What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Operating system
Armin B. Cremers spends much of his time researching Robot, Artificial intelligence, Mobile robot, Computer vision and Human–computer interaction.
The concepts of his Robot study are interwoven with issues in Probabilistic logic and Visual routine.
His study looks at the relationship between Artificial intelligence and topics such as Pattern recognition, which overlap with Contextual image classification and Softmax function.
His research in Mobile robot intersects with topics in Autonomous system, Simulation, Motion planning and Markov chain.
The various areas that Armin B. Cremers examines in his Human–computer interaction study include User interface, Software, Probabilistic analysis of algorithms and Component-based software engineering.
His research investigates the link between Mobile robot navigation and topics such as Personal robot that cross with problems in Ubiquitous robot and Multimedia.
His most cited work include:
- Experiences with an interactive museum tour-guide robot (724 citations)
- MINERVA: a second-generation museum tour-guide robot (558 citations)
- Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva (511 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, Robot, Database and Component.
His Pattern recognition research extends to the thematically linked field of Artificial intelligence.
His study in the field of Histogram, Object, Particle filter and Tracking is also linked to topics like Pedestrian detection.
His Robot research incorporates elements of Multimedia and Human–computer interaction.
In his study, Software engineering and Context is inextricably linked to Component-based software engineering, which falls within the broad field of Component.
In his research on the topic of Mobile robot, Real-time computing is strongly related with Simulation.
He most often published in these fields:
- Artificial intelligence (27.20%)
- Computer vision (14.23%)
- Robot (12.55%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (27.20%)
- Computer vision (14.23%)
- Pattern recognition (7.11%)
In recent papers he was focusing on the following fields of study:
Armin B. Cremers mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image segmentation.
His studies in Artificial intelligence integrate themes in fields like Score and Natural language processing.
His Computer vision research includes themes of Hierarchical clustering, Visualization, Mobile robot and Scale.
Specifically, his work in Mobile robot is concerned with the study of Robot control.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Pixel and Salient object detection.
His Image segmentation research is multidisciplinary, relying on both Change detection and Data mining.
Between 2013 and 2021, his most popular works were:
- Informed Haar-Like Features Improve Pedestrian Detection (231 citations)
- A multisize superpixel approach for salient object detection based on multivariate normal distribution estimation. (41 citations)
- A Cognitive Approach for Object Discovery (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Operating system
His scientific interests lie mostly in Computer vision, Artificial intelligence, Pedestrian detection, Pattern recognition and Discriminative model.
His Computer vision study frequently intersects with other fields, such as Mobile robot.
His Artificial intelligence study frequently draws parallels with other fields, such as Random walk.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Pixel, Compact space, Robustness and Salient object detection.
His Discriminative model study combines topics from a wide range of disciplines, such as Mathematical model, Advanced driver assistance systems, Boosting, Statistical model and Feature extraction.
His work carried out in the field of Feature extraction brings together such families of science as Visualization, Contrast and Scale.
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.
