IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第9页

Data-driven road detection 数据驱动的道路检测

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6835730

J. Álvarez, M. Salzmann, N. Barnes

引用次数: 6

Determining underwater vehicle movement from sonar data in relatively featureless seafloor tracking missions 在相对无特征的海底跟踪任务中，从声纳数据确定水下航行器的运动

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836007

A. Spears, A. Howard, M. West, Thomas Collins

{"title":"Determining underwater vehicle movement from sonar data in relatively featureless seafloor tracking missions","authors":"A. Spears, A. Howard, M. West, Thomas Collins","doi":"10.1109/WACV.2014.6836007","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836007","url":null,"abstract":"Navigation through underwater environments is challenging given the lack of accurate positioning systems. The determination of underwater vehicle movement using an integrated acoustic sonar sensor would provide underwater vehicles with greatly increased autonomous navigation capabilities. A forward looking sonar sensor may be used for determining autonomous vehicle movement using filtering and optical flow algorithms. Optical flow algorithms have shown excellent results for vision image processing. However, they have been found difficult to implement using sonar data due to the high level of noise present, as well as the widely varying appearances of objects from frame to frame. For the bottom tracking applications considered, the simplifying assumption can be made that all features move with an equivalent direction and magnitude between frames. Statistical analysis of all estimated feature movements provides an accurate estimate of the overall shift, which translates directly to the vehicle movement. Results using acoustic sonar data are presented which illustrate the effectiveness of this methodology.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"1 1","pages":"909-916"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83030338","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

Automatic 3D change detection for glaucoma diagnosis 用于青光眼诊断的自动三维变化检测

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836072

Lu Wang, V. Kallem, Mayank Bansal, J. Eledath, H. Sawhney, Denise J. Pearson, R. Stone

引用次数: 3

Online discriminative dictionary learning for visual tracking 用于视觉跟踪的在线判别字典学习

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836014

Fan Yang, Zhuolin Jiang, L. Davis

引用次数: 35

Ant tracking with occlusion tunnels 蚂蚁追踪闭塞隧道

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836002

Thomas Fasciano, A. Dornhaus, M. Shin

引用次数: 15

Multi-leaf alignment from fluorescence plant images 荧光植物图像的多叶排列

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836067

Xi Yin, Xiaoming Liu, Jin Chen, D. Kramer

引用次数: 27

Combining semantic scene priors and haze removal for single image depth estimation 结合语义场景先验和去雾的单幅图像深度估计

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836021

Ke Wang, Enrique Dunn, Joseph Tighe, Jan-Michael Frahm

引用次数: 14

Beyond PASCAL: A benchmark for 3D object detection in the wild 超越PASCAL:野外3D物体检测的基准

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836101

Yu Xiang, Roozbeh Mottaghi, S. Savarese

{"title":"Beyond PASCAL: A benchmark for 3D object detection in the wild","authors":"Yu Xiang, Roozbeh Mottaghi, S. Savarese","doi":"10.1109/WACV.2014.6836101","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836101","url":null,"abstract":"3D object detection and pose estimation methods have become popular in recent years since they can handle ambiguities in 2D images and also provide a richer description for objects compared to 2D object detectors. However, most of the datasets for 3D recognition are limited to a small amount of images per category or are captured in controlled environments. In this paper, we contribute PASCAL3D+ dataset, which is a novel and challenging dataset for 3D object detection and pose estimation. PASCAL3D+ augments 12 rigid categories of the PASCAL VOC 2012 [4] with 3D annotations. Furthermore, more images are added for each category from ImageNet [3]. PASCAL3D+ images exhibit much more variability compared to the existing 3D datasets, and on average there are more than 3,000 object instances per category. We believe this dataset will provide a rich testbed to study 3D detection and pose estimation and will help to significantly push forward research in this area. We provide the results of variations of DPM [6] on our new dataset for object detection and viewpoint estimation in different scenarios, which can be used as baselines for the community. Our benchmark is available online at http://cvgl.stanford.edu/projects/pascal3d.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"14 1","pages":"75-82"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80353826","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: 726

Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search 基于经验硬度模型的近似最近邻搜索贝叶斯优化

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836049

Julieta Martinez, J. Little, Nando de Freitas

引用次数: 2

A lp-norm MTMKL framework for simultaneous detection of multiple facial action units 同时检测多个面部动作单元的lp范数MTMKL框架

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6835735

Xiao Zhang, M. Mahoor, S. Mavadati, J. Cohn

{"title":"A lp-norm MTMKL framework for simultaneous detection of multiple facial action units","authors":"Xiao Zhang, M. Mahoor, S. Mavadati, J. Cohn","doi":"10.1109/WACV.2014.6835735","DOIUrl":"https://doi.org/10.1109/WACV.2014.6835735","url":null,"abstract":"Facial action unit (AU) detection is a challenging topic in computer vision and pattern recognition. Most existing approaches design classifiers to detect AUs individually or AU combinations without considering the intrinsic relations among AUs. This paper presents a novel method, lp-norm multi-task multiple kernel learning (MTMKL), that jointly learns the classifiers for detecting the absence and presence of multiple AUs. lp-norm MTMKL is an extension of the regularized multi-task learning, which learns shared kernels from a given set of base kernels among all the tasks within Support Vector Machines (SVM). Our approach has several advantages over existing methods: (1) AU detection work is transformed to a MTL problem, where given a specific frame, multiple AUs are detected simultaneously by exploiting their inter-relations; (2) lp-norm multiple kernel learning is applied to increase the discriminant power of classifiers. Our experimental results on the CK+ and DISFA databases show that the proposed method outperforms the state-of-the-art methods for AU detection.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"17 1","pages":"1104-1111"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73480128","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: 38