What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His main research concerns Artificial intelligence, Computer vision, Active appearance model, Pattern recognition and Chromatic adaptation.
His work is connected to Color emotion, CIECAM02 and Gamut, as a part of Artificial intelligence.
M. Ronnier Luo combines subjects such as Harmony and Preference with his study of Color emotion.
M. Ronnier Luo does research in Computer vision, focusing on Metamerism specifically.
His Pattern recognition study incorporates themes from Data set and Colour model.
His Chromatic adaptation study combines topics from a wide range of disciplines, such as Algorithm and Brightness.
His most cited work include:
- A study of colour emotion and colour preference. Part I: Colour emotions for single colours (421 citations)
- The CIECAM02 color appearance model (280 citations)
- A study of digital camera colorimetric characterization based on polynomial modeling (274 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Optics, CIECAM02 and Pattern recognition.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Computer graphics.
Computer vision is closely attributed to Brightness in his work.
In Optics, he works on issues like Color space, which are connected to Color histogram, Color balance and RGB color model.
In his research, M. Ronnier Luo performs multidisciplinary study on Pattern recognition and Colour difference.
His research in Gamut intersects with topics in Observer, Experimental data and Mapping algorithm.
He most often published in these fields:
- Artificial intelligence (59.81%)
- Computer vision (45.33%)
- Optics (26.17%)
What were the highlights of his more recent work (between 2013-2019)?
- Artificial intelligence (59.81%)
- Computer vision (45.33%)
- Optics (26.17%)
In recent papers he was focusing on the following fields of study:
M. Ronnier Luo mainly focuses on Artificial intelligence, Computer vision, Optics, Luminance and Color temperature.
His work on Lightness is typically connected to Current as part of general Artificial intelligence study, connecting several disciplines of science.
When carried out as part of a general Optics research project, his work on Metamerism and Colorimetry is frequently linked to work in Matrix, therefore connecting diverse disciplines of study.
In his study, Contrast ratio is strongly linked to Glare, which falls under the umbrella field of Luminance.
His Pattern recognition research focuses on subjects like Fruit juice, which are linked to Hue and Harmony.
The study incorporates disciplines such as Computer graphics and Metric in addition to Gamut.
Between 2013 and 2019, his most popular works were:
- Light dominates colour preference when correlated colour temperature differs (31 citations)
- Gamut Volume Index: a color preference metric based on meta-analysis and optimized colour samples (25 citations)
- A study of atmosphere perception of dynamic coloured light (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
M. Ronnier Luo mainly focuses on Artificial intelligence, Computer vision, Color temperature, LED lamp and Preference.
The Hue research M. Ronnier Luo does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Tablet computer, therefore creating a link between diverse domains of science.
His work on Rendering as part of general Computer vision study is frequently linked to Negative deviation, therefore connecting diverse disciplines of science.
M. Ronnier Luo has included themes like Second derivative, Current and Observer in his Color temperature study.
His Preference research includes themes of Brightness, Meteorology and Simulation.
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