What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Computer vision, Iterative reconstruction, Computer graphics and Graphics hardware.
His study brings together the fields of Machine learning and Artificial intelligence.
His study on Computer vision is mostly dedicated to connecting different topics, such as Interpolation.
He combines subjects such as Image processing, Algorithm, Tomography and Medical imaging with his study of Iterative reconstruction.
His Graphics hardware research includes themes of Simultaneous Algebraic Reconstruction Technique, Visualization and Lattice Boltzmann methods.
In the field of Visualization, his study on Parallel coordinates overlaps with subjects such as Artistic rendering.
His most cited work include:
- Transferring color to greyscale images (616 citations)
- Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware (244 citations)
- Real-time 3D computed tomographic reconstruction using commodity graphics hardware. (205 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Visualization, Computer graphics and Iterative reconstruction.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
The Computer vision study combines topics in areas such as Shading and Computed tomography.
Visualization is the subject of his research, which falls under Data mining.
His Iterative reconstruction research is multidisciplinary, incorporating perspectives in Image processing, Algorithm, Iterative method and Graphics hardware.
To a larger extent, Klaus Mueller studies Rendering with the aim of understanding Volume rendering.
He most often published in these fields:
- Artificial intelligence (42.00%)
- Computer vision (31.33%)
- Visualization (30.67%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (42.00%)
- Visualization (30.67%)
- Visual analytics (11.33%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Visualization, Visual analytics, Pattern recognition and Field.
Klaus Mueller has included themes like Generator, Machine learning and Computer vision in his Artificial intelligence study.
Klaus Mueller studies Image generation, a branch of Computer vision.
His Visualization study combines topics from a wide range of disciplines, such as Interface, Eye tracking, Computer graphics and Library science.
His biological study spans a wide range of topics, including Tree, Tree structure, Anomaly detection and Data science.
His Field research is multidisciplinary, incorporating elements of Interface, Domain knowledge and Human–computer interaction.
Between 2018 and 2021, his most popular works were:
- A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications (18 citations)
- Beyond saliency: Understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation (16 citations)
- ColorMap ND : A Data-Driven Approach and Tool for Mapping Multivariate Data to Color (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Artificial intelligence, Visualization, Visual analytics, Pattern recognition and Domain knowledge.
The concepts of his Artificial intelligence study are interwoven with issues in Style and Computer vision.
His Visualization research incorporates themes from Variety, Algorithm, Eye tracking and Time series.
He combines subjects such as Tree, Social network, Dashboard and Domain with his study of Visual analytics.
The various areas that Klaus Mueller examines in his Pattern recognition study include Construct, Human visual system model, Bilinear interpolation and Source code.
His Domain knowledge study combines topics in areas such as Active learning, Field, Human–computer interaction, Need for cognition and Interface.
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