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Computational Media Lab
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The lab uses camera video and computing power to explore how people relate to information.
AboutAbout the lab
Connecting people and information through video and computing.
The Computational Media Lab studies computational media, which merges sensing functions such as cameras with abundant computing resources over computer networks.
It aims to realize next-generation video media and visual augmentation technologies, including free-viewpoint video, mixed-reality information presentation, and see-through vision.
Because it deals with information related to people, the lab places importance on aligning the time scale of information processing with that of humans, and proposes new frameworks for presenting information in forms that people can easily understand.
The research descriptions on the lab's research page date from around 2019.
Key terms
- multi-view video
- Video of the same scene filmed from various angles by many cameras
- visual feedback
- Showing people their own movements and the like back to them as video. The lab is researching methods that can be used in settings such as sports training
- autoencoder
- A type of deep learning model. In this lab, it is used to find corresponding points between images
ResearchResearch themes
The Computational Media Lab studies human society and the living spaces and urban environments around it, processing real-world information with computers and presenting it in forms that people can easily understand. The lab calls its approach real-world computational informatics: integrating, over a network, functions that sense real-world information, computing functions that process diverse information, and large-scale database functions that select and store information.
*Illustrative imageVisual Feedback with Multi-View Video
The lab aims to realize visual feedback using multi-view video, in which many cameras film the same scene from various angles. By introducing automated re-setting of the point of focus and automated video switching into bullet-time video, it has realized a method that can be used in settings such as sports training.
*Illustrative imageEstimating Swimmer Positions
In competitive swimming, the lab is developing video analysis that enables detailed time-series performance analysis from footage taken by a camera installed at the top of the spectator stands. Using a method based on homography transformation and pixel distribution, it can now estimate the positions of swimmers in all lanes with high accuracy.
*Illustrative image3D Reconstruction of Cultural Heritage Buildings
The goal is to match past and present photographs of cultural heritage buildings. The challenges are long-term degradation of shape and a mix of correct and incorrect correspondences caused by the symmetry of buildings. Using an autoencoder and the Guided Matching method, the lab has achieved image matching between past and present photographs.
MembersFaculty
The lab's members consist of faculty members and researchers.As of 2026
- ProfessorYoshinari Kameda
- ProfessorItaru Kitahara
- Assistant ProfessorChun Xie
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Augmented Reality Headset 03” This image is a work by Wikipedia and Wikimedia Commons user C.Suthorn. When reusing, credit me as described in the Licensing information below. I would appreciate being notified (wikimail, talk page, , Social Media) if you use my work outside Wikimedia. Do not copy this image illegally by ignoring the terms of the license below, as it is not in the public domain. If you would like special permission to use, license, or purchase the image please contact me to negotiate terms. More of my work can be found in my personal gallery or at my Peertube channel. A prepared attribution especially for use in Social Media and Print (newspapers, flyers, books, murals, mousepads, mugs, posters, t-shirts) can be copied here / CC BY-SA 4.0 (cropped)
- Visual Feedback with Multi-View Video: “Scan the Wild Camera Array” Tessa Bury / CC BY 4.0 (cropped)
- Estimating Swimmer Positions: “Lei Cheng Uk swimming pool” Citobun / CC BY-SA 3.0 (cropped)
- 3D Reconstruction of Cultural Heritage Buildings: “Delphinion Miletus 3D Reconstruction” Adesloge / CC BY 4.0 (cropped)
- Related lab (計算情報学研究部門 データ基盤分野): “Wikimedia Foundation Servers-8055 15” Victorgrigas / CC BY-SA 3.0 (cropped)
- Related lab (高性能計算システム研究部門): “GIK Institute High Performance Computing Cluster” Me.hassan91 / CC BY-SA 3.0 (cropped)
- Related lab (生命科学研究部門 生命機能情報分野): “CSIRO ScienceImage 1349 Protein crystals” Health Science and Nutrition, CSIRO / CC BY 3.0 (cropped)
- Related lab (生命科学研究部門 分子進化分野): “Unrooted Phylogenetic Tree (SMCO2)” Nguy4148 / CC0 (cropped)
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