2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)最新文献_第4页

Periodic Variance Maximization Using Generalized Eigenvalue Decomposition Applied to Remote Photoplethysmography Estimation 基于广义特征值分解的周期方差最大化方法在光体积脉搏波遥感估计中的应用

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00181

R. Macwan, Serge Bobbia, Y. Benezeth, Julien Dubois, A. Mansouri

引用次数: 20

Camera Calibration for Underwater 3D Reconstruction Based on Ray Tracing Using Snell’s Law 基于Snell定律光线追踪的水下三维重建摄像机标定

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00190

Malte Pedersen, S. Bengtson, Rikke Gade, N. Madsen, T. Moeslund

引用次数: 27

Multi-scale Single Image Dehazing Using Perceptual Pyramid Deep Network 基于感知金字塔深度网络的多尺度单幅图像去雾

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00135

He Zhang, Vishwanath A. Sindagi, Vishal M. Patel

{"title":"Multi-scale Single Image Dehazing Using Perceptual Pyramid Deep Network","authors":"He Zhang, Vishwanath A. Sindagi, Vishal M. Patel","doi":"10.1109/CVPRW.2018.00135","DOIUrl":"https://doi.org/10.1109/CVPRW.2018.00135","url":null,"abstract":"Haze adversely degrades quality of an image thereby affecting its aesthetic appeal and visibility in outdoor scenes. Single image dehazing is particularly challenging due to its ill-posed nature. Most existing work, including the recent convolutional neural network (CNN) based methods, rely on the classical mathematical formulation where the hazy image is modeled as the superposition of attenuated scene radiance and the atmospheric light. In this work, we explore CNNs to directly learn a non-linear function between hazy images and the corresponding clear images. We present a multi-scale image dehazing method using Perceptual Pyramid Deep Network based on the recently popular dense blocks and residual blocks. The proposed method involves an encoder-decoder structure with a pyramid pooling module in the decoder to incorporate contextual information of the scene while decoding. The network is learned by minimizing the mean squared error and perceptual losses. Multi-scale patches are used during training and inference process to further improve the performance. Experiments on the recently released NTIRE2018-Dehazing dataset demonstrates the superior performance of the proposed method over recent state-of-the-art approaches. Additionally, the proposed method is ranked among top-3 methods in terms of quantitative performance in the recently conducted NTIRE2018-Dehazing challenge. Code can be found at https://github.com/hezhangsprinter/NTIRE-2018-Dehazing-Challenge","PeriodicalId":150600,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132212077","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: 128

Elastic Handling of Predictor Phase in Functional Regression Models 函数回归模型中预测期的弹性处理

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00072

Kyungmin Ahn, J. D. Tucker, Wei Wu, Anuj Srivastava

{"title":"Elastic Handling of Predictor Phase in Functional Regression Models","authors":"Kyungmin Ahn, J. D. Tucker, Wei Wu, Anuj Srivastava","doi":"10.1109/CVPRW.2018.00072","DOIUrl":"https://doi.org/10.1109/CVPRW.2018.00072","url":null,"abstract":"Functional variables serve important roles as predictors in a variety of pattern recognition and vision applications. Focusing on a specific subproblem, termed scalar-on-function regression, most current approaches adopt the standard L2 inner product to form a link between functional predictors and scalar responses. These methods may perform poorly when predictor functions contain nuisance phase variability, i.e., predictors are temporally misaligned due to noise. While a simple solution could be to prealign predictors as a pre-processing step, before applying a regression model, this alignment is seldom optimal from the perspective of regression. We propose a new approach, termed elastic functional regression, where alignment is included in the regression model itself, and is performed in conjunction with the estimation of other model parameters. This model is based on a norm-preserving warping of predictors, not the standard time warping of functions, and provides better prediction in situations where the shape or the amplitude of the predictor is more useful than its phase. We demonstrate the effectiveness of this framework using simulated and stock market data.","PeriodicalId":150600,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129222184","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: 3

Active Vision Dataset Benchmark 主动视觉数据集基准

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00277

Phil Ammirato, A. Berg, J. Kosecka

引用次数: 6

A Novel Framework for Remote Photoplethysmography Pulse Extraction on Compressed Videos 一种基于压缩视频的光容积脉搏波脉冲提取新框架

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00177

Changchen Zhao, Chun-Liang Lin, Weihai Chen, Zhengguo Li

{"title":"A Novel Framework for Remote Photoplethysmography Pulse Extraction on Compressed Videos","authors":"Changchen Zhao, Chun-Liang Lin, Weihai Chen, Zhengguo Li","doi":"10.1109/CVPRW.2018.00177","DOIUrl":"https://doi.org/10.1109/CVPRW.2018.00177","url":null,"abstract":"Remote photoplethysmography (rPPG) has recently attracted much attention due to its non-contact measurement convenience and great potential in health care and computer vision applications. However, almost all the existing rPPG methods are based on uncompressed video data, which greatly limits its application to the scenarios that require long-distance video transmission. This paper proposes a novel framework as a first attempt to address the rPPG pulse extraction in presence of video compression artifacts. Based on the analysis of the impact of various compression methods on rPPG measurements, the problem is cast as single-channel signal separation. The framework consists of three major steps to extract the pulse waveform and heart rate by exploiting frequency structure of the rPPG signal. A benchmark dataset which contains stationary and motion videos has been built. The results show that the proposed algorithm significantly improves the SNR and heart rate precision of state-of-the-art rPPG algorithms on stationary videos and has a positive effect on motion videos at low bitrates.","PeriodicalId":150600,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123368753","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: 32

Cross-Domain Fashion Image Retrieval 跨域时尚图像检索

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00243

B. Gajic, R. Baldrich

引用次数: 34

On Visible Adversarial Perturbations & Digital Watermarking 可见对抗性扰动与数字水印

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00210

Jamie Hayes

引用次数: 92

Generative Adversarial Style Transfer Networks for Face Aging 面部老化的生成对抗风格迁移网络

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00282

Sveinn Pálsson, E. Agustsson, R. Timofte, L. Gool

{"title":"Generative Adversarial Style Transfer Networks for Face Aging","authors":"Sveinn Pálsson, E. Agustsson, R. Timofte, L. Gool","doi":"10.1109/CVPRW.2018.00282","DOIUrl":"https://doi.org/10.1109/CVPRW.2018.00282","url":null,"abstract":"How somebody looked like when younger? What could a person look like when 10 years older? In this paper we look at the problem of face aging, which relates to processing an image of a face to change its apparent age. This task involves synthesizing images and modeling the aging process, which both are problems that have recently enjoyed much research interest in the field of face and gesture recognition. We propose to look at the problem from the perspective of image style transfer, where we consider the age of the person as the underlying style of the image. We show that for large age differences, convincing face aging can be achieved by formulating the problem with a pairwise training of Cycle-consistent Generative Adversarial Networks (CycleGAN) over age groups. Furthermore, we propose a variant of CycleGAN which directly incorporates a pre-trained age prediction model, which performs better when the desired age difference is smaller. The proposed approaches are complementary in strengths and their fusion performs well for any desired level of aging effect. We quantitatively evaluate our proposed method through a user study and show that it outperforms prior state-of-the-art techniques for face aging.","PeriodicalId":150600,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124912787","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: 47

A Directed Sparse Graphical Model for Multi-target Tracking 多目标跟踪的有向稀疏图模型

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2018-06-01 DOI: 10.1109/CVPRW.2018.00235

M. Ullah, F. A. Cheikh

{"title":"A Directed Sparse Graphical Model for Multi-target Tracking","authors":"M. Ullah, F. A. Cheikh","doi":"10.1109/CVPRW.2018.00235","DOIUrl":"https://doi.org/10.1109/CVPRW.2018.00235","url":null,"abstract":"We propose a Directed Sparse Graphical Model (DSGM) for multi-target tracking. In the category of global optimization for multi-target tracking, traditional approaches have two main drawbacks. First, a cost function is defined in terms of the linear combination of the spatial and appearance constraints of the targets which results a highly non-convex function. And second, a very dense graph is constructed to capture the global attribute of the targets. In such a graph, It is impossible to find reliable tracks in polynomial time unless some relaxation and heuristics are used. To address these limitations, we proposed DSGM which finds a set of reliable tracks for the targets without any heuristics or relaxation and keeps the computational complexity very low through the design of the graph. Irrespective of traditional approaches where spatial and appearance constraints are added up linearly with a given weight factor, we incorporated these constraints in a cascaded fashion. First, we exploited a Hidden Markov Model (HMM) for the spatial constraints of the target and obtain most probable locations of the targets in a segment of video. Afterwards, a deep feature based appearance model is used to generate the sparse graph. The track for each target is found through dynamic programming. Experiments are performed on 3 challenging sports datasets (football, basketball and sprint) and promising results are achieved.","PeriodicalId":150600,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"39 25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114053380","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