What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Operating system
His scientific interests lie mostly in Artificial intelligence, Computer vision, Robot, Mobile robot and Simulation.
Nikolaos Papanikolopoulos combines topics linked to Pattern recognition with his work on Artificial intelligence.
His study ties his expertise on Kalman filter together with the subject of Computer vision.
Many of his research projects under Robot are closely connected to Terrain with Terrain, tying the diverse disciplines of science together.
His study in Mobile robot is interdisciplinary in nature, drawing from both Robotic systems, Embedded system and SIMPLE.
His biological study spans a wide range of topics, including Spring, Remotely operated vehicle, Real-time computing, Task and Workstation.
His most cited work include:
- Detection and classification of vehicles (693 citations)
- Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision (538 citations)
- Multi-class active learning for image classification (277 citations)
What are the main themes of his work throughout his whole career to date?
Nikolaos Papanikolopoulos mainly focuses on Artificial intelligence, Computer vision, Robot, Mobile robot and Pattern recognition.
His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His Robot study incorporates themes from Distributed computing, Real-time computing and Simulation.
The various areas that he examines in his Mobile robot study include Control engineering, Embedded system and Human–computer interaction.
His Pattern recognition course of study focuses on Contextual image classification and Support vector machine.
His Eye tracking research includes elements of Optical flow and Adaptive control.
He most often published in these fields:
- Artificial intelligence (68.19%)
- Computer vision (49.20%)
- Robot (26.54%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (68.19%)
- Pattern recognition (13.50%)
- Segmentation (8.70%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Point cloud are his primary areas of study.
His research in Artificial intelligence intersects with topics in Contrast and Divergence.
His research in Pattern recognition tackles topics such as Ground truth which are related to areas like Test set and Background subtraction.
The study incorporates disciplines such as HTML5, Training set, Kidney tumor, Annotation and Computer-aided diagnosis in addition to Segmentation.
His Computer vision study combines topics in areas such as Representation and Pedestrian.
His Point cloud research includes themes of Object detection, Agricultural engineering, Persistent homology and Pipeline.
Between 2015 and 2021, his most popular works were:
- Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications (91 citations)
- The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes. (82 citations)
- The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge. (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Operating system
Nikolaos Papanikolopoulos mainly focuses on Artificial intelligence, Segmentation, Pattern recognition, Point cloud and Deep learning.
His Artificial intelligence study frequently draws connections between related disciplines such as Computer vision.
Nikolaos Papanikolopoulos combines subjects such as HTML5, Training set, Kidney tumor, Information retrieval and Web application with his study of Segmentation.
His work in Pattern recognition addresses issues such as Divergence, which are connected to fields such as Data modeling and Dirichlet process.
His Point cloud study also includes fields such as
- Robustness, Shape analysis, Topological space and Topological data analysis most often made with reference to Persistent homology,
- Object detection that connect with fields like Topology, Depth map, Cognitive neuroscience of visual object recognition, Image processing and RGB color model.
His Deep learning research focuses on subjects like Convolutional neural network, which are linked to Entropy, Task analysis, Active learning, Stochastic gradient descent and Feature extraction.
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