What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His main research concerns Artificial intelligence, Matching, Scene statistics, Machine learning and Polygon mesh. The study incorporates disciplines such as Parametrization and Parametric surface in addition to Artificial intelligence. The study of Matching is intertwined with the study of Pattern recognition in a number of ways.
His research investigates the link between Pattern recognition and topics such as Image resolution that cross with problems in Surface reconstruction. His Machine learning research is multidisciplinary, incorporating perspectives in Process and Procedural modeling. Matthew Fisher interconnects Feature, Feature vector, Deep learning, Transformation and Computational geometry in the investigation of issues within Polygon mesh.
His most cited work include:
- 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions (328 citations)
- Real-time non-rigid reconstruction using an RGB-D camera (297 citations)
- Example-based synthesis of 3D object arrangements (230 citations)
What are the main themes of his work throughout his whole career to date?
Matthew Fisher mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Representation. His Artificial intelligence study focuses mostly on Deep learning, RGB color model, Object, Matching and Pixel. Matthew Fisher has included themes like Artificial neural network, Polygon mesh and Surface reconstruction in his Computer vision study.
His Pattern recognition research includes themes of Margin, Histogram and Structure. His Algorithm study also includes
- Parametric surface which intersects with area such as Generalization, Morphing and Point cloud,
- Benchmark most often made with reference to Voxel. His Representation study integrates concerns from other disciplines, such as Parametrization and Image segmentation.
He most often published in these fields:
- Artificial intelligence (60.23%)
- Computer vision (25.00%)
- Pattern recognition (21.59%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (60.23%)
- Computer vision (25.00%)
- Image (10.23%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Image, Deep learning and Pixel. His studies deal with areas such as Set and Pattern recognition as well as Artificial intelligence. Matthew Fisher performs integrative Computer vision and Reflection mapping research in his work.
His work deals with themes such as Map projection and Human–computer interaction, which intersect with Image. Matthew Fisher has included themes like Geometric data analysis, Representation and Graphics in his Deep learning study. He works mostly in the field of Object, limiting it down to concerns involving Polygon mesh and, occasionally, RGB color model, Face and Depth map.
Between 2019 and 2021, his most popular works were:
- Deep Parametric Shape Predictions Using Distance Fields (18 citations)
- Improved Techniques for Training Single-Image GANs (9 citations)
- Modeling Artistic Workflows for Image Generation and Editing. (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Deep learning, Pixel and Image. Matthew Fisher studies Image resolution, a branch of Artificial intelligence. The various areas that Matthew Fisher examines in his Pattern recognition study include Matching, Structure, Artificial neural network and Metric.
His Deep learning study incorporates themes from Geometric data analysis, Graphics, Range, Shape analysis and Rendering. The study incorporates disciplines such as Representation, Unsupervised learning and Compositing in addition to Pixel. Matthew Fisher interconnects Regularization, Generative model, Workflow and Human–computer interaction in the investigation of issues within Image.
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