“FishTech” is a new technology which is a collaboration with fishery science, ocean science and informatics. Our goal is to develop FishTech for sustainable fishery which balances economic efficiency with resource management. We develop a new pattern recognition and data assimilation technology which employ domain knowledge of ecology of fish and oceanography, and analyze environmental data acquired in a process of fishing activities. Our technology supports short-term and long-term fishing operation providing suitable fishing spots, oceanographic conditions and fishery management plans.
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Research
Research Projects
We are studing intelligent information technologies and apply them to various application fields, such as education, tourism, and primary industries.
This research aims to accelerate the creation of detailed bathymetric charts. In this research, treating bathymetric charts as digital images whose pixel values represent ocean depths, we proposed to use superresolution, a technique to enhance image resolution, to estimate fine bathymetric information from coarse observation data. This approach enables us to make full use of existing data and minimize new observation, thereby realizing efficient mapping of seafloor details.
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3D reconstruction is a technique to reconstruct an object’s 3D shape from 2D images captured with cameras. This technique can be utilized for a variety of applications such as self-driving vehicles and AR/VR technology. We develop 3D reconstruction techniques in participating media. For example, the contrast of an image captured under murky water, fog, or smoke is corrupted by scattered light due to suspended particles. 3D reconstruction techniques in such environments enable various applications in the real world.
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Smart tourism that provides real-time and personal information support to tourists is emerging. In this research, we are studying technologies that can contribute to solving social problems such as over-tourism and disaster prevention by providing appropriate information to tourists by estimating and predicting the condition of them from sensor information such as GPS.
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Pen-stroke data analysis
For estimating "a person's comprehension level from pen strokes", we are developing methods to classify and visualize the solution process from sensor data obtained during the learning process. Specifically, we are developing methods to detect moments where students stumble in their answers and to analyze "typical" answer solutions using answer data obtained from tablet PCs.