2019 IEEE Winter Conference on Applications of Computer Vision (WACV)最新文献_第3页

Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network 密集三维点云重建使用深度金字塔网络

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00117

Priyanka Mandikal, R. Venkatesh Babu

{"title":"Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network","authors":"Priyanka Mandikal, R. Venkatesh Babu","doi":"10.1109/WACV.2019.00117","DOIUrl":"https://doi.org/10.1109/WACV.2019.00117","url":null,"abstract":"Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this problem. Existing point-cloud based reconstruction approaches directly predict the entire point cloud in a single stage. Although this technique can handle low-resolution point clouds, it is not a viable solution for generating dense, high-resolution outputs. In this work, we introduce DensePCR, a deep pyramidal network for point cloud reconstruction that hierarchically predicts point clouds of increasing resolution. Towards this end, we propose an architecture that first predicts a low-resolution point cloud, and then hierarchically increases the resolution by aggregating local and global point features to deform a grid. Our method generates point clouds that are accurate, uniform and dense. Through extensive quantitative and qualitative evaluation on synthetic and real datasets, we demonstrate that DensePCR outperforms the existing state-of-the-art point cloud reconstruction works, while also providing a light-weight and scalable architecture for predicting high-resolution outputs.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114631883","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: 88

DAFE-FD: Density Aware Feature Enrichment for Face Detection 基于密度感知特征的人脸检测

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00236

Vishwanath A. Sindagi, Vishal M. Patel

引用次数: 15

Latent Fingerprint Enhancement Using Generative Adversarial Networks 基于生成对抗网络的潜在指纹增强

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00100

Indu Joshi, A. Anand, Mayank Vatsa, Richa Singh, Sumantra Dutta Roy, P. Kalra

引用次数: 30

Recovering Faces From Portraits with Auxiliary Facial Attributes 从具有辅助面部属性的肖像中恢复面部

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00049

Fatemeh Shiri, Xin Yu, F. Porikli, R. Hartley, Piotr Koniusz

{"title":"Recovering Faces From Portraits with Auxiliary Facial Attributes","authors":"Fatemeh Shiri, Xin Yu, F. Porikli, R. Hartley, Piotr Koniusz","doi":"10.1109/WACV.2019.00049","DOIUrl":"https://doi.org/10.1109/WACV.2019.00049","url":null,"abstract":"Recovering a photorealistic face from an artistic portrait is a challenging task since crucial facial details are often distorted or completely lost in artistic compositions. To handle this loss, we propose an Attribute-guided Face Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and a Discriminative Network (DN). FRN consists of an autoencoder with residual block-embedded skip-connections and incorporates facial attribute vectors into the feature maps of input portraits at the bottleneck of the autoencoder. DN has multiple convolutional and fully-connected layers, and its role is to enforce FRN to generate authentic face images with corresponding facial attributes dictated by the input attribute vectors. For the preservation of identities, we impose the recovered and ground-truth faces to share similar visual features. Specifically, DN determines whether the recovered image looks like a real face and checks if the facial attributes extracted from the recovered image are consistent with given attributes. Our method can recover photorealistic identity-preserving faces with desired attributes from unseen stylized portraits, artistic paintings, and hand-drawn sketches. On large-scale synthesized and sketch datasets, we demonstrate that our face recovery method achieves state-of-the-art results.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122319415","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: 6

GAN-Based Pose-Aware Regulation for Video-Based Person Re-Identification 基于gan的视频人物再识别姿态感知调节

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00130

Alessandro Borgia, Yang Hua, Elyor Kodirov, N. Robertson

{"title":"GAN-Based Pose-Aware Regulation for Video-Based Person Re-Identification","authors":"Alessandro Borgia, Yang Hua, Elyor Kodirov, N. Robertson","doi":"10.1109/WACV.2019.00130","DOIUrl":"https://doi.org/10.1109/WACV.2019.00130","url":null,"abstract":"Video-based person re-identification deals with the inherent difficulty of matching sequences with different length, unregulated, and incomplete target pose/viewpoint structure. Common approaches operate either by reducing the problem to the still images case, facing a significant information loss, or by exploiting inter-sequence temporal dependencies as in Siamese Recurrent Neural Networks or in gait analysis. However, in all cases, the inter-sequences pose/viewpoint misalignment is considered, and the existing spatial approaches are mostly limited to the still images context. To this end, we propose a novel approach that can exploit more effectively the rich video information, by accounting for the role that the changing pose/viewpoint factor plays in the sequences matching process. In particular, our approach consists of two components. The first one attempts to complement the original pose-incomplete information carried by the sequences with synthetic GAN-generated images, and fuse their features vectors into a more discriminative viewpoint-insensitive embedding, namely Weighted Fusion (WF). Another one performs an explicit pose-based alignment of sequence pairs to promote coherent feature matching, namely Weighted-Pose Regulation (WPR). Extensive experiments on two large video-based benchmark datasets show that our approach outperforms considerably existing methods.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"363 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114011029","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: 7

FgGAN: A Cascaded Unpaired Learning for Background Estimation and Foreground Segmentation FgGAN:一种用于背景估计和前景分割的级联非配对学习

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00193

Prashant W. Patil, S. Murala

{"title":"FgGAN: A Cascaded Unpaired Learning for Background Estimation and Foreground Segmentation","authors":"Prashant W. Patil, S. Murala","doi":"10.1109/WACV.2019.00193","DOIUrl":"https://doi.org/10.1109/WACV.2019.00193","url":null,"abstract":"The moving object segmentation (MOS) in videos with bad weather, irregular motion of objects, camera jitter, shadow and dynamic background scenarios is still an open problem for computer vision applications. To address these issues, in this paper, we propose an approach named as Foreground Generative Adversarial Network (FgGAN) with the recent concepts of generative adversarial network (GAN) and unpaired training for background estimation and foreground segmentation. To the best of our knowledge, this is the first paper with the concept of GAN-based unpaired learning for MOS. Initially, video-wise background is estimated using GAN-based unpaired learning network (network-I). Then, to extract the motion information related to foreground, motion saliency is estimated using estimated background and current video frame. Further, estimated motion saliency is given as input to the GANbased unpaired learning network (network-II) for foreground segmentation. To examine the effectiveness of proposed FgGAN (cascaded networks I and II), the challenging video categories like dynamic background, bad weather, intermittent object motion and shadow are collected from ChangeDetection.net-2014 [26] database. The segmentation accuracy is observed qualitatively and quantitatively in terms of F-measure and percentage of wrong classification (PWC) and compared with the existing state-of-the-art methods. From experimental results, it is evident that the proposed FgGAN shows significant improvement in terms of F-measure and PWC as compared to the existing stateof-the-art methods for MOS.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128451602","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: 30

Conditional Generative Adversarial Refinement Networks for Unbalanced Medical Image Semantic Segmentation 不平衡医学图像语义分割的条件生成对抗优化网络

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00200

Mina Rezaei, Haojin Yang, Konstantin Harmuth, C. Meinel

{"title":"Conditional Generative Adversarial Refinement Networks for Unbalanced Medical Image Semantic Segmentation","authors":"Mina Rezaei, Haojin Yang, Konstantin Harmuth, C. Meinel","doi":"10.1109/WACV.2019.00200","DOIUrl":"https://doi.org/10.1109/WACV.2019.00200","url":null,"abstract":"We propose a new generative adversarial architecture to mitigate imbalance data problem in medical image semantic segmentation where the majority of pixels belongs to a healthy region and few belong to lesion or non-health region. A model trained with imbalanced data tends to bias towards healthy data which is not desired in clinical applications and predicted outputs by these networks have high precision and low sensitivity. We propose a new conditional generative refinement network with three components: a generative, a discriminative, and a refinement networks to mitigate imbalanced data problem through ensemble learning. The generative network learns to the segment at the pixel level by getting feedback from the discriminative network according to the true positive and true negative maps. On the other hand, the refinement network learns to predict the false positive and the false negative masks produced by the generative network that has significant value, especially in medical application. The final semantic segmentation masks are then composed by the output of the three networks. The proposed architecture shows state-of-the-art results on LiTS-2017 for simultaneous liver and lesion segmentation, and MDA231 for microscopic cell segmentation. We have achieved competitive results on BraTS-2017 for brain tumor segmentation.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128452348","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

Starts Better and Ends Better: A Target Adaptive Image Signature Tracker 开始更好，结束更好:目标自适应图像签名跟踪器

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00024

Xingchao Liu, Ce Li, Hongren Wang, Xiantong Zhen, Baochang Zhang, Qixiang Ye

{"title":"Starts Better and Ends Better: A Target Adaptive Image Signature Tracker","authors":"Xingchao Liu, Ce Li, Hongren Wang, Xiantong Zhen, Baochang Zhang, Qixiang Ye","doi":"10.1109/WACV.2019.00024","DOIUrl":"https://doi.org/10.1109/WACV.2019.00024","url":null,"abstract":"Correlation filter (CF) trackers have achieved outstanding performance in visual object tracking tasks, in which the cosine mask plays an essential role in alleviating boundary effects caused by the circular assumption. However, the cosine mask imposes a larger weight on its center position, which greatly affects CF trackers, that is, their performance will drop significantly if a bad starting point happens to occur. To address the above issue, we propose a target adaptive image signature (TaiS) model to refine the starting point in each frame for CF trackers. Specifically, we incorporate the target priori into the image signature to build a target-specific saliency map, and iteratively refine the starting point with a closed-form solution during the tracking process. As a result, our TaiS is able to find a better starting point close to the center of targets; more importantly, it is independent of specific CF trackers and can efficiently improve their performance. Experiments on two benchmark datasets, i.e., OTB100 and UAV123, demonstrate that our TaiS consistently achieves high performance and updates the state of the arts in visual tracking. The source code of our approach will be made publicly available.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115988498","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: 0

Shadow Patching: Guided Image Completion for Shadow Removal 阴影修补:引导图像完成阴影去除

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00217

Ryan S. Hintze, B. Morse

引用次数: 4

Observing Pianist Accuracy and Form with Computer Vision 用计算机视觉观察钢琴家的准确性和形式

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00165

Jangwon Lee, Bardia Doosti, Yupeng Gu, David Cartledge, David J. Crandall, C. Raphael

引用次数: 10