2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第7页

A dual-layer estimator architecture for long-term localization 用于长期定位的双层估计器体系结构

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563131

Anastasios I. Mourikis, S. Roumeliotis

{"title":"A dual-layer estimator architecture for long-term localization","authors":"Anastasios I. Mourikis, S. Roumeliotis","doi":"10.1109/CVPRW.2008.4563131","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563131","url":null,"abstract":"In this paper, we present a localization algorithm for estimating the 3D position and orientation (pose) of a moving vehicle based on visual and inertial measurements. The main advantage of the proposed method is that it provides precise pose estimates at low computational cost. This is achieved by introducing a two-layer estimation architecture that processes measurements based on their information content. Inertial measurements and feature tracks between consecutive images are processed locally in the first layer (multi-state-constraint Kalman filter) providing estimates for the motion of the vehicle at a high rate. The second layer comprises a bundle adjustment iterative estimator that operates intermittently so as to (i) reduce the effect of the linearization errors, and (ii) update the state estimates every time an area is re-visited and features are re-detected (loop closure). Through this process reliable state estimates are available continuously, while the estimation errors remain bounded during long-term operation. The performance of the developed system is demonstrated in large-scale experiments, involving a vehicle localizing within an urban area.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124425174","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: 64

Fast gain-adaptive KLT tracking on the GPU GPU上的快速增益自适应KLT跟踪

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563089

C. Zach, D. Gallup, Jan-Michael Frahm

引用次数: 83

Incident light related distance error study and calibration of the PMD-range imaging camera pmd距离成像相机的入射光相关距离误差研究与标定

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563168

Jochen Radmer, Pol Moser Fuste, H. Schmidt, J. Krüger

引用次数: 41

Variational registration of tensor-valued images 张量值图像的变分配准

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562964

S. Barbieri, M. Welk, J. Weickert

引用次数: 3

Multiple cue integration in transductive confidence machines for head pose classification 基于多线索集成的头部姿态分类换能型置信度机器

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563070

V. Balasubramanian, S. Panchanathan, Shayok Chakraborty

{"title":"Multiple cue integration in transductive confidence machines for head pose classification","authors":"V. Balasubramanian, S. Panchanathan, Shayok Chakraborty","doi":"10.1109/CVPRW.2008.4563070","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563070","url":null,"abstract":"An important facet of learning in an online setting is the confidence associated with a prediction on a given test data point. In an online learning scenario, it would be expected that the system can increase its confidence of prediction as training data increases. We present a statistical approach in this work to associate a confidence value with a predicted class label in an online learning scenario. Our work is based on the existing work on transductive confidence machines (TCM) [1], which provided a methodology to define a heuristic confidence measure. We applied this approach to the problem of head pose classification from face images, and extended the framework to compute a confidence value when multiple cues are extracted from images to perform classification. Our approach is based on combining the results of multiple hypotheses and obtaining an integrated p-value to validate a single test hypothesis. From our experiments on the widely accepted FERET database, we obtained results which corroborated the significance of confidence measures - particularly, in online learning approaches. We could infer from our results with transductive learning that using confidence measures in online learning could yield significant boosts in the prediction accuracy, which would be very useful in critical pattern recognition applications.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116951436","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: 3

Implementation of Advanced Encryption Standard for encryption and decryption of images and text on a GPU 高级加密标准在GPU上对图像和文本进行加密和解密的实现

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563094

Manoj Seshadrinathan, K. Dempski

引用次数: 8

New insights into the calibration of ToF-sensors tof传感器校准的新见解

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563172

Marvin Lindner, A. Kolb, T. Ringbeck

引用次数: 36

A probabilistic representation of LiDAR range data for efficient 3D object detection 用于有效三维目标检测的激光雷达距离数据的概率表示

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563033

Theodore C. Yapo, C. Stewart, R. Radke

引用次数: 29

Verifying liveness by multiple experts in face biometrics 由多名面部生物识别专家验证活体

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563115

K. Kollreider, H. Fronthaler, J. Bigün

引用次数: 131

Scalable classifiers for Internet vision tasks 用于互联网视觉任务的可扩展分类器

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562958

Tom Yeh, John J. Lee, Trevor Darrell

{"title":"Scalable classifiers for Internet vision tasks","authors":"Tom Yeh, John J. Lee, Trevor Darrell","doi":"10.1109/CVPRW.2008.4562958","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4562958","url":null,"abstract":"Object recognition systems designed for Internet applications typically need to adapt to userspsila needs in a flexible fashion and scale up to very large data sets. In this paper, we analyze the complexity of several multiclass SVM-based algorithms and highlight the computational bottleneck they suffer at test time: comparing the input image to every training image. We propose an algorithm that overcomes this bottleneck; it offers not only the efficiency of a simple nearest-neighbor classifier, by voting on class labels based on the k nearest neighbors quickly determined by a vocabulary tree, but also the recognition accuracy comparable to that of a complex SVM classifier, by incorporating SVM parameters into the voting scores incrementally accumulated from individual image features. Empirical results demonstrate that adjusting votes by relevant support vector weights can improve the recognition accuracy of a nearest-neighbor classifier without sacrificing speed. Compared to existing methods, our algorithm achieves a ten-fold speed increase while incurring an acceptable accuracy loss that can be easily offset by showing about two more labels in the result. The speed, scalability, and adaptability of our algorithm makes it suitable for Internet vision applications.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127797960","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: 4