What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Masatoshi Okutomi mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Pixel and Demosaicing.
His research investigates the connection between Artificial intelligence and topics such as Matching that intersect with issues in Similarity measure.
Computer vision is closely attributed to Algorithm in his study.
The Pattern recognition study combines topics in areas such as Weighting, Salt-and-pepper noise, Feature and Gradient noise.
His work on Subpixel rendering as part of general Pixel research is frequently linked to Process, thereby connecting diverse disciplines of science.
His research in Demosaicing intersects with topics in Color filter array and Interpolation.
His most cited work include:
- A stereo matching algorithm with an adaptive window: theory and experiment (962 citations)
- A multiple-baseline stereo (906 citations)
- Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions (259 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Image, Image processing and Pixel.
Masatoshi Okutomi interconnects Position and Pattern recognition in the investigation of issues within Artificial intelligence.
His work investigates the relationship between Computer vision and topics such as Color filter array that intersect with problems in Demosaicing.
His work carried out in the field of Image brings together such families of science as Basis and Surface.
His Image processing research incorporates elements of Image quality, Image resolution and Image formation.
His Pixel research is multidisciplinary, incorporating elements of Motion estimation, Image sensor, Image registration and Superresolution.
He most often published in these fields:
- Artificial intelligence (83.06%)
- Computer vision (73.50%)
- Image (22.13%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (83.06%)
- Computer vision (73.50%)
- RGB color model (7.38%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, RGB color model, 3D reconstruction and Iterative reconstruction.
His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
His research on Computer vision frequently connects to adjacent areas such as Visualization.
Masatoshi Okutomi has included themes like Projector, Data set, Rendering and Hyperspectral imaging in his RGB color model study.
While the research belongs to areas of 3D reconstruction, Masatoshi Okutomi spends his time largely on the problem of Translation, intersecting his research to questions surrounding Image generation.
His study looks at the relationship between Iterative reconstruction and fields such as Luminance, as well as how they intersect with chemical problems.
Between 2018 and 2021, his most popular works were:
- Single-Sensor RGB-NIR Imaging: High-Quality System Design and Prototype Implementation (16 citations)
- 3D Reconstruction of Whole Stomach from Endoscope Video Using Structure-from-Motion (13 citations)
- Is This the Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Artificial intelligence, Computer vision, Visualization, 3D reconstruction and Pose are his primary areas of study.
His study on Jpeg compression and Convolutional neural network is often connected to Quality, Factor and Estimation as part of broader study in Artificial intelligence.
Masatoshi Okutomi integrates Computer vision and Process in his studies.
The concepts of his Visualization study are interwoven with issues in Augmented reality, Real-time computing, Robot, Teleoperation and Feature extraction.
He studied 3D reconstruction and Iterative reconstruction that intersect with Projector, Structured light, Solid modeling and Reflectivity.
The study incorporates disciplines such as Pixel, Image sensor, Hyperspectral imaging and Data set in addition to RGB color model.
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