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Image Analysis Lab
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The lab studies computer vision, image recognition, and related fields.
AboutAbout the lab
Teaching computers to understand images
The Image Analysis Lab studies computer vision, image recognition, deep learning, and related topics.
The lab's website introduces research on understanding images of tables, object detection, video super-resolution, visual localization, anomaly detection, and more.
The lab also conducts research on multimodal AI, which handles images and text together.
The lab belongs to the Intelligent Robotics Division of the Department of System Information Sciences, Graduate School of Information Sciences.
Key terms
- Computer vision
- A research field that aims to make computers see and understand images and video
- Object detection
- A technology that finds objects appearing in an image and classifies what they are
- Self-supervised learning
- A method of learning features from large amounts of unlabeled image data
- Multimodal AI
- AI that handles multiple types of information together, such as images and natural language
ResearchResearch themes
The lab studies computer vision, image recognition, deep learning, artificial intelligence, and more. Its website introduces research on understanding images of tables, object detection, video super-resolution, visual localization, and other topics.
*Illustrative imageTeaching AI to understand images of tables
This research aims to have AI understand tables from images, such as the tables used in papers, corporate documents, and financial reports. Tables pasted into PDFs or slides are not simply strings of characters to an AI. The lab takes an approach based on code-generation reasoning.
*Illustrative imageSelf-supervised learning for object detection
This research applies self-supervised learning, which learns features from large amounts of unlabeled images, to object detection. The lab works on methods that learn features combining image content and positional information.
*Illustrative imageProblem settings for detecting unknown objects
In real-world applications, systems often encounter objects of unknown classes that they did not see during training. From the perspective of who decides what should be detected, the lab addresses the challenges in this field and new problem settings.
*Illustrative imageLandslide image analysis and disaster risk assessment
This research uses multimodal AI to analyze images of landslides and assess disaster risk. Because the terrain changes greatly after a landslide occurs, rapid and advanced understanding of the situation is required.
MembersFaculty
The Image Analysis Lab consists of faculty members, NEXUS Research Fellows, researchers, and doctoral and master's students.As of 2026
- ProfessorTakayuki Okatani
- Assistant ProfessorYusuke Hosoya
- Project Assistant ProfessorTabe Jamaat Golsa
- Assistant ProfessorTran Thi Thanh Huyen
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: “Optical Sensor” QueSera4710 / CC BY-SA 3.0 (cropped)
- Teaching AI to understand images of tables: “Research Wikipedia and data protection example data set” Sean Rosenberg / CC BY-SA 4.0 (cropped)
- Self-supervised learning for object detection: “Intersection over Union - object detection bounding boxes” Adrian Rosebrock / CC BY-SA 4.0 (cropped)
- Problem settings for detecting unknown objects: “Zoox Toyota Highlander Test Vehicle - Sensor Closeup - San Francisco, May 2025 07” 9yz / CC BY 4.0 (cropped)
- Landslide image analysis and disaster risk assessment: “Niscemi landslide” Gianfrancodp / CC BY 4.0 (cropped)
- Related lab (Information Content Lab): “Reality check ESA384313” European Space Agency / CC BY-SA 3.0 igo (cropped)
- Related lab (System Information Mathematics I Lab): “Collection of geometry models-CnAM 3824-IMG 6752” Rama / CC BY-SA 3.0 fr (cropped)
- Related lab (Higher-Order Visual Information Lab): “Optical Illusion” Ironnail / CC BY-SA 4.0 (cropped)
- Related lab (Information Systems Evaluation Lab): “Efficient and Secure Group Messaging Encryption (2020) - page 8” Tyler Weitzman / CC BY-SA 4.0 (cropped)
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