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

Classification of Puck Possession Events in Ice Hockey 冰球控球事件的分类

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.24

Moumita Roy Tora, Jianhui Chen, J. Little

引用次数: 43

Exploration of Social and Web Image Search Results Using Tensor Decomposition 利用张量分解探索社交和网络图像搜索结果

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.239

Liuqing Yang, E. Papalexakis

引用次数: 0

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

Robust FEC-CNN: A High Accuracy Facial Landmark Detection System 鲁棒FEC-CNN:一种高精度人脸特征检测系统

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.255

Zhenliang He, Jie Zhang, Meina Kan, S. Shan, Xilin Chen

{"title":"Robust FEC-CNN: A High Accuracy Facial Landmark Detection System","authors":"Zhenliang He, Jie Zhang, Meina Kan, S. Shan, Xilin Chen","doi":"10.1109/CVPRW.2017.255","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.255","url":null,"abstract":"Facial landmark detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial landmark detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work, we propose an effective facial landmark detection system, recorded as Robust FEC-CNN (RFC), which achieves impressive results on facial landmark detection in the wild. Considering the favorable ability of deep convolutional neural network, we resort to FEC-CNN as a basic method to characterize the complex nonlinearity from face appearance to shape. Moreover, face bounding box invariant technique is adopted to reduce the landmark localization sensitivity to the face detector while model ensemble strategy is adopted to further enhance the landmark localization performance. We participate the Menpo Facial Landmark Localisation in-the-Wild Challenge and our RFC significantly outperforms the baseline approach APS. Extensive experiments on Menpo Challenge dataset and IBUG dataset demonstrate the superior performance of the proposed RFC.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"52 1","pages":"2044-2050"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82259484","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: 28

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

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

Robust Hand Detection and Classification in Vehicles and in the Wild 车辆和野外的鲁棒手部检测与分类

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-01 DOI: 10.1109/CVPRW.2017.159

T. Le, Kha Gia Quach, Chenchen Zhu, C. Duong, Khoa Luu, M. Savvides

{"title":"Robust Hand Detection and Classification in Vehicles and in the Wild","authors":"T. Le, Kha Gia Quach, Chenchen Zhu, C. Duong, Khoa Luu, M. Savvides","doi":"10.1109/CVPRW.2017.159","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.159","url":null,"abstract":"Robust hand detection and classification is one of the most crucial pre-processing steps to support human computer interaction, driver behavior monitoring, virtual reality, etc. This problem, however, is very challenging due to numerous variations of hand images in real-world scenarios. This work presents a novel approach named Multiple Scale Region-based Fully Convolutional Networks (MSRFCN) to robustly detect and classify human hand regions under various challenging conditions, e.g. occlusions, illumination, low-resolutions. In this approach, the whole image is passed through the proposed fully convolutional network to compute score maps. Those score maps with their position-sensitive properties can help to efficiently address a dilemma between translation-invariance in classification and detection. The method is evaluated on the challenging hand databases, i.e. the Vision for Intelligent Vehicles and Applications (VIVA) Challenge, Oxford hand dataset and compared against various recent hand detection methods. The experimental results show that our proposed MS-FRCN approach consistently achieves the state-of-the-art hand detection results, i.e. Average Precision (AP) / Average Recall (AR) of 95.1% / 94.5% at level 1 and 86.0% / 83.4% at level 2, on the VIVA challenge. In addition, the proposed method achieves the state-of-the-art results for left/right hand and driver/passenger classification tasks on the VIVA database with a significant improvement on AP/AR of ~7% and ~13% for both classification tasks, respectively. The hand detection performance of MS-RFCN reaches to 75.1% of AP and 77.8% of AR on Oxford database.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"32 1","pages":"1203-1210"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91384722","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: 54

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