What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Optics
Uwe Stilla spends much of his time researching Artificial intelligence, Computer vision, Remote sensing, Synthetic aperture radar and Feature extraction.
His study ties his expertise on Pattern recognition together with the subject of Artificial intelligence.
Uwe Stilla interconnects Orientation and Level of detail in the investigation of issues within Remote sensing.
His research integrates issues of Image sensor, Radar imaging, Computational science and Signal processing in his study of Synthetic aperture radar.
He has researched Feature extraction in several fields, including Rural area, Light scattering and Filter.
His study on Image segmentation is often connected to Traffic analysis as part of broader study in Segmentation.
His most cited work include:
- 3D segmentation of single trees exploiting full waveform LIDAR data (319 citations)
- Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks (305 citations)
- Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection (250 citations)
What are the main themes of his work throughout his whole career to date?
Uwe Stilla mainly investigates Artificial intelligence, Computer vision, Remote sensing, Point cloud and Pattern recognition.
His work investigates the relationship between Artificial intelligence and topics such as Lidar that intersect with problems in Ranging.
His Computer vision study frequently draws connections between adjacent fields such as Focus.
His work on Synthetic aperture radar, Remote sensing, Interferometric synthetic aperture radar and Layover as part of general Remote sensing study is frequently linked to High resolution, bridging the gap between disciplines.
His work deals with themes such as Inverse synthetic aperture radar and Radar imaging, which intersect with Synthetic aperture radar.
The Point cloud study combines topics in areas such as Data mining, Photogrammetry, Point, Voxel and Laser scanning.
He most often published in these fields:
- Artificial intelligence (65.99%)
- Computer vision (52.61%)
- Remote sensing (27.21%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (65.99%)
- Point cloud (26.08%)
- Computer vision (52.61%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Point cloud, Computer vision, Pattern recognition and Segmentation.
His research investigates the link between Artificial intelligence and topics such as Graph that cross with problems in Smoothing.
His Point cloud research incorporates elements of Data mining, Artificial neural network, Photogrammetry, Lidar and Point.
His Lidar research is multidisciplinary, relying on both Focus, Ranging, Histogram and Laser scanning.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Graphical model, Random forest and Probabilistic logic.
His biological study spans a wide range of topics, including Convolutional neural network and Cluster analysis.
Between 2016 and 2021, his most popular works were:
- Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection (250 citations)
- Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark (33 citations)
- Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Optics
His primary areas of investigation include Artificial intelligence, Point cloud, Computer vision, Pattern recognition and Voxel.
His study explores the link between Artificial intelligence and topics such as Laser scanning that cross with problems in Lidar.
His Point cloud study integrates concerns from other disciplines, such as Graph, Data mining, Photogrammetry, Transformation and Algorithm.
His research in the fields of Matching, Orientation and Change detection overlaps with other disciplines such as Building information modeling.
His Pattern recognition study combines topics from a wide range of disciplines, such as Graphical model and Collinearity.
His Voxel research incorporates themes from RANSAC and Graph based.
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