What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
His primary areas of investigation include Artificial intelligence, Computer vision, Image, Computer graphics and Bilateral filter.
His research on Artificial intelligence often connects related topics like CAD.
Computer vision is closely attributed to Hallucinating in his study.
His study on Histogram is often connected to Tone as part of broader study in Image.
The study incorporates disciplines such as Adaptive histogram equalization and Subject in addition to Computer graphics.
In his work, Library science and Computer graphics is strongly intertwined with Noise reduction, which is a subfield of Bilateral filter.
His most cited work include:
- Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines (638 citations)
- A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach (549 citations)
- Real-time edge-aware image processing with the bilateral grid (441 citations)
What are the main themes of his work throughout his whole career to date?
Sylvain Paris mainly investigates Artificial intelligence, Computer vision, Image, Computer graphics and Image processing.
His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Pattern recognition, with regards to Kernel.
His work on Pixel, Bilateral filter, Rendering and Computational photography as part of his general Computer vision study is frequently connected to Process, thereby bridging the divide between different branches of science.
He has researched Image in several fields, including Transformation and Machine learning.
He has included themes like Video tracking and Video editing in his Computer graphics study.
The various areas that Sylvain Paris examines in his Image processing study include Range, Algorithm and Tone mapping.
He most often published in these fields:
- Artificial intelligence (75.76%)
- Computer vision (65.91%)
- Image (30.30%)
What were the highlights of his more recent work (between 2016-2020)?
- Artificial intelligence (75.76%)
- Image (30.30%)
- Computer vision (65.91%)
In recent papers he was focusing on the following fields of study:
Sylvain Paris focuses on Artificial intelligence, Image, Computer vision, Computer graphics and Painting.
His Artificial intelligence study typically links adjacent topics like Pattern recognition.
In his research on the topic of Image, Color space is strongly related with Transformation.
His work on Pixel and Exposure fusion as part of general Computer vision research is frequently linked to Context, Crowdsourcing and Brightness, thereby connecting diverse disciplines of science.
His Pixel study integrates concerns from other disciplines, such as Lightness, Color histogram, Face and Position.
The concepts of his Computer graphics study are interwoven with issues in Light field and Video processing.
Between 2016 and 2020, his most popular works were:
- Deep Photo Style Transfer (352 citations)
- Semantic soft segmentation (57 citations)
- GANSpace: Discovering Interpretable GAN Controls (56 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, Pattern recognition, Image editing and Representation.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Transformation and Position.
His study in Computer vision focuses on Image, Lightness, Color histogram, Pixel and Face.
Image is closely attributed to Computer graphics in his research.
His Pattern recognition research integrates issues from Compositing and Thesaurus.
His research integrates issues of Object, Segmentation, Laplacian matrix and Surface geometry in his study of Image editing.
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.
