2019 IEEE Winter Conference on Applications of Computer Vision (WACV)最新文献_第6页

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2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/wacv.2019.00001

引用次数: 0

Semi-Supervised Convolutional Neural Networks for In-Situ Video Monitoring of Selective Laser Melting 半监督卷积神经网络在选择性激光熔化现场视频监控中的应用

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00084

Bodi Yuan, B. Giera, G. Guss, Ibo Matthews, Sara McMains

引用次数: 32

Single Image Deblurring and Camera Motion Estimation With Depth Map 单幅图像去模糊和相机运动估计与深度图

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00229

Liyuan Pan, Yuchao Dai, Miaomiao Liu

{"title":"Single Image Deblurring and Camera Motion Estimation With Depth Map","authors":"Liyuan Pan, Yuchao Dai, Miaomiao Liu","doi":"10.1109/WACV.2019.00229","DOIUrl":"https://doi.org/10.1109/WACV.2019.00229","url":null,"abstract":"Camera shake during exposure is a major problem in hand-held photography, as it causes image blur that destroys details in the captured images. In the real world, such blur is mainly caused by both the camera motion and the complex scene structure. While considerable existing approaches have been proposed based on various assumptions regarding the scene structure or the camera motion, few existing methods could handle the real 6 DoF camera motion. In this paper, we propose to jointly estimate the 6 DoF camera motion and remove the non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with a single blurry image and its depth map (either direct depth measurements, or a learned depth map) as input. We formulate our joint deblurring and 6 DoF camera motion estimation as an energy minimization problem which is solved in an alternative manner. Our model enables the recovery of the 6 DoF camera motion and the latent clean image, which could also achieve the goal of generating a sharp sequence from a single blurry image. Experiments on challenging real-world and synthetic datasets demonstrate that image blur from camera shake can be well addressed within our proposed framework.","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":"130492688","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: 17

Semantic Matching by Weakly Supervised 2D Point Set Registration 基于弱监督二维点集配准的语义匹配

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00118

Zakaria Laskar, H. R. Tavakoli, Juho Kannala

引用次数: 7

Deep Representation Learning Characterized by Inter-Class Separation for Image Clustering 基于类间分离的图像聚类深度表征学习

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00072

Dipanjan Das, Ratul Ghosh, B. Bhowmick

{"title":"Deep Representation Learning Characterized by Inter-Class Separation for Image Clustering","authors":"Dipanjan Das, Ratul Ghosh, B. Bhowmick","doi":"10.1109/WACV.2019.00072","DOIUrl":"https://doi.org/10.1109/WACV.2019.00072","url":null,"abstract":"Despite significant advances in clustering methods in recent years, the outcome of clustering of a natural image dataset is still unsatisfactory due to two important drawbacks. Firstly, clustering of images needs a good feature representation of an image and secondly, we need a robust method which can discriminate these features for making them belonging to different clusters such that intra-class variance is less and inter-class variance is high. Often these two aspects are dealt with independently and thus the features are not sufficient enough to partition the data meaningfully. In this paper, we propose a method where we discover these features required for the separation of the images using deep autoencoder. Our method learns the image representation features automatically for the purpose of clustering and also select a coherent image and an incoherent image simultaneously for a given image so that the feature representation learning can learn better discriminative features for grouping the similar images in a cluster and at the same time separating the dissimilar images across clusters. Experiment results show that our method produces significantly better result than the state-of-the-art methods and we also show that our method is more generalized across different dataset without using any pre-trained model like other existing methods.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"45 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":"123163860","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 Hierarchical Grocery Store Image Dataset With Visual and Semantic Labels 具有视觉和语义标签的分层杂货店图像数据集

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00058

Marcus Klasson, Cheng Zhang, H. Kjellström

{"title":"A Hierarchical Grocery Store Image Dataset With Visual and Semantic Labels","authors":"Marcus Klasson, Cheng Zhang, H. Kjellström","doi":"10.1109/WACV.2019.00058","DOIUrl":"https://doi.org/10.1109/WACV.2019.00058","url":null,"abstract":"Image classification models built into visual support systems and other assistive devices need to provide accurate predictions about their environment. We focus on an application of assistive technology for people with visual impairments, for daily activities such as shopping or cooking. In this paper, we provide a new benchmark dataset for a challenging task in this application - classification of fruits, vegetables, and refrigerated products, e.g. milk packages and juice cartons, in grocery stores. To enable the learning process to utilize multiple sources of structured information, this dataset not only contains a large volume of natural images but also includes the corresponding information of the product from an online shopping website. Such information encompasses the hierarchical structure of the object classes, as well as an iconic image of each type of object. This dataset can be used to train and evaluate image classification models for helping visually impaired people in natural environments. Additionally, we provide benchmark results evaluated on pretrained convolutional neural networks often used for image understanding purposes, and also a multi-view variational autoencoder, which is capable of utilizing the rich product information in the dataset.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"47 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":"127581265","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

FuturePose - Mixed Reality Martial Arts Training Using Real-Time 3D Human Pose Forecasting With a RGB Camera FuturePose -混合现实武术训练使用实时3D人体姿态预测与RGB相机

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00152

Erwin Wu, H. Koike

{"title":"FuturePose - Mixed Reality Martial Arts Training Using Real-Time 3D Human Pose Forecasting With a RGB Camera","authors":"Erwin Wu, H. Koike","doi":"10.1109/WACV.2019.00152","DOIUrl":"https://doi.org/10.1109/WACV.2019.00152","url":null,"abstract":"In this paper, we propose a novel mixed reality martial arts training system using deep learning based real-time human pose forecasting. Our training system is based on 3D pose estimation using a residual neural network with input from a RGB camera, which captures the motion of a trainer. The student wearing a head mounted display can see the virtual model of the trainer and his forecasted future pose. The pose forecasting is based on recurrent networks, to improve the learning quantity of the motion's temporal feature, we use a special lattice optical flow method for the joints movement estimation. We visualize the real-time human motion by a generated human model while the forecasted pose is shown by a red skeleton model. In our experiments, we evaluated the performance of our system when predicting 15 frames ahead in a 30-fps video (0.5s forecasting), the accuracies were acceptable since they are equal to or even outperforms some methods using depth IR cameras or fabric technologies, user studies showed that our system is helpful for beginners to understand martial arts and the usability is comfortable since the motions were captured by RGB camera.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"167 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":"117096064","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

Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity, Representation, Coverage and Importance 揭开多面视频摘要的神秘面纱:多样性、代表性、覆盖面和重要性之间的权衡

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00054

Vishal Kaushal, Rishabh K. Iyer, Khoshrav Doctor, Anurag Sahoo, P. Dubal, S. Kothawade, Rohan Mahadev, Kunal Dargan, Ganesh Ramakrishnan

{"title":"Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity, Representation, Coverage and Importance","authors":"Vishal Kaushal, Rishabh K. Iyer, Khoshrav Doctor, Anurag Sahoo, P. Dubal, S. Kothawade, Rohan Mahadev, Kunal Dargan, Ganesh Ramakrishnan","doi":"10.1109/WACV.2019.00054","DOIUrl":"https://doi.org/10.1109/WACV.2019.00054","url":null,"abstract":"This paper addresses automatic summarization of videos in a unified manner. In particular, we propose a framework for multi-faceted summarization for extractive, query base and entity summarization (summarization at the level of entities like objects, scenes, humans and faces in the video). We investigate several summarization models which capture notions of diversity, coverage, representation and importance, and argue the utility of these different models depending on the application. While most of the prior work on submodular summarization approaches has focused on combining several models and learning weighted mixtures, we focus on the explainability of different models and featurizations, and how they apply to different domains. We also provide implementation details on summarization systems and the different modalities involved. We hope that the study from this paper will give insights into practitioners to appropriately choose the right summarization models for the problems at hand.","PeriodicalId":436637,"journal":{"name":"2019 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"12 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":"114900008","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

Where to Focus on for Human Action Recognition? 人类行为识别的重点在哪里?

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00015

Srijan Das, Arpit Chaudhary, F. Brémond, M. Thonnat

引用次数: 32

Resultant Based Incremental Recovery of Camera Pose From Pairwise Matches 基于结果的相机姿势从成对匹配中增量恢复

2019 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2019-01-01 DOI: 10.1109/WACV.2019.00120

Y. Kasten, M. Galun, R. Basri

引用次数: 13