What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Rainer Lienhart mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Information retrieval and Video processing.
His Artificial intelligence study frequently links to related topics such as Hit rate.
His study in the fields of Image processing, Motion, Optical character recognition and Segmentation under the domain of Computer vision overlaps with other disciplines such as Boundary detection.
His studies in Pattern recognition integrate themes in fields like Cognitive neuroscience of visual object recognition, Machine learning, Scalability and Image retrieval.
His Object detection research incorporates themes from Contextual image classification, Constant false alarm rate and Face detection.
His work investigates the relationship between Constant false alarm rate and topics such as Facial recognition system that intersect with problems in Feature and Feature extraction.
His most cited work include:
- An extended set of Haar-like features for rapid object detection (2415 citations)
- Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection (667 citations)
- Localizing and segmenting text in images and videos (424 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Multimedia and Feature are his primary areas of study.
Artificial intelligence is closely attributed to Machine learning in his study.
Rainer Lienhart combines subjects such as Event and Frame with his study of Computer vision.
His research investigates the connection between Pattern recognition and topics such as Probabilistic latent semantic analysis that intersect with issues in Scale-invariant feature transform.
His Feature research is multidisciplinary, incorporating elements of Object and Data mining.
His Object detection study combines topics in areas such as Viola–Jones object detection framework and Constant false alarm rate.
He most often published in these fields:
- Artificial intelligence (52.97%)
- Computer vision (29.22%)
- Pattern recognition (24.66%)
What were the highlights of his more recent work (between 2017-2020)?
- Artificial intelligence (52.97%)
- Computer vision (29.22%)
- Multimedia (17.81%)
In recent papers he was focusing on the following fields of study:
Rainer Lienhart mainly focuses on Artificial intelligence, Computer vision, Multimedia, Natural language processing and Pose.
His research on Artificial intelligence often connects related areas such as Pattern recognition.
His Pattern recognition research is multidisciplinary, relying on both Representation and Projection.
His Computer vision research includes elements of Frame, Translation and Task.
His work deals with themes such as Field and Session, which intersect with Multimedia.
His Natural language processing study integrates concerns from other disciplines, such as Word, X ray image and Focus.
Between 2017 and 2020, his most popular works were:
- Multimodal Image Captioning for Marketing Analysis (11 citations)
- Activity-Conditioned Continuous Human Pose Estimation for Performance Analysis of Athletes Using the Example of Swimming (9 citations)
- Refining Joint Locations for Human Pose Tracking in Sports Videos (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Rainer Lienhart spends much of his time researching Artificial intelligence, Computer vision, Deep learning, Natural language processing and Closed captioning.
His Artificial intelligence research includes themes of Frame and Focus.
His work on Kernel and Image resolution as part of general Computer vision research is often related to Baseline and Aquatic environment, thus linking different fields of science.
His Deep learning study combines topics from a wide range of disciplines, such as Calibration, Ground truth, Moment and Mean squared error.
His Closed captioning research is multidisciplinary, incorporating perspectives in Question answering and Multimedia.
Rainer Lienhart has included themes like Partition and Measure in his Pattern recognition study.
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