IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第7页

Benchmarking large-scale Fine-Grained Categorization 大规模细粒度分类的基准测试

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.2014.6836056

A. Angelova, Philip M. Long

{"title":"Benchmarking large-scale Fine-Grained Categorization","authors":"A. Angelova, Philip M. Long","doi":"10.1109/WACV.2014.6836056","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836056","url":null,"abstract":"This paper presents a systematic evaluation of recent methods in the fine-grained categorization domain, which have shown significant promise. More specifically, we investigate an automatic segmentation algorithm, a region pooling algorithm which is akin to pose-normalized pooling [31] [28], and a multi-class optimization method. We considered the largest and most popular datasets for fine-grained categorization available in the field: the Caltech-UCSD 200 Birds dataset [27], the Oxford 102 Flowers dataset [19], the Stanford 120 Dogs dataset [16], and the Oxford 37 Cats and Dogs dataset [21]. We view this work from a practitioner's perspective, answering the question: what are the methods that can create the best possible fine-grained recognition system which can be applied in practice? Our experiments provide insights of the relative merit of these methods. More importantly, after combining the methods, we achieve the top results in the field, outperforming the state-of-the-art methods by 4.8% and 10.3% for birds and dogs datasets, respectively. Additionally, our method achieves a mAP of 37.92 on the of 2012 Imagenet Fine-Grained Categorization Challenge [1], which outperforms the winner of this challenge by 5.7 points.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"83 1","pages":"532-539"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89952993","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: 10

Fast dense 3D reconstruction using an adaptive multiscale discrete-continuous variational method 基于自适应多尺度离散连续变分方法的快速密集三维重建

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.2014.6836118

Z. Kang, G. Medioni

{"title":"Fast dense 3D reconstruction using an adaptive multiscale discrete-continuous variational method","authors":"Z. Kang, G. Medioni","doi":"10.1109/WACV.2014.6836118","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836118","url":null,"abstract":"We present a system for fast dense 3D reconstruction with a hand-held camera. Walking around a target object, we shoot sequential images using continuous shooting mode. High-quality camera poses are obtained offline using structure-from-motion (SfM) algorithm with Bundle Adjustment. Multi-view stereo is solved using a new, efficient adaptive multiscale discrete-continuous variational method to generate depth maps with sub-pixel accuracy. Depth maps are then fused into a 3D model using volumetric integration with truncated signed distance function (TSDF). Our system is accurate, efficient and flexible: accurate depth maps are estimated with sub-pixel accuracy in stereo matching; dense models can be achieved within minutes as major algorithms parallelized on multi-core processor and GPU; various tasks can be handled (e.g. reconstruction of objects in both indoor and outdoor environment with different scales) without specific hand-tuning parameters. We evaluate our system quantitatively and qualitatively on Middlebury benchmark and another dataset collected with a smartphone camera.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"5 9 1","pages":"53-60"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80468986","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

Segmentation and tracking of partial planar templates 部分平面模板的分割与跟踪

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.2014.6835731

Abdelsalam Masoud, W. Hoff

引用次数: 2

Gradient based efficient feature selection 基于梯度的高效特征选择

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.2014.6836102

S. Z. Gilani, F. Shafait, A. Mian

引用次数: 8

Viewpoint-independent book spine segmentation 独立于视点的书脊分割

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.2014.6836066

L. Talker, Y. Moses

引用次数: 6

Detecting 3D geometric boundaries of indoor scenes under varying lighting 检测室内场景在不同光照条件下的三维几何边界

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.2014.6836125

Jie Ni, Tim K. Marks, Oncel Tuzel, F. Porikli

引用次数: 1

3D Metric Rectification using Angle Regularity 使用角度规则的三维度量校正

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.2014.6836121

Aamer Zaheer, Sohaib Khan

引用次数: 2

A combination of generative and discriminative models for fast unsupervised activity recognition from traffic scene videos 基于生成和判别模型的交通场景视频快速无监督活动识别

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.2014.6836042

M. V. Krishna, Joachim Denzler

引用次数: 7

A CRF approach to fitting a generalized hand skeleton model 广义手骨架模型的CRF拟合方法

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.2014.6836070

R. Mihail, G. Blomquist, Nathan Jacobs

引用次数: 5

Pedestrian detection in low resolution videos 低分辨率视频中的行人检测

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.2014.6836038

Hisham Sager, W. Hoff

引用次数: 10