IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第4页

Segmentation and matching: Towards a robust object detection system 分割与匹配:实现鲁棒目标检测系统

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.2014.6836082

Jing Huang, Suya You

引用次数: 4

Introspective semantic segmentation 内省语义分割

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.2014.6836032

Gautam Singh, J. Kosecka

引用次数: 3

GPU-accelerated and efficient multi-view triangulation for scene reconstruction gpu加速和高效的场景重建多视图三角剖分

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.2014.6836117

J. Mak, Mauricio Hess-Flores, S. Recker, John Douglas Owens, K. Joy

{"title":"GPU-accelerated and efficient multi-view triangulation for scene reconstruction","authors":"J. Mak, Mauricio Hess-Flores, S. Recker, John Douglas Owens, K. Joy","doi":"10.1109/WACV.2014.6836117","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836117","url":null,"abstract":"This paper presents a framework for GPU-accelerated N-view triangulation in multi-view reconstruction that improves processing time and final reprojection error with respect to methods in the literature. The framework uses an algorithm based on optimizing an angular error-based L1 cost function and it is shown how adaptive gradient descent can be applied for convergence. The triangulation algorithm is mapped onto the GPU and two approaches for parallelization are compared: one thread per track and one thread block per track. The better performing approach depends on the number of tracks and the lengths of the tracks in the dataset. Furthermore, the algorithm uses statistical sampling based on confidence levels to successfully reduce the quantity of feature track positions needed to triangulate an entire track. Sampling aids in load balancing for the GPU's SIMD architecture and for exploiting the GPU's memory hierarchy. When compared to a serial implementation, a typical performance increase of 3-4× can be achieved on a 4-core CPU. On a GPU, large track numbers are favorable and an increase of up to 40× can be achieved. Results on real and synthetic data prove that reprojection errors are similar to the best performing current triangulation methods but costing only a fraction of the computation time, allowing for efficient and accurate triangulation of large scenes.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"110 1","pages":"61-68"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88247327","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

Understanding and analyzing a large collection of archived swimming videos 理解和分析大量存档的游泳视频

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.2014.6836037

Long Sha, P. Lucey, S. Sridharan, S. Morgan, D. Pease

{"title":"Understanding and analyzing a large collection of archived swimming videos","authors":"Long Sha, P. Lucey, S. Sridharan, S. Morgan, D. Pease","doi":"10.1109/WACV.2014.6836037","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836037","url":null,"abstract":"In elite sports, nearly all performances are captured on video. Despite the massive amounts of video that has been captured in this domain over the last 10-15 years, most of it remains in an “unstructured” or “raw” form, meaning it can only be viewed or manually annotated/tagged with higher-level event labels which is time consuming and subjective. As such, depending on the detail or depth of annotation, the value of the collected repositories of archived data is minimal as it does not lend itself to large-scale analysis and retrieval. One such example is swimming, where each race of a swimmer is captured on a camcorder and in-addition to the split-times (i.e., the time it takes for each lap), stroke rate and stroke-lengths are manually annotated. In this paper, we propose a vision-based system which effectively “digitizes” a large collection of archived swimming races by estimating the location of the swimmer in each frame, as well as detecting the stroke rate. As the videos are captured from moving hand-held cameras which are located at different positions and angles, we show our hierarchical-based approach to tracking the swimmer and their different parts is robust to these issues and allows us to accurately estimate the swimmer location and stroke rates.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"29 1","pages":"674-681"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82766189","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: 17

Real time action recognition using histograms of depth gradients and random decision forests 使用深度梯度直方图和随机决策森林的实时动作识别

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.2014.6836044

H. Rahmani, A. Mahmood, D. Huynh, A. Mian

{"title":"Real time action recognition using histograms of depth gradients and random decision forests","authors":"H. Rahmani, A. Mahmood, D. Huynh, A. Mian","doi":"10.1109/WACV.2014.6836044","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836044","url":null,"abstract":"We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at a specific space-time location in the action volume. Moreover, we encode the dominant skeleton movements by computing a local 3D joint position difference histogram. For each joint, we compute a 3D space-time motion volume which we use as an importance indicator and incorporate in the feature vector for improved action discrimination. To retain only the discriminant features, we train a random decision forest (RDF). The proposed algorithm is evaluated on three standard datasets and compared with nine state-of-the-art algorithms. Experimental results show that, on the average, the proposed algorithm outperform all other algorithms in accuracy and have a processing speed of over 112 frames/second.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"1 1","pages":"626-633"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88786006","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: 114

Efficient dense subspace clustering 高效密集子空间聚类

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.2014.6836065

Pan Ji, M. Salzmann, Hongdong Li

{"title":"Efficient dense subspace clustering","authors":"Pan Ji, M. Salzmann, Hongdong Li","doi":"10.1109/WACV.2014.6836065","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836065","url":null,"abstract":"In this paper, we tackle the problem of clustering data points drawn from a union of linear (or affine) subspaces. To this end, we introduce an efficient subspace clustering algorithm that estimates dense connections between the points lying in the same subspace. In particular, instead of following the standard compressive sensing approach, we formulate subspace clustering as a Frobenius norm minimization problem, which inherently yields denser con- nections between the data points. While in the noise-free case we rely on the self-expressiveness of the observations, in the presence of noise we simultaneously learn a clean dictionary to represent the data. Our formulation lets us address the subspace clustering problem efficiently. More specifically, the solution can be obtained in closed-form for outlier-free observations, and by performing a series of linear operations in the presence of outliers. Interestingly, we show that our Frobenius norm formulation shares the same solution as the popular nuclear norm minimization approach when the data is free of any noise, or, in the case of corrupted data, when a clean dictionary is learned. Our experimental evaluation on motion segmentation and face clustering demonstrates the benefits of our algorithm in terms of clustering accuracy and efficiency.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"49 1","pages":"461-468"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87395999","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: 129

Generalized feature learning and indexing for object localization and recognition 目标定位与识别的广义特征学习与索引

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.2014.6836100

Ning Zhou, A. Angelova, Jianping Fan

引用次数: 0

Composite Discriminant Factor analysis 复合判别因子分析

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.2014.6836052

Vlad I. Morariu, Ejaz Ahmed, Venkataraman Santhanam, David Harwood, L. Davis

{"title":"Composite Discriminant Factor analysis","authors":"Vlad I. Morariu, Ejaz Ahmed, Venkataraman Santhanam, David Harwood, L. Davis","doi":"10.1109/WACV.2014.6836052","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836052","url":null,"abstract":"We propose a linear dimensionality reduction method, Composite Discriminant Factor (CDF) analysis, which searches for a discriminative but compact feature subspace that can be used as input to classifiers that suffer from problems such as multi-collinearity or the curse of dimensionality. The subspace selected by CDF maximizes the performance of the entire classification pipeline, and is chosen from a set of candidate subspaces that are each discriminative. Our method is based on Partial Least Squares (PLS) analysis, and can be viewed as a generalization of the PLS1 algorithm, designed to increase discrimination in classification tasks. We demonstrate our approach on the UCF50 action recognition dataset, two object detection datasets (INRIA pedestrians and vehicles from aerial imagery), and machine learning datasets from the UCI Machine Learning repository. Experimental results show that the proposed approach improves significantly in terms of accuracy over linear SVM, and also over PLS in terms of compactness and efficiency, while maintaining or improving accuracy.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"1 1","pages":"564-571"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90376019","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: 9

Improving multiview face detection with multi-task deep convolutional neural networks 用多任务深度卷积神经网络改进多视图人脸检测

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.2014.6835990

Cha Zhang, Zhengyou Zhang

引用次数: 203

Relative facial action unit detection 相对面部动作单元检测

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.2014.6835983

M. Khademi, Louis-Philippe Morency

引用次数: 14