What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
Matthew T. Uyttendaele spends much of his time researching Artificial intelligence, Computer vision, Computer graphics, Geography and Pixel.
His Artificial intelligence study frequently draws connections to adjacent fields such as Computer engineering.
His work in Computer vision addresses subjects such as Interpolation, which are connected to disciplines such as Panoramic photography.
His biological study spans a wide range of topics, including Image stitching and Panorama.
The study incorporates disciplines such as Video post-processing and Variety in addition to Image-based modeling and rendering.
His View synthesis research is multidisciplinary, relying on both 3D rendering, Real-time rendering and Image-based lighting.
His most cited work include:
- High-quality video view interpolation using a layered representation (1362 citations)
- Joint bilateral upsampling (942 citations)
- Deep photo: model-based photograph enhancement and viewing (586 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Computer graphics, Pixel and Geography.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Tone mapping and Dynamic range.
Borrowing concepts from Process, Matthew T. Uyttendaele weaves in ideas under Computer vision.
His work on Rendering as part of general Computer graphics study is frequently linked to Zoom, bridging the gap between disciplines.
Within one scientific family, Matthew T. Uyttendaele focuses on topics pertaining to Video post-processing under Rendering, and may sometimes address concerns connected to Real-time rendering.
Matthew T. Uyttendaele has researched Pixel in several fields, including Exposure compensation, Linear system and Projection.
He most often published in these fields:
- Artificial intelligence (81.18%)
- Computer vision (80.00%)
- Computer graphics (45.88%)
What were the highlights of his more recent work (between 2011-2019)?
- Artificial intelligence (81.18%)
- Computer vision (80.00%)
- Image (21.18%)
In recent papers he was focusing on the following fields of study:
Matthew T. Uyttendaele focuses on Artificial intelligence, Computer vision, Image, Structure from motion and Range.
Many of his research projects under Artificial intelligence are closely connected to Scale and Chaining with Scale and Chaining, tying the diverse disciplines of science together.
His Computer vision research includes themes of Representation and Computation.
His Image study combines topics in areas such as Web page, World Wide Web, Noise reduction and Information retrieval.
His study looks at the intersection of Structure from motion and topics like Feature with Matching, Point cloud and Pose.
His Range study combines topics from a wide range of disciplines, such as Motion, Contrast, Frame rate and Image stabilization.
Between 2011 and 2019, his most popular works were:
- ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation (114 citations)
- Real-time image-based 6-DOF localization in large-scale environments (88 citations)
- Fast burst images denoising (77 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Image, Structure from motion and Range.
His work on Orientation and Iterative reconstruction as part of general Artificial intelligence research is frequently linked to Network planning and design and Orders of magnitude, bridging the gap between disciplines.
His studies deal with areas such as Image based and Video camera as well as Orientation.
His study in Iterative reconstruction is interdisciplinary in nature, drawing from both Matching, Feature extraction, Pose and Video tracking.
The concepts of his Feature extraction study are interwoven with issues in Point cloud and Feature.
His Range research is multidisciplinary, incorporating elements of Motion, Frame rate and Image stabilization.
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