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Tanaka Lab
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A lab that combines technologies for capturing images and for understanding them with machine learning.
AboutAbout the lab
Imaging × Vision × Machine Learning
The Tanaka Lab studies image processing, computer vision, and deep learning.
The lab works on both the processing stage in which a camera forms an image and recognition techniques that find and track the people and objects in it.
Some of its research uses light information beyond the three colors of red, green, and blue, such as polarization and hyperspectral images.
The lab also pursues research that improves the training methods of deep learning models themselves.
Key terms
- Computer vision
- Technology that enables computers to understand the objects and scenes shown in camera images and video
- Polarization
- A property describing the direction in which light waves oscillate. Ordinary cameras do not record it
- Demosaicing
- Processing that fills in color or polarization information recorded for only part of each pixel using the surrounding pixels, completing the image
- Point cloud
- Data that represents the shape of an object as a collection of many points scattered in 3D space
ResearchResearch themes
The lab combines research on forming images (imaging), understanding images (vision), and machine learning. It works on many themes, from camera signal processing to detecting and tracking people and objects and training methods for deep learning.
*Illustrative imageImage processing for polarization cameras
In collaboration with the Okutomi-Monno Lab, the lab develops high-quality demosaicing methods for monochrome and color polarization cameras that capture polarization in a single shot. Both learning-based and interpolation-based methods are used.
*Illustrative imageTracking people and objects with multiple cameras
In collaboration with the Okutomi-Monno Lab and researchers at NEC, the lab studies methods for continuously tracking people and objects with multiple cameras whose fields of view overlap. Using the positional relationships between cameras and appearance as cues, the system recognizes the same target even when it is hidden.
*Illustrative image2D and 3D object segmentation from street images
In collaboration with the Okutomi-Monno Lab, the lab builds 3D point clouds from multi-view street images, then segments objects both in the images and in 3D and matches them to each other. Reading street images is considered essential for urban mapping.
*Illustrative imageImproving deep learning training methods
This research improves a training method called SAM so that deep learning models perform well even on data they see for the first time, and applies it to a variety of uses.
MembersFaculty
The Tanaka Lab consists of faculty members and doctoral, master's, and undergraduate students.As of 2026
- ProfessorMasayuki TanakaPublications ↗
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Write a lab reviewAnonymous · Your draft is saved if you stopRelatedLabs similar to Tanaka Lab
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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: “Пиксели матрицы видеорегистратора” Gribkov / CC BY 4.0 (cropped)
- Image processing for polarization cameras: “BiodegradablePlasticUtensils1” Scott Bauer / Public domain (cropped)
- Tracking people and objects with multiple cameras: “Moscow, surveilance cameras, July 2025 01” Retired electrician / CC0 (cropped)
- 2D and 3D object segmentation from street images: “Ouster OS1-64 lidar point cloud of intersection of Folsom and Dore St, San Francisco” Daniel L. Lu / CC BY 4.0 (cropped)
- Improving deep learning training methods: “Rastrigin function” Diegotorquemada / Public domain (cropped)
- Related lab (Okutomi-Monno Lab): “Monmouth castle point cloud, created with Photosynth 01” John Cummings / CC BY-SA 3.0 (cropped)
- Related lab (Hara Lab): “Scratch on glass surface, 3D topography ( )” ZEISS Microscopy from Germany / CC BY-SA 2.0 (cropped)
- Related lab (Sato Lab): “Automobile exhaust gas” Ruben de Rijcke / CC BY-SA 3.0 (cropped)
- Related lab (Nakadai Lab): “RF Fully Anechoic Room 2” Mihaelawojcik / CC BY 4.0 (cropped)
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