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Shono Lab
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The lab explores image processing and deep learning, drawing on the mechanisms of vision.
AboutAbout the lab
Thinking about image processing and deep learning through the mechanisms of vision.
Shono Lab studies image processing based on visual information processing, and machine learning such as neural network models and deep learning.
Using how human and animal brains process information as a clue, the lab aims to build computational models.
Focusing on the "expert eye" of trained engineers and physicians, the lab investigates the properties of texture images, such as microscope images of magnetic thin films.
It is also considering applications of machine learning that support people in fields such as materials science.
Associate Professor Daiki Kimura of IBM Japan, Ltd., an affiliated faculty member, also takes part in the lab's research.
Key terms
- Neural network model
- A model for reproducing on a computer how neurons in the brain come together to process information
- CNN
- A deep convolutional neural network. A deep learning model inspired by the brain's hierarchical structure, whose basic operation is convolution
- Texture
- A pattern-like image pattern. An example is a microscope image of a magnetic thin film
NewsLab news
Paper co-authored with a doctoral student published in Knowledge-Based Systems (medical image segmentation)
Paper co-authored with a doctoral student published in IEEE Access (medical image classification)
Paper co-authored with a doctoral student published in IEEE Access (graph representation of electronic circuits)
Paper co-authored with a doctoral student published in IEEE Access (MRI image segmentation)
Paper co-authored with a doctoral student published in Information (tumor classification in MRI images)
ResearchResearch themes
Shono Lab studies image processing based on visual information processing, and machine learning such as neural network models and deep learning. Drawing on the visual mechanisms of the brain, the lab develops information processing technology for measurement images and other data.
*Illustrative imageVisual mechanisms and image processing
The brain performs advanced information processing, such as seeing objects and thinking, through an enormous number of relatively simple components called neurons. The lab treats understanding this mechanism as the first step toward building computational models.
*Illustrative imageVisual mechanisms and deep learning
Deep convolutional neural networks (CNNs) are designed with reference to the brain's hierarchical structure. Rather than leaving deep learning as a black box, the lab seeks to understand it by linking it to visual representations in the brain, and to use that understanding as a source for new models.
*Illustrative imageUnderstanding the expert eye
Trained engineers and physicians are said to be highly skilled at distinguishing textures specific to their problems. Using methods from visual psychology, the lab aims to clarify the properties that textures have.
*Illustrative imageMachine learning that supports experts
Experts like these are always in short supply, and advanced fields such as materials science need machine learning applications that support people. Because such applications must be able to explain their results, the lab develops them carefully to fit each field.
MembersFaculty
Shono Lab members consist of faculty, affiliated faculty, and students in the Master's program and the undergraduate program.As of 2026
- ProfessorHayaru Shono
- Affiliated Associate ProfessorDaiki Kimura
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: “Convolutional Neural Network” Irisbox / CC BY 4.0 (cropped)
- Visual mechanisms and image processing: “Visual Cortex Infographic - 1” Martianeighbour / CC BY-SA 4.0 (cropped)
- Visual mechanisms and deep learning: “Neural Abstraction Pyramid” Sven Behnke / CC BY-SA 4.0 (cropped)
- Understanding the expert eye: “Visible light eye-tracking algorithm” Z22. Derivative work of the above file created by Björn Markmann. / CC BY 3.0 (cropped)
- Machine learning that supports experts: “Electron microscope Mamut at the Institute of Scientific Instruments of the Czech Academy of Science in Brno (9)” Akademie věd České republiky / Czech Academy of Science / CC BY-SA 3.0 cz (cropped)
- Related lab (Takagi Lab): “Utaco recording with Blue Snowball Microphone 20111101” Danny Choo / CC BY-SA 2.0 (cropped)
- Related lab (Takahashi Lab): “Peyman Majidi Prototyping a dimensioning system via image processing” پیمان مجیدی معین / CC BY-SA 4.0 (cropped)
- Related lab (Kohjima Lab): “Machine Learning cookies” Syced / CC0 (cropped)
- Related lab (Sei Lab): “Playing with Kilobots” QuarkyTale / CC BY-SA 4.0 (cropped)
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