2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)最新文献_第9页

Unconstrained Face Alignment Without Face Detection 无人脸检测的无约束人脸对齐

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.258

Xiaohu Shao, Junliang Xing, Jiang-Jing Lv, C. Xiao, Pengcheng Liu, Youji Feng, Cheng Cheng

引用次数: 11

Exploring the Granularity of Sparsity in Convolutional Neural Networks 卷积神经网络稀疏度粒度的研究

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.241

Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, W. Dally

引用次数: 118

Fast Simplex-HMM for One-Shot Learning Activity Recognition 快速Simplex-HMM一次性学习活动识别

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.166

Mario Rodríguez, C. Orrite-Uruñuela, C. Medrano, D. Makris

引用次数: 14

Locally Adaptive Color Correction for Underwater Image Dehazing and Matching 水下图像去雾与匹配的局部自适应色彩校正

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.136

C. Ancuti, Cosmin Ancuti, C. Vleeschouwer, Rafael García

{"title":"Locally Adaptive Color Correction for Underwater Image Dehazing and Matching","authors":"C. Ancuti, Cosmin Ancuti, C. Vleeschouwer, Rafael García","doi":"10.1109/CVPRW.2017.136","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.136","url":null,"abstract":"Underwater images are known to be strongly deteriorated by a combination of wavelength-dependent light attenuation and scattering. This results in complex color casts that depend both on the scene depth map and on the light spectrum. Color transfer, which is a technique of choice to counterbalance color casts, assumes stationary casts, defined by global parameters, and is therefore not directly applicable to the locally variable color casts encountered in underwater scenarios. To fill this gap, this paper introduces an original fusion-based strategy to exploit color transfer while tuning the color correction locally, as a function of the light attenuation level estimated from the red channel. The Dark Channel Prior (DCP) is then used to restore the color compensated image, by inverting the simplified Koschmieder light transmission model, as for outdoor dehazing. Our technique enhances image contrast in a quite effective manner and also supports accurate transmission map estimation. Our extensive experiments also show that our color correction strongly improves the effectiveness of local keypoints matching.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"26 3 1","pages":"997-1005"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78820364","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: 39

Earth Observation Using SAR and Social Media Images 利用SAR和社交媒体图像进行地球观测

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.202

Yuanyuan Wang, Xiaoxiang Zhu

引用次数: 0

Parsimonious Coding and Verification of Offline Handwritten Signatures 离线手写签名的简约编码与验证

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.92

E. Zois, Ilias Theodorakopoulos, Dimitrios Tsourounis, G. Economou

{"title":"Parsimonious Coding and Verification of Offline Handwritten Signatures","authors":"E. Zois, Ilias Theodorakopoulos, Dimitrios Tsourounis, G. Economou","doi":"10.1109/CVPRW.2017.92","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.92","url":null,"abstract":"A common practice for addressing the problem of verifying the presence, or the consent of a person in many transactions is to utilize the handwritten signature. Among others, the offline or static signature is a valuable tool in forensic related studies. Thus, the importance of verifying static handwritten signatures still poses a challenging task. Throughout the literature, gray-level images, composed of handwritten signature traces are subjected to numerous processing stages; their outcome is the mapping of any input signature image in a so-called corresponding feature space. Pattern recognition techniques utilize this feature space, usually as a binary verification problem. In this work, sparse dictionary learning and coding are for the first time employed as a means to provide a feature space for offline signature verification, which intuitively adapts to a small set of randomly selected genuine reference samples, thus making it attractable for forensic cases. In this context, the K-SVD dictionary learning algorithm is employed in order to create a writer oriented lexicon. For any signature sample, sparse representation with the use of the writer's lexicon and the Orthogonal Matching Pursuit algorithm generates a weight matrix; features are then extracted by applying simple average pooling to the generated sparse codes. The performance of the proposed scheme is demonstrated using the popular CEDAR, MCYT75 and GPDS300 signature datasets, delivering state of the art results.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"36 1","pages":"636-645"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89923691","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: 15

Caught Red-Handed: Toward Practical Video-Based Subsequences Matching in the Presence of Real-World Transformations 当场抓获:在现实世界变换的存在下，走向实用的基于视频的子序列匹配

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.182

Yi Xu, True Price, F. Monrose, Jan-Michael Frahm

{"title":"Caught Red-Handed: Toward Practical Video-Based Subsequences Matching in the Presence of Real-World Transformations","authors":"Yi Xu, True Price, F. Monrose, Jan-Michael Frahm","doi":"10.1109/CVPRW.2017.182","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.182","url":null,"abstract":"Every minute, staggering amounts of user-generated videos are uploaded to on-line social networks. These videos can generate significant advertising revenue, providing strong incentive for unscrupulous individuals that wish to capitalize on this bonanza by pirating short clips from popular content and altering the copied media in ways that might bypass detection. Unfortunately, while the challenges posed by the use of skillful transformations has been known for quite some time, current state-of-the-art methods still suffer from severe limitations. Indeed, most of today's techniques perform poorly in the face of real world copies. To address this, we propose a novel approach that leverages temporal characteristics to identify subsequences of a video that were copied from elsewhere. Our approach takes advantage of a new temporal feature to index a reference library in a manner that is robust to popular spatial and temporal transformations in pirated videos. Our experimental evaluation on 27 hours of video obtained from social networks demonstrates that our technique significantly outperforms the existing state-of-the-art approaches with respect to accuracy, resilience, and efficiency.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"51 1","pages":"1397-1406"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89160497","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

Human Activity Recognition Using Combinatorial Deep Belief Networks 基于组合深度信念网络的人类活动识别

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.203

Shreyank N. Gowda

引用次数: 32

A Cost-Effective Framework for Automated Vehicle-Pedestrian Near-Miss Detection Through Onboard Monocular Vision 基于车载单目视觉的车辆-行人近距离碰撞自动检测框架

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.124

Ruimin Ke, J. Lutin, J. Spears, Yinhai Wang

引用次数: 22

Cartooning for Enhanced Privacy in Lifelogging and Streaming Videos 在生活日志和流媒体视频中增强隐私的卡通

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.175

Eman T. Hassan, Rakibul Hasan, Patrick Shaffer, David J. Crandall, Apu Kapadia

引用次数: 46