2019 IEEE Winter Conference on Applications of Computer Vision (WACV)最新文献_第10页

Location-Velocity Attention for Pedestrian Trajectory Prediction 行人轨迹预测的位置-速度注意

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00221

Hao Xue, D. Huynh, Mark Reynolds

引用次数: 25

Bringing Vision to the Blind: From Coarse to Fine, One Dollar at a Time 为盲人带来视力:从粗糙到精细，一次一美元

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00057

T. Huynh, J. Pillai, Eunyoung Kim, Kristen Aw, Jack Sim, Ken Goldman, Rui Min

引用次数: 3

A Comparative Analysis of Visual-Inertial SLAM for Assisted Wayfinding of the Visually Impaired 视觉惯性SLAM在视障人士辅助寻路中的比较分析

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00028

He Zhang, Lingqiu Jin, H. Zhang, C. Ye

引用次数: 5

Iris Recognition: Comparing Visible-Light Lateral and Frontal Illumination to NIR Frontal Illumination 虹膜识别:比较可见光侧面和正面照明与近红外正面照明

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00097

Daniel P. Benalcazar, C. Pérez, Diego Bastias, K. Bowyer

{"title":"Iris Recognition: Comparing Visible-Light Lateral and Frontal Illumination to NIR Frontal Illumination","authors":"Daniel P. Benalcazar, C. Pérez, Diego Bastias, K. Bowyer","doi":"10.1109/WACV.2019.00097","DOIUrl":"https://doi.org/10.1109/WACV.2019.00097","url":null,"abstract":"In most iris recognition systems the texture of the iris image is either the result of the interaction between the iris and Near Infrared (NIR) light, or between the iris pigmentation and visible-light. The iris, however, is a three-dimensional organ, and the information contained on its relief is not being exploited completely. In this article, we present an image acquisition method that enhances viewing the structural information of the iris. Our method consists of adding lateral illumination to the visible light frontal illumination to capture the structural information of the muscle fibers of the iris on the resulting image. These resulting images contain highly textured patterns of the iris. To test our method, we collected a database of 1,920 iris images using both a conventional NIR device, and a custom-made device that illuminates the eye in lateral and frontal angles with visible-light (LFVL). Then, we compared the iris recognition performance of both devices by means of a Hamming distance distribution analysis among the corresponding binary iris codes. The ROC curves show that our method produced more separable distributions than those of the NIR device, and much better distribution than using frontal visible-light alone. Eliminating errors produced by images captured with different iris dilation (13 cases), the NIR produced inter-class and intra-class distributions that are completely separable as in the case of LFVL. This acquisition method could also be useful for 3D iris scanning.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"191 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":"129237288","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: 9

Multi-Component Image Translation for Deep Domain Generalization 基于深度域泛化的多分量图像平移

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2018-12-21 DOI: 10.1109/WACV.2019.00067

Mohammad Mahfujur Rahman, C. Fookes, Mahsa Baktash, S. Sridharan

{"title":"Multi-Component Image Translation for Deep Domain Generalization","authors":"Mohammad Mahfujur Rahman, C. Fookes, Mahsa Baktash, S. Sridharan","doi":"10.1109/WACV.2019.00067","DOIUrl":"https://doi.org/10.1109/WACV.2019.00067","url":null,"abstract":"Domain adaption (DA) and domain generalization (DG) are two closely related methods which are both concerned with the task of assigning labels to an unlabeled data set. The only dissimilarity between these approaches is that DA can access the target data during the training phase, while the target data is totally unseen during the training phase in DG. The task of DG is challenging as we have no earlier knowledge of the target samples. If DA methods are applied directly to DG by a simple exclusion of the target data from training, poor performance will result for a given task. In this paper, we tackle the domain generalization challenge in two ways. In our first approach, we propose a novel deep domain generalization architecture utilizing synthetic data generated by a Generative Adversarial Network (GAN). The discrepancy between the generated images and synthetic images is minimized using existing domain discrepancy metrics such as maximum mean discrepancy or correlation alignment. In our second approach, we introduce a protocol for applying DA methods to a DG scenario by excluding the target data from the training phase, splitting the source data to training and validation parts, and treating the validation data as target data for DA. We conduct extensive experiments on four cross-domain benchmark datasets. Experimental results signify our proposed model outperforms the current state-of-the-art methods for DG.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116613401","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

DAC: Data-Free Automatic Acceleration of Convolutional Networks 卷积网络的无数据自动加速

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2018-12-20 DOI: 10.1109/WACV.2019.00175

Xin Li, Shuai Zhang, Bolan Jiang, Y. Qi, M. Chuah, N. Bi

{"title":"DAC: Data-Free Automatic Acceleration of Convolutional Networks","authors":"Xin Li, Shuai Zhang, Bolan Jiang, Y. Qi, M. Chuah, N. Bi","doi":"10.1109/WACV.2019.00175","DOIUrl":"https://doi.org/10.1109/WACV.2019.00175","url":null,"abstract":"Deploying a deep learning model on mobile/IoT devices is a challenging task. The difficulty lies in the trade-off between computation speed and accuracy. A complex deep learning model with high accuracy runs slowly on resource-limited devices, while a light-weight model that runs much faster loses accuracy. In this paper, we propose a novel decomposition method, namely DAC, that is capable of factorizing an ordinary convolutional layer into two layers with much fewer parameters. DAC computes the corresponding weights for the newly generated layers directly from the weights of the original convolutional layer. Thus, no training (or fine-tuning) or any data is needed. The experimental results show that DAC reduces a large number of floating-point operations (FLOPs) while maintaining high accuracy of a pre-trained model. If 2% accuracy drop is acceptable, DAC saves 53% FLOPs of VGG16 image classification model on ImageNet dataset, 29% FLOPS of SSD300 object detection model on PASCAL VOC2007 dataset, and 46% FLOPS of a multi-person pose estimation model on Microsoft COCO dataset. Compared to other existing decomposition methods, DAC achieves better performance.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"68 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128725176","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

SfMLearner++: Learning Monocular Depth & Ego-Motion Using Meaningful Geometric Constraints SfMLearner++:使用有意义的几何约束学习单目深度和自我运动

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2018-12-20 DOI: 10.1109/WACV.2019.00226

V. Prasad, B. Bhowmick

{"title":"SfMLearner++: Learning Monocular Depth & Ego-Motion Using Meaningful Geometric Constraints","authors":"V. Prasad, B. Bhowmick","doi":"10.1109/WACV.2019.00226","DOIUrl":"https://doi.org/10.1109/WACV.2019.00226","url":null,"abstract":"Most geometric approaches to monocular Visual Odometry (VO) provide robust pose estimates, but sparse or semi-dense depth estimates. Off late, deep methods have shown good performance in generating dense depths and VO from monocular images by optimizing the photometric consistency between images. Despite being intuitive, a naive photometric loss does not ensure proper pixel correspondences between two views, which is the key factor for accurate depth and relative pose estimations. It is a well known fact that simply minimizing such an error is prone to failures. We propose a method using Epipolar constraints to make the learning more geometrically sound. We use the Essential matrix, obtained using Nistér's Five Point Algorithm, for enforcing meaningful geometric constraints on the loss, rather than using it as labels for training. Our method, although simplistic but more geometrically meaningful, uses lesser number of parameters to give a comparable performance to state-of-the-art methods which use complex losses and large networks showing the effectiveness of using epipolar constraints. Such a geometrically constrained learning method performs successfully even in cases where simply minimizing the photometric error would fail.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123576278","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

Learning On-Road Visual Control for Self-Driving Vehicles With Auxiliary Tasks 具有辅助任务的自动驾驶汽车的道路视觉控制学习

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2018-12-19 DOI: 10.1109/WACV.2019.00041

Yilun Chen, Praveen Palanisamy, P. Mudalige, Katharina Muelling, J. Dolan

引用次数: 16

Model-Free Tracking With Deep Appearance and Motion Features Integration 无模型跟踪与深度外观和运动特征的集成

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2018-12-16 DOI: 10.1109/WACV.2019.00018

Xiaolong Jiang, Peizhao Li, Xiantong Zhen, Xianbin Cao

引用次数: 10

Action Quality Assessment Across Multiple Actions 跨多个行动的行动质量评估

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2018-12-15 DOI: 10.1109/WACV.2019.00161

Paritosh Parmar, B. Morris

引用次数: 78