What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Geometry
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Surface, Algorithm and Computer graphics.
Gabriel Taubin interconnects Query expansion, Information retrieval and Query optimization in the investigation of issues within Artificial intelligence.
His Computer vision study combines topics from a wide range of disciplines, such as Process and Surface reconstruction.
His Surface research is multidisciplinary, relying on both Smoothing, Point and Bump mapping.
His studies deal with areas such as Vertex, Theoretical computer science, Point cloud and Triangle mesh as well as Algorithm.
His Vertex normal study in the realm of Vertex interacts with subjects such as Laplacian smoothing.
His most cited work include:
- QBIC project: querying images by content, using color, texture, and shape (1779 citations)
- A signal processing approach to fair surface design (1546 citations)
- Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation (937 citations)
What are the main themes of his work throughout his whole career to date?
Gabriel Taubin mostly deals with Artificial intelligence, Computer vision, Computer graphics, Algorithm and Polygon mesh.
His Artificial intelligence research is multidisciplinary, incorporating elements of Process, Point and Surface, Surface reconstruction.
His Surface study is concerned with Geometry in general.
His work deals with themes such as Smoothing, Point cloud, Discrete mathematics, Theoretical computer science and Vertex, which intersect with Algorithm.
Gabriel Taubin has included themes like Image stitching, Topology, Data structure and Spanning tree in his Vertex study.
Gabriel Taubin combines subjects such as Computational science, Isosurface, Combinatorics and Polygon with his study of Polygon mesh.
He most often published in these fields:
- Artificial intelligence (40.61%)
- Computer vision (34.52%)
- Computer graphics (20.81%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (40.61%)
- Computer vision (34.52%)
- Projector (7.11%)
In recent papers he was focusing on the following fields of study:
Gabriel Taubin focuses on Artificial intelligence, Computer vision, Projector, Point cloud and Computer graphics.
In general Artificial intelligence, his work in Segmentation, 3D reconstruction and Bidirectional texture function is often linked to Gesture recognition and Absolute phase linking many areas of study.
His work on Image texture and Image as part of general Computer vision study is frequently linked to ENCODE and Small hand, therefore connecting diverse disciplines of science.
His Point cloud research includes elements of Algorithm, Approximation algorithm and Laser scanning.
His Algorithm research includes themes of Surface reconstruction, Quadratic function, Mathematical optimization, Focus and Point.
The concepts of his Computer graphics study are interwoven with issues in Object, Virtual machine and Frame rate.
Between 2013 and 2021, his most popular works were:
- Virtual reality methods and systems (47 citations)
- Laplacian Coordinates for Seeded Image Segmentation (44 citations)
- Falling with Style: Bats Perform Complex Aerial Rotations by Adjusting Wing Inertia (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Geometry
Gabriel Taubin mainly investigates Computer vision, Artificial intelligence, Image texture, Scale-space segmentation and Segmentation.
His Computer vision study combines topics in areas such as Field and Detector.
The Image, Camera resectioning and Projector research Gabriel Taubin does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Noise and Absolute phase, therefore creating a link between diverse domains of science.
His studies in Image texture integrate themes in fields like Color gradient, Texture mapping and Color balance.
His Scale-space segmentation research integrates issues from Point cloud, Iterated function, Graph, Cut and Pairwise comparison.
Segmentation is represented through his Image segmentation, Minimum spanning tree-based segmentation, Segmentation-based object categorization and Region growing research.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
