2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)最新文献_第4页

Ball 3D Trajectory Reconstruction without Preliminary Temporal and Geometrical Camera Calibration 无初步时间和几何摄像机标定的球三维轨迹重建

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.26

S. Miyata, H. Saito, Kosuke Takahashi, Dan Mikami, Mariko Isogawa, H. Kimata

引用次数: 10

Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge 野性:价态和唤醒“野性”挑战

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.248

S. Zafeiriou, D. Kollias, M. Nicolaou, A. Papaioannou, Guoying Zhao, I. Kotsia

引用次数: 275

A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms 密集光场深度估计算法的分类与评价

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.226

O. Johannsen, Katrin Honauer, Bastian Goldlücke, A. Alperovich, F. Battisti, Yunsu Bok, Michele Brizzi, M. Carli, Gyeongmin Choe, M. Diebold, M. Gutsche, Hae-Gon Jeon, In-So Kweon, Jaesik Park, Jinsun Park, H. Schilling, Hao Sheng, Lipeng Si, Michael Strecke, Antonin Sulc, Yu-Wing Tai, Qing Wang, Tingxian Wang, S. Wanner, Z. Xiong, Jingyi Yu, Shuo Zhang, Hao Zhu

{"title":"A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms","authors":"O. Johannsen, Katrin Honauer, Bastian Goldlücke, A. Alperovich, F. Battisti, Yunsu Bok, Michele Brizzi, M. Carli, Gyeongmin Choe, M. Diebold, M. Gutsche, Hae-Gon Jeon, In-So Kweon, Jaesik Park, Jinsun Park, H. Schilling, Hao Sheng, Lipeng Si, Michael Strecke, Antonin Sulc, Yu-Wing Tai, Qing Wang, Tingxian Wang, S. Wanner, Z. Xiong, Jingyi Yu, Shuo Zhang, Hao Zhu","doi":"10.1109/CVPRW.2017.226","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.226","url":null,"abstract":"This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a recent benchmark [7], which allows a thorough performance analysis. While individual results are readily available on the benchmark web page http://www.lightfield-analysis.net, we take this opportunity to give a detailed overview of the current participants. Based on the algorithms submitted to our challenge, we develop a taxonomy of light field disparity estimation algorithms and give a report on the current state-ofthe- art. In addition, we include more comparative metrics, and discuss the relative strengths and weaknesses of the algorithms. Thus, we obtain a snapshot of where light field algorithm development stands at the moment and identify aspects with potential for further improvement.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"70 1","pages":"1795-1812"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73890287","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: 79

Analysis, Comparison, and Assessment of Latent Fingerprint Image Preprocessing 潜在指纹图像预处理的分析、比较与评价

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.91

Haiying Guan, Paul Lee, A. Dienstfrey, M. Theofanos, C. Lamp, Brian C. Stanton, Matthew T. Schwarz

{"title":"Analysis, Comparison, and Assessment of Latent Fingerprint Image Preprocessing","authors":"Haiying Guan, Paul Lee, A. Dienstfrey, M. Theofanos, C. Lamp, Brian C. Stanton, Matthew T. Schwarz","doi":"10.1109/CVPRW.2017.91","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.91","url":null,"abstract":"Latent fingerprints obtained from crime scenes are rarely immediately suitable for identification purposes. Instead, most latent fingerprint images must be preprocessed to enhance the fingerprint information held within the digital image, while suppressing interference arising from noise and otherwise unwanted image features. In the following we present results of our ongoing research to assess this critical step in the forensic workflow. Previously we discussed the creation of a new database of latent fingerprint images to support such research. The new contributions of this paper are twofold. First, we implement a study in which a group of trained Latent Print Examiners provide Extended Feature Set markups of all images. We discuss the experimental design of this study, and its execution. Next, we propose metrics for measuring the increase of fingerprint information provided by latent fingerprint image preprocessing, and we present preliminary analysis of these metrics when applied to the images in our database. We consider formally defined quality scales (Good, Bad, Ugly), and minutiae identifications of latent fingerprint images before and after preprocessing. All analyses show that latent fingerprint image preprocessing results in a statistically significant increase in fingerprint information and quality.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"196 1","pages":"628-635"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77436937","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

A Deep Convolutional Neural Network with Selection Units for Super-Resolution 具有超分辨率选择单元的深度卷积神经网络

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.153

Jae-Seok Choi, Munchurl Kim

引用次数: 103

End-to-End Driving in a Realistic Racing Game with Deep Reinforcement Learning 端到端驾驶在现实的赛车游戏与深度强化学习

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.64

E. Perot, M. Jaritz, Marin Toromanoff, Raoul de Charette

引用次数: 59

Infrared Variation Optimized Deep Convolutional Neural Network for Robust Automatic Ground Target Recognition 红外变化优化的深度卷积神经网络鲁棒自动地面目标识别

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.30

Sungho Kim, Woo‐Jin Song, Sohyeon Kim

{"title":"Infrared Variation Optimized Deep Convolutional Neural Network for Robust Automatic Ground Target Recognition","authors":"Sungho Kim, Woo‐Jin Song, Sohyeon Kim","doi":"10.1109/CVPRW.2017.30","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.30","url":null,"abstract":"Automatic infrared target recognition (ATR) is a traditionally unsolved problem in military applications because of the wide range of infrared (IR) image variations and limited number of training images, which is caused by various 3D target poses, non-cooperative weather conditions, and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches in RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of the RGB-CNN to IR ATR problem fails to work because of the IR database problems. This paper presents a novel infrared variation-optimized deep convolutional neural network (IVO-CNN) by considering database management, such as increasing the database by a thermal simulator, controlling the image contrast automatically and suppressing the thermal noise to reduce the effects of infrared image variations in deep convolutional neural network-based automatic ground target recognition. The experimental results on the synthesized infrared images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVO-CNN for military ATR applications.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"15 1","pages":"195-202"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87390410","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: 20

High-Magnification Multi-views Based Classification of Breast Fine Needle Aspiration Cytology Cell Samples Using Fusion of Decisions from Deep Convolutional Networks 基于深度卷积网络决策融合的高倍多视图乳腺细针穿刺细胞学样本分类

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.115

Hrushikesh Garud, S. Karri, D. Sheet, J. Chatterjee, M. Mahadevappa, A. Ray, Arindam Ghosh, A. Maity

{"title":"High-Magnification Multi-views Based Classification of Breast Fine Needle Aspiration Cytology Cell Samples Using Fusion of Decisions from Deep Convolutional Networks","authors":"Hrushikesh Garud, S. Karri, D. Sheet, J. Chatterjee, M. Mahadevappa, A. Ray, Arindam Ghosh, A. Maity","doi":"10.1109/CVPRW.2017.115","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.115","url":null,"abstract":"Fine needle aspiration cytology is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a deep convolutional neural network (CNN) based classification approach for the diagnosis of the cell samples using their microscopic high-magnification multi-views. The proposed approach has been tested using GoogLeNet architecture of CNN on an image dataset of 37 breast cytopathology samples (24 benign and 13 malignant), where the network was trained using images of ~54% cell samples and tested on the rest, achieving 89.7% mean accuracy in 8 fold validation.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"7 1","pages":"828-833"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88358026","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: 27

Finding Anomalies with Generative Adversarial Networks for a Patrolbot 基于生成对抗网络的巡逻机器人异常发现

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.68

W. Lawson, Esube Bekele, Keith Sullivan

引用次数: 37

Court-Based Volleyball Video Summarization Focusing on Rally Scene 以拉力赛场景为重点的基于球场的排球视频总结

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.28

Takahiro Itazuri, Tsukasa Fukusato, Shugo Yamaguchi, S. Morishima

引用次数: 5