Robotics and Computer Vision Lab

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저 자 Hyoseok Hwang, Hyun Sung Chang, In So Kweon
학 회 OSA Optics Express
BidTex Ref @article{Hwang:17, author = {Hyoseok Hwang and Hyun Sung Chang and In So Kweon}, journal = {Opt. Express}, keywords = {Image analysis; Three-dimensional image processing}, number = {10}, pages = {10801--10814}, publisher = {OSA}, title = {Local deformation calibration for autostereoscopic 3D display}, volume = {25}, month = {May}, year = {2017}, url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-25-10-10801}, abstract = {Calibration is vital to autostereoscopic 3D displays. This paper proposes a local calibration method that copes with any type of deformation in the optical layer. The proposed method is based on visual pattern analysis. Given the observations, we manage to localize the optical slits by matching the observations to the input pattern. In a principled optimization framework, we find an efficient calibration algorithm. Experimental validation follows. The local calibration shows significant improvement in 3D visual quality over the global calibration method. This paper also finds a new intuitive insight on the calibration in terms of the light field theory.}, }
논문일시(Year) 2017
논문일시(Month) 05
Calibration is vital to autostereoscopic 3D displays. This paper proposes a local calibration method that copes with any type of deformation in the optical layer. The proposed method is based on visual pattern analysis. Given the observations, we manage to localize the optical slits by matching the observations to the input pattern. In a principled optimization framework, we find an efficient calibration algorithm. Experimental validation follows. The local calibration shows significant improvement in 3D visual quality over the global calibration method. This paper also finds a new intuitive insight on the calibration in terms of the light field theory.

List of Articles
565. Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019 / 07
564. Learning Loss for Active Learning
Donggeun Yoo, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019 / 07
563. Gated Bidirectional Feature Pyramid Network for Accurate One Shot Detection
Sanghyun Woo, Soonmin Hwang, Ho-Deok Jang, In So Kweon
Machine Vision And Applications (MVA) 2019 / 1
562. High-Fidelity Depth Upsampling Using the Self-Learning Framework
Inwook Shim, Tae-Hyun Oh, In So Kweon
Sensors 2019 / 01
561. DPSNet: End-to-end Deep Plane Sweep Stereo
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon
International Conference on Learning Representations (ICLR) 2019 / 05
560. Robust Depth Estimation using Auto-Exposure Bracketing
Sunghoon Im, Hae-Gon Jeon, In So Kweon
IEEE Transaction on Image Processing (TIP) 2018 / 12
559. Part-based Player Identification using Deep Convolutional Representation and Multi-scale Pooling
Arda Senocak, Tae-Hyun Oh, Junsik Kim, In So Kweon
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2018 / 06
558. Discriminative Feature Learning for Unsupervised Video Summarization
Yunjae Jung, Donghyeon Cho, Dahun Kim, Sanghyun Woo, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2019 / 01
557. Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles
Dahun Kim, Donghyeon Cho, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2019 / 01
556. Semi-calibrated Photometric Stereo
Donghyeon Cho, Yasuyuki Matsushita, Yu-Wing Tai, and In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) /
555. Deep Convolutional Neural Network for Natural Image Matting using Initial Alpha Mattes
Donghyeon Cho, Yu-Wing Tai, In So Kweon
IEEE Transactions on Image Processing (TIP), accepted /
554. LinkNet: Relational Embedding for Scene Graph
Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon
Neural Information Processing Systems (NIPS) 2018 / 12
553. CBAM: Convolutional Block Attention Module
Jongchan Park, Sanghyun Woo, Joon-Young Lee, In So Kweon
European Conference on Computer Vision (ECCV) 2018 / 09
552. BAM: Bottleneck Attention Module
Jongchan Park, Sanghyun Woo, Joon-Young Lee, In So Kweon
British Machine Vision Conference (BMVC) 2018 / 09
551. Robust Low-rank Optimization with Priors
Tae-Hyun Oh
KAIST 2017 / 5
550. EPINET: A Fully-Convolutional Neural Network using Epipolar Geometry for Depth from Light Field Images
Changha Shin, Hae-Gon Jeon, Youngjin Yoon, In So Kweon, Seon Joo Kim
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
549. Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
Jongchan Park, Joon-Young Lee, Donggeun Yoo, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
548. Robust Depth Estimation from Auto Bracketed Images
Sunghoon Im, Hae-Gon Jeon, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
547. Learning to Localize Sound Source in Visual Scenes
Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 2018 / 06
546. Globally Optimal Inlier Set Maximization for Atlanta Frame Estimation
Kyungdon Joo, Tae-Hyun Oh, In So Kweon, Jean-Charles Bazin
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
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