What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Reinhard Klette mainly investigates Artificial intelligence, Computer vision, Algorithm, Digital geometry and Pattern recognition.
His Artificial intelligence and Image, Motion analysis, Convolutional neural network, Segmentation and Object investigations all form part of his Artificial intelligence research activities.
His study brings together the fields of Computer graphics and Computer vision.
His biological study spans a wide range of topics, including Digitization and Image segmentation.
His Digital geometry research is multidisciplinary, incorporating elements of Planarity testing, Arc length, Computational geometry and Curvature.
His study explores the link between Pattern recognition and topics such as Artificial neural network that cross with problems in Hidden Markov model, Hyperspectral imaging and Support vector machine.
His most cited work include:
- Digital Geometry: Geometric Methods for Digital Picture Analysis (428 citations)
- Computer Vision: Three-Dimensional Data from Images (313 citations)
- Automatic human model generation (218 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, Algorithm, Computer graphics and Pattern recognition.
Stereopsis, Image, Segmentation, Optical flow and Ground truth are subfields of Artificial intelligence in which his conducts study.
His biological study spans a wide range of topics, including Kalman filter and Advanced driver assistance systems.
His research integrates issues of Image processing, Digital geometry and Photometric stereo in his study of Algorithm.
Reinhard Klette combines subjects such as Epipolar geometry and Visualization with his study of Computer graphics.
His Pattern recognition research focuses on Feature extraction in particular.
He most often published in these fields:
- Artificial intelligence (56.08%)
- Computer vision (44.79%)
- Algorithm (16.32%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (56.08%)
- Computer vision (44.79%)
- Pattern recognition (8.68%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Deep learning.
His Object detection, Image, Visual odometry, Segmentation and Image segmentation study are his primary interests in Artificial intelligence.
His biological study deals with issues like Kalman filter, which deal with fields such as Ground truth.
His work deals with themes such as Iterative reconstruction and Feature, which intersect with Pattern recognition.
His research in Convolutional neural network tackles topics such as Anomaly detection which are related to areas like Representation.
His Deep learning research focuses on Convolution and how it connects with Recurrent neural network, Network architecture and Key.
Between 2015 and 2021, his most popular works were:
- Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes (164 citations)
- Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes (138 citations)
- STFCN: Spatio-Temporal FCN for Semantic Video Segmentation. (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Hyperspectral imaging.
His Artificial intelligence study is mostly concerned with Deep learning, Anomaly detection, Feature, Convolution and Object detection.
His work in the fields of Feature extraction overlaps with other areas such as Muscle type.
The Computer vision study combines topics in areas such as Lidar and Curve fitting.
The various areas that he examines in his Convolutional neural network study include Feature, Artificial neural network, Image, Transfer of learning and Glaucoma.
His work carried out in the field of Hyperspectral imaging brings together such families of science as Support vector machine and Snapshot.
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