What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Yasuyuki Matsushita focuses on Artificial intelligence, Computer vision, Iterative reconstruction, Reflectivity and Photometric stereo.
His study brings together the fields of Pattern recognition and Artificial intelligence.
His Computer vision study frequently draws parallels with other fields, such as Robustness.
His biological study deals with issues like Computer graphics, which deal with fields such as Quality, Range and Surface.
Yasuyuki Matsushita interconnects RGB color model, Sparse matrix and Inverse in the investigation of issues within Reflectivity.
His Photometric stereo research incorporates themes from Low-rank approximation, Matrix norm, Heuristic and Matrix completion.
His most cited work include:
- Traffic monitoring and accident detection at intersections (440 citations)
- Full-frame video stabilization with motion inpainting (427 citations)
- Motion detail preserving optical flow estimation (397 citations)
What are the main themes of his work throughout his whole career to date?
Yasuyuki Matsushita spends much of his time researching Artificial intelligence, Computer vision, Photometric stereo, Algorithm and Computer graphics.
Yasuyuki Matsushita has included themes like Surface and Pattern recognition in his Artificial intelligence study.
His research integrates issues of Object, Image noise, Categorization and Outlier in his study of Pattern recognition.
His Computer vision study frequently links to related topics such as Reflectivity.
His Photometric stereo study integrates concerns from other disciplines, such as Isotropy, Stereo camera, Calibration and Normal.
His study in the field of Singular value decomposition is also linked to topics like Shot noise.
He most often published in these fields:
- Artificial intelligence (77.16%)
- Computer vision (62.94%)
- Photometric stereo (21.32%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (77.16%)
- Computer vision (62.94%)
- Photometric stereo (21.32%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Photometric stereo, Normal and Reflectivity.
His Artificial intelligence study frequently draws connections to adjacent fields such as Pattern recognition.
The study incorporates disciplines such as Image based and Feature in addition to Pattern recognition.
Yasuyuki Matsushita has researched Computer vision in several fields, including Time of flight and Calibration.
His Photometric stereo research is multidisciplinary, incorporating elements of Computational photography, Polarization, Cast shadow, Structured light and Remote sensing.
His biological study spans a wide range of topics, including Algorithm, Computation and Point.
Between 2018 and 2021, his most popular works were:
- Self-Calibrating Deep Photometric Stereo Networks (22 citations)
- Learning to Minify Photometric Stereo (15 citations)
- Deep Photometric Stereo for Non-Lambertian Surfaces. (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Optics
Artificial intelligence, Computer vision, Photometric stereo, Normal and Image formation are his primary areas of study.
His Artificial intelligence research is multidisciplinary, relying on both Isotropy and Surface.
A large part of his Computer vision studies is devoted to Stereo camera.
His Photometric stereo research includes elements of Point, Local search and Robustness.
His studies in Normal integrate themes in fields like Light intensity, Bilinear interpolation, Calibration and Minification.
His research in Image formation intersects with topics in Flashlight, Normal mapping, Stereoscopy and Flash.
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