2021 IEEE Winter Conference on Applications of Computer Vision (WACV)最新文献_第8页

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2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/wacv48630.2021.00414

引用次数: 0

Splatty- A Unified Image Demosaicing and Rectification Method 飞溅-一个统一的图像去马赛克和纠正方法

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00083

Pranav Verma, D. Meyer, Hanyang Xu, F. Kuester

{"title":"Splatty- A Unified Image Demosaicing and Rectification Method","authors":"Pranav Verma, D. Meyer, Hanyang Xu, F. Kuester","doi":"10.1109/WACV48630.2021.00083","DOIUrl":"https://doi.org/10.1109/WACV48630.2021.00083","url":null,"abstract":"Image demosaicing and rectification are key tasks that are frequently used in many computer vision systems. To date, however, their implementations have been plagued with large memory requirements and inconvenient dataflow, making it difficult to scale them to real-time, high resolution settings. This has motivated the development of joint demo-saicing and rectification algorithms that resolve the back-ward mapping dataflow for improved hardware implementation. Towards this purpose, we propose Splatty: an algorithmic solution to pipelined image stream demosaicing and rectification for memory bound applications requiring computational efficiency.We begin by introducing a polynomial Look-up-Table (LUT) compression scheme that can encode any arbitrarily complex lens model for rectification while keeping the remapping errors below 1E-10 pixels, and reducing the memory footprint to O(min(m, n)) from O(mn) for an m × n sized image. The core contribution leverages this LUT for a unified, forward-only splatting algorithm for simultaneous demosaicing and rectification. We demonstrate that merging these two steps into a single, forward-only splatting pass with interpolation, provides distinctive dataflow and performance efficiency benefits while maintaining quality standards when compared to state-of-the-art demosaicing and rectification algorithms.","PeriodicalId":236300,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133879593","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

Learning Fast Converging, Effective Conditional Generative Adversarial Networks with a Mirrored Auxiliary Classifier 使用镜像辅助分类器学习快速收敛、有效的条件生成对抗网络

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00261

Z. Wang

{"title":"Learning Fast Converging, Effective Conditional Generative Adversarial Networks with a Mirrored Auxiliary Classifier","authors":"Z. Wang","doi":"10.1109/WACV48630.2021.00261","DOIUrl":"https://doi.org/10.1109/WACV48630.2021.00261","url":null,"abstract":"Training conditional generative adversarial networks (GANs) has been remaining as a challenging task, though standard GANs have developed substantially and gained huge successes in recent years. In this paper, we propose a novel conditional GAN architecture with a mirrored auxiliary classifier (MAC-GAN) in its discriminator for the purpose of label conditioning. Unlike existing works, our mirrored auxiliary classifier contains both a real and a fake node for each specific class to distinguish real samples from generated samples that are assigned into the same category by previous models. Comparing with previous auxiliary classifier-based conditional GANs, our MAC-GAN learns a fast converging model for high-quality image generation, taking benefits from its robust, newly designed auxiliary classifier. Experiments on multiple benchmark datasets illustrate that our proposed model improves the quality of image synthesis compared with state-of-the-art approaches. Moreover, much better classification performance can be achieved with the mirrored auxiliary classifier, which can in turn promote the use of MAC-GAN in various transfer learning tasks.","PeriodicalId":236300,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129731812","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: 4

A Robust and Efficient Framework for Sports-Field Registration 一个健壮而高效的运动场注册框架

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00198

Xiaohan Nie, Shixing Chen, Raffay Hamid

{"title":"A Robust and Efficient Framework for Sports-Field Registration","authors":"Xiaohan Nie, Shixing Chen, Raffay Hamid","doi":"10.1109/WACV48630.2021.00198","DOIUrl":"https://doi.org/10.1109/WACV48630.2021.00198","url":null,"abstract":"We propose a novel framework to register sports-fields as they appear in broadcast sports videos. Unlike previous approaches, we particularly address the challenge of field- registration when: (a) there are not enough distinguishable features on the field, and (b) no prior knowledge is available about the camera. To this end, we detect a grid of key- points distributed uniformly on the entire field instead of using only sparse local corners and line intersections, thereby extending the keypoint coverage to the texture-less parts of the field as well. To further improve keypoint based homography estimate, we differentialbly warp and align it with a set of dense field-features defined as normalized distance- map of pixels to their nearest lines and key-regions. We predict the keypoints and dense field-features simultaneously using a multi-task deep network to achieve computational efficiency. To have a comprehensive evaluation, we have compiled a new dataset called SportsFields which is collected from 192 video-clips from 5 different sports covering large environmental and camera variations. We empirically demonstrate that our algorithm not only achieves state of the art field-registration accuracy but also runs in real-time for HD resolution videos using commodity hardware.","PeriodicalId":236300,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643129","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: 19

Deep Interactive Thin Object Selection 深度交互瘦对象选择

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00035

J. Liew, Scott D. Cohen, Brian L. Price, Long Mai, Jiashi Feng

{"title":"Deep Interactive Thin Object Selection","authors":"J. Liew, Scott D. Cohen, Brian L. Price, Long Mai, Jiashi Feng","doi":"10.1109/WACV48630.2021.00035","DOIUrl":"https://doi.org/10.1109/WACV48630.2021.00035","url":null,"abstract":"Existing deep learning based interactive segmentation methods have achieved remarkable performance with only a few user clicks, e.g. DEXTR [32] attaining 91.5% IoU on PASCAL VOC with only four extreme clicks. However, we observe even the state-of-the-art methods would often struggle in cases of objects to be segmented with elongated thin structures (e.g. bug legs and bicycle spokes). We investigate such failures, and find the critical reasons behind are two-fold: 1) lack of appropriate training dataset; and 2) extremely imbalanced distribution w.r.t. number of pixels belonging to thin and non-thin regions. Targeted at these challenges, we collect a large-scale dataset specifically for segmentation of thin elongated objects, named ThinObject-5K. Also, we present a novel integrative thin object segmentation network consisting of three streams. Among them, the high-resolution edge stream aims at preserving fine-grained details including elongated thin parts; the fixed-resolution context stream focuses on capturing semantic contexts. The two streams’ outputs are then amalgamated in the fusion stream to complement each other for help producing a refined segmentation output with sharper predictions around thin parts. Extensive experimental results well demonstrate the effectiveness of our proposed solution on segmenting thin objects, surpassing the baseline by ~ 30% IoUthin despite using only four clicks. Codes and dataset are available at https://github.com/liewjunhao/thin-object-selection.","PeriodicalId":236300,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132540529","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: 15

Automatic Object Recoloring Using Adversarial Learning 使用对抗性学习自动对象重新着色

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00153

Siavash Khodadadeh, Saeid Motiian, Zhe L. Lin, Ladislau Bölöni, S. Ghadar

引用次数: 3

Future Moment Assessment for Action Query 动作查询的未来时刻评估

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00326

Qiuhong Ke, Mario Fritz, B. Schiele

引用次数: 2

AutoRetouch: Automatic Professional Face Retouching AutoRetouch:自动专业的面部修饰

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00103

Alireza Shafaei

引用次数: 12

Dense 3D-Reconstruction from Monocular Image Sequences for Computationally Constrained UAS∗ 单眼图像序列密集三维重建的计算约束UAS *

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00186

Matthias Domnik, Pedro F. Proença, J. Delaune, J. Thiem, R. Brockers

{"title":"Dense 3D-Reconstruction from Monocular Image Sequences for Computationally Constrained UAS∗","authors":"Matthias Domnik, Pedro F. Proença, J. Delaune, J. Thiem, R. Brockers","doi":"10.1109/WACV48630.2021.00186","DOIUrl":"https://doi.org/10.1109/WACV48630.2021.00186","url":null,"abstract":"The ability to find safe landing sites over complex 3D terrain is an essential safety feature for fully autonomous small unmanned aerial systems (UAS), which requires on-board perception for 3D reconstruction and terrain analysis if the overflown terrain is unknown. This is a challenge for UAS that are limited in size, weight and computational power, such as small rotorcrafts executing autonomous missions on Earth, or in planetary applications such as the Mars Helicopter. For such a computationally constraint system, we propose a structure from motion approach that uses inputs from a single downward facing camera to produce dense point clouds of the overflown terrain in real time. In contrast to existing approaches, our method uses metric pose information from a visual-inertial odometry algorithm as camera pose priors, which allows deploying a fast pose refinement step to align camera frames such that a conventional stereo algorithm can be used for dense 3D reconstruction. We validate the performance of our approach with extensive evaluations in simulation, and demonstrate the feasibility with data from UAS flights.","PeriodicalId":236300,"journal":{"name":"2021 IEEE Winter Conference on Applications of Computer Vision (WACV)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126078169","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

Boosting Monocular Depth with Panoptic Segmentation Maps 提高单眼深度与全视分割地图

2021 IEEE Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2021-01-01 DOI: 10.1109/WACV48630.2021.00390

Faraz Saeedan, S. Roth

引用次数: 18