2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)最新文献_第10页

Blur vs. Block: Investigating the Effectiveness of Privacy-Enhancing Obfuscation for Images 模糊与块:调查图像隐私增强混淆的有效性

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.176

Yifang Li, Nishant Vishwamitra, Bart P. Knijnenburg, Hongxin Hu, Kelly E. Caine

{"title":"Blur vs. Block: Investigating the Effectiveness of Privacy-Enhancing Obfuscation for Images","authors":"Yifang Li, Nishant Vishwamitra, Bart P. Knijnenburg, Hongxin Hu, Kelly E. Caine","doi":"10.1109/CVPRW.2017.176","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.176","url":null,"abstract":"Computer vision can lead to privacy issues such as unauthorized disclosure of private information and identity theft, but it may also be used to preserve user privacy. For example, using computer vision, we may be able to identify sensitive elements of an image and obfuscate those elements thereby protecting private information or identity. However, there is a lack of research investigating the effectiveness of applying obfuscation techniques to parts of images as a privacy enhancing technology. In particular, we know very little about how well obfuscation works for human viewers or users' attitudes towards using these mechanisms. In this paper, we report results from an online experiment with 53 participants that investigates the effectiveness two exemplar obfuscation techniques: \"blurring\" and \"blocking\", and explores users' perceptions of these obfuscations in terms of image satisfaction, information sufficiency, enjoyment, and social presence. Results show that although \"blocking\" is more effective at de-identification compared to \"blurring\" or leaving the image \"as is\", users' attitudes towards \"blocking\" are the most negative, which creates a conflict between privacy protection and users' experience. Future work should explore alternative obfuscation techniques that could protect users' privacy and also provide a good viewing experience.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"65 1","pages":"1343-1351"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85714754","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: 62

The First Automatic Method for Mapping the Pothole in Seagrass 第一种海草坑洞自动测绘方法

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.39

M. Rahnemoonfar, M. Yari, Abdullah F. Rahman, Richard J. Kline

{"title":"The First Automatic Method for Mapping the Pothole in Seagrass","authors":"M. Rahnemoonfar, M. Yari, Abdullah F. Rahman, Richard J. Kline","doi":"10.1109/CVPRW.2017.39","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.39","url":null,"abstract":"There is a vital need to map seagrass ecosystems in order to determine worldwide abundance and distribution. Currently there is no established method for mapping the pothole or scars in seagrass. Detection of seagrass with optical remote sensing is challenged by the fact that light is attenuated as it passes through the water column and reflects back from the benthos. Optical remote sensing of seagrass is only possible if the water is shallow and relatively clear. In reality, coastal waters are commonly turbid, and seagrasses can grow under 10 meters of water or even deeper. One of the most precise sensors to map the seagrass disturbance is side scan sonar. Underwater acoustics mapping produces a high definition, two-dimensional sonar image of seagrass ecosystems. This paper proposes a methodology which detects seagrass potholes in sonar images. Side scan sonar images usually contain speckle noise and uneven illumination across the image. Moreover, disturbance presents complex patterns where most segmentation techniques will fail. In this paper, the quality of image is improved in the first stage using adaptive thresholding and wavelet denoising techniques. In the next step, a novel level set technique is applied to identify the pothole patterns. Our method is robust to noise and uneven illumination. Moreover it can detect the complex pothole patterns. We tested our proposed approach on a collection of underwater sonar images taken from Laguna Madre in Texas. Experimental results in comparison with the ground-truth show the efficiency of the proposed method.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"16 1","pages":"267-274"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85996780","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

Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image 基于Bagging和卷积神经网络的多视点监控图像车型分类

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.126

Pyong-Kun Kim, Kil-Taek Lim

引用次数: 43

Optimizing the Lens Selection Process for Multi-focus Plenoptic Cameras and Numerical Evaluation 多焦全光相机镜头选择过程优化及数值评价

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.223

L. Palmieri, R. Koch

引用次数: 6

Investigating Nuisance Factors in Face Recognition with DCNN Representation 用DCNN表示研究人脸识别中的妨害因素

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.86

C. Ferrari, G. Lisanti, S. Berretti, A. Bimbo

引用次数: 11

Leveraging Intra and Inter-Dataset Variations for Robust Face Alignment 利用数据集内部和数据集之间的变化进行稳健的人脸对齐

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.261

Wenyan Wu, Shuo Yang

{"title":"Leveraging Intra and Inter-Dataset Variations for Robust Face Alignment","authors":"Wenyan Wu, Shuo Yang","doi":"10.1109/CVPRW.2017.261","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.261","url":null,"abstract":"Face alignment is a critical topic in the computer vision community. Numerous efforts have been made and various benchmark datasets have been released in recent decades. However, two significant issues remain in recent datasets, e.g., Intra-Dataset Variation and Inter-Dataset Variation. Inter-Dataset Variation refers to bias on expression, head pose, etc. inside one certain dataset, while Intra-Dataset Variation refers to different bias across different datasets. To address the mentioned problems, we proposed a novel Deep Variation Leveraging Network (DVLN), which consists of two strong coupling sub-networks, e.g., Dataset-Across Network (DA-Net) and Candidate-Decision Network (CD-Net). Extensive evaluations show that our approach demonstrates real-time performance and dramatically outperforms state-of-the-art methods on the challenging 300-W dataset.,,,,,, To address the mentioned problems, we proposed a novel Deep Variation Leveraging Network (DVLN), which consists of two strong coupling sub-networks, e.g., Dataset-Across Network (DA-Net) and Candidate-Decision Network (CD-Net). In particular, DA-Net takes advantage of different characteristics and distributions across different datasets, while CD-Net makes a final decision on candidate hypotheses given by DA-Net to leverage variations within one certain dataset. Extensive evaluations show that our approach demonstrates real-time performance and dramatically outperforms state-of-the-art methods on the challenging 300-W dataset.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"26 1","pages":"2096-2105"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81129729","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: 100

Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks 基于深度神经网络模型压缩的嵌入式系统驾驶员困倦实时检测

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.59

B. Reddy, Ye-Hoon Kim, Sojung Yun, Chanwon Seo, Junik Jang

引用次数: 158

FORMS-Locks: A Dataset for the Evaluation of Similarity Measures for Forensic Toolmark Images FORMS-Locks:用于评估法医工具标记图像相似性度量的数据集

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.236

M. Keglevic, Robert Sablatnig

引用次数: 1

Intel(R) RealSense(TM) Stereoscopic Depth Cameras Intel(R) RealSense(TM)立体深度相机

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.167

L. Keselman, J. Woodfill, A. Grunnet-Jepsen, A. Bhowmik

引用次数: 116

Balanced Two-Stage Residual Networks for Image Super-Resolution 图像超分辨率的平衡两级残差网络

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.154

Yuchen Fan, Humphrey Shi, Jiahui Yu, Ding Liu, Wei Han, Haichao Yu, Zhangyang Wang, Xinchao Wang, Thomas S. Huang

引用次数: 88