency introduced by the computational overhead for tracking blobs.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"40 1","pages":"1178"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79540376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Efficient Real-Time Algorithms for Eye State and Head Pose Tracking in Advanced Driver Support Systems 高级驾驶员辅助系统中眼态和头部姿态跟踪的高效实时算法
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.142
R. Hammoud, Andrew Wilhelm, P. Malawey, Gerald J. Witt
{"title":"Efficient Real-Time Algorithms for Eye State and Head Pose Tracking in Advanced Driver Support Systems","authors":"R. Hammoud, Andrew Wilhelm, P. Malawey, Gerald J. Witt","doi":"10.1109/CVPR.2005.142","DOIUrl":"https://doi.org/10.1109/CVPR.2005.142","url":null,"abstract":"This article shows cutting-edge computer vision methods employed in advanced vision sensing technologies for medical, safety and security applications, where the human eye represents the object of interest for both the imager and the computer. As the eye scans the environment, or focuses on particular objects in the scene, the processor simultaneously localizes the eye position, tracks its position and movement over time, and infers counter measures such as fatigue level, attention level, and gaze direction in real-time and automatically. The focus of this demonstration is placed on four different algorithms: auto-initialization (RHED), eye position tracking (SIRAT), eye closure recognition (HRA), driver head pose categorization.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"39 6 1","pages":"1181"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82818683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
Computer Vision for Music Identification: Video Demonstration 音乐识别的计算机视觉:视频演示
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.106
Yan Ke, Derek Hoiem, R. Sukthankar
{"title":"Computer Vision for Music Identification: Video Demonstration","authors":"Yan Ke, Derek Hoiem, R. Sukthankar","doi":"10.1109/CVPR.2005.106","DOIUrl":"https://doi.org/10.1109/CVPR.2005.106","url":null,"abstract":"This paper describes a demonstration video for our music identification system. The goal of music identification is to reliably recognize a song from a small sample of noisy audio. This problem is challenging because the recording is often corrupted by noise and because the audio sample will only match a small portion of the target song. Additionally, a practical music identification system should scale (in both accuracy and speed) to databases containing hundreds of thousands of songs. Recently, the music identification problem has attracted considerable attention. However, the task remains unsolved, particularly for noisy real-world queries. We cast music identification into an equivalent sub-image retrieval framework: identify the portion of a spectrogram image from the database that best matches a given query snippet. Our approach treats the spectrogram of each music clip as a 2D image and transforms music identification into a corrupted sub-image retrieval problem.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"15 1","pages":"1184"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90850552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Online Modeling and Tracking of Pose-Varying Faces in Video 视频中姿态变化人脸的在线建模与跟踪
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.261
Xiaoming Liu, Tsuhan Chen
{"title":"Online Modeling and Tracking of Pose-Varying Faces in Video","authors":"Xiaoming Liu, Tsuhan Chen","doi":"10.1109/CVPR.2005.261","DOIUrl":"https://doi.org/10.1109/CVPR.2005.261","url":null,"abstract":"We propose a face mosaicing approach to model both the facial appearance and geometry from pose-varying videos, and apply it in face tracking and recognition. The basic idea is that by approximating the human head as a 3D ellipsoid, multi-view face images can be back projected onto the surface of the ellipsoid, and the surface texture map is decomposed into an array of local patches. During the online modeling process, the position and pose of the first frame is assumed to be known for a given video sequence. For each frame in the sequence, the algorithm estimates the face-position and pose, and generates a texture map, which is further utilized in updating the mosaic model.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"39 1","pages":"1189"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77685025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Calibration of Pan-Tilt-Zoom (PTZ) Cameras and Omni-Directional Cameras 平移-倾斜-变焦(PTZ)相机和全方位相机的标定
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.94
S. Thirthala, Sudipta N. Sinha, M. Pollefeys
{"title":"Calibration of Pan-Tilt-Zoom (PTZ) Cameras and Omni-Directional Cameras","authors":"S. Thirthala, Sudipta N. Sinha, M. Pollefeys","doi":"10.1109/CVPR.2005.94","DOIUrl":"https://doi.org/10.1109/CVPR.2005.94","url":null,"abstract":"In the first part we discuss the problem of recovering the calibration of a network of pan-tilt-zoom cameras. The intrinsic parameters of each camera over its full range of zoom settings are estimated through a two step procedure. We first determine the intrinsic parameters at the camera's lowest zoom setting very accurately by capturing an extended panorama. The camera intrinsics are then determined at discrete steps in a monotonically increasing zoom sequence that spans the full zoom range of the cameras. Both steps are fully automatic and do not assume any knowledge of the scene structure. We validate our approach by calibrating two different types of pan tilt zoom cameras placed in an outdoor environment. We also show the high-resolution panoramic mosaics built from the images captured during this process. The second section deals with the calibration of omnidirectional cameras. A broad class of both central and non-central cameras, such as fish-eye and catadioptric cameras, can be reduced to 1D radial cameras under the assumption of known center of radial distortion. We study the multi-view geometry of 1D radial cameras. For cameras in general configuration, we introduce a quadrifocal tensor. From this tensor a metric reconstruction of the 1D cameras as well as the observed features can be obtained. In a second phase this reconstruction can then be used as a calibration object to estimate a non-parametric non-central model for the cameras. We study some degenerate cases, including pure rotation. In the case of a purely rotating camera we obtain a trifocal tensor. This allows us to obtain a metric reconstruction of the plane at infinity. Next, we use the plane at infinity as a calibration device to non-parametrically estimate the radial distortion.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"154 1","pages":"1198"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76743033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
A Markov Random Field Approach for Dense Photometric Stereo 稠密光度立体的马尔可夫随机场方法
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.34
K. Tang, Chi-Keung Tang, T. Wong
{"title":"A Markov Random Field Approach for Dense Photometric Stereo","authors":"K. Tang, Chi-Keung Tang, T. Wong","doi":"10.1109/CVPR.2005.34","DOIUrl":"https://doi.org/10.1109/CVPR.2005.34","url":null,"abstract":"We present a surprisingly simple system that allows for robust normal reconstruction by photometric stereo using a uniform and dense set of photometric images captured at fixed viewpoint, in the presense of spurious noises caused by highlight, shadows and non-Lambertian reflections. Our system consists of a mirror sphere, a spotlight and a DV camera only. Using this, a dense set of unbiased but noisy photometric data that roughly distributed uniformly on the light direction sphere is produced. To simultaneously recover normal orientations and preserve discontinuities, we model the dense photometric stereo problem into two coupled Markov random fields (MRFs): a smooth field for normal orientations, and a spatial line process for normal orientation discontinuities. A very fast tensorial belief propagation method is used to approximate the maximum a posteriori (MAP) solution of the Markov network. We present very encouraging results on a wide range of difficult objects to show the efficacy of our approach.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"2 1","pages":"1197"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74264292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
ALIP: The Automatic Linguistic Indexing of Pictures System 图片自动语言索引系统
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2005-06-20 DOI: 10.1109/CVPR.2005.67
Jia Li, James Ze Wang
{"title":"ALIP: The Automatic Linguistic Indexing of Pictures System","authors":"Jia Li, James Ze Wang","doi":"10.1109/CVPR.2005.67","DOIUrl":"https://doi.org/10.1109/CVPR.2005.67","url":null,"abstract":"We present the Automatic Linguistic Indexing of Pictures (ALIP) system. The system annotates images with linguistic terms, chosen among hundreds of such terms. The system uses a wavelet-based approach for feature extraction, a statistical modeling process for training, and a statistical significance processor to annotate images. We implemented and tested our ALIP system on a photographic image database of 600 different concepts, each with about 40 training images. The ALIP system has been used to annotate about 60,000 photographic images. In this demonstration, we illustrate the algorithms in the system and show the annotation results. With distributed computation, the annotation of an image can be provided in real-time.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"100 1","pages":"1208-1209"},"PeriodicalIF":0.0,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90846158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信