IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第10页

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

Coupling video segmentation and action recognition 视频分割与动作识别的耦合

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.2014.6836045

Amir Ghodrati, M. Pedersoli, T. Tuytelaars

引用次数: 1

Finger-knuckle-print verification based on vector consistency of corresponding interest points 基于相应兴趣点向量一致性的指关节指纹验证

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.2014.6835996

Min-Ki Kim, P. Flynn

引用次数: 2

Linear Local Distance coding for classification of HEp-2 staining patterns 线性局部距离编码用于HEp-2染色模式分类

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.2014.6836073

Xiang Xu, F. Lin, Carol Ng, K. Leong

{"title":"Linear Local Distance coding for classification of HEp-2 staining patterns","authors":"Xiang Xu, F. Lin, Carol Ng, K. Leong","doi":"10.1109/WACV.2014.6836073","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836073","url":null,"abstract":"Indirect Immunofluorescence (IIF) on Human Epithelial-2 (HEp-2) cells is the recommended methodology for detecting some specific autoimmune diseases by searching for antinuclear antibodies (ANAs) within a patient's serum. Due to the limitations of IIF such as subjective evaluation, automated Computer-Aided Diagnosis (CAD) system is required for diagnostic purposes. In particular, staining patterns classification of HEp-2 cells is a challenging task. In this paper, we adopt a feature extraction-coding-pooling framework which has shown impressive performance in image classification tasks, because it can obtain discriminative and effective image representation. However, the information loss is inevitable in the coding process. Therefore, we propose a Linear Local Distance (LLD) coding method to capture more discriminative information. LLD transforms original local feature to local distance vector by searching for local nearest few neighbors of local feature in the class-specific manifolds. The obtained local distance vector is further encoded and pooled together to get salient image representation. We demonstrate the effectiveness of LLD method on a public HEp-2 cells dataset containing six major staining patterns. Experimental results show that our approach has a superior performance to the state-of-the-art coding methods for staining patterns classification of HEp-2 cells.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"8 1","pages":"393-400"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81445168","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: 6