What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Erhardt Barth spends much of his time researching Artificial intelligence, Computer vision, Computer graphics, Machine learning and Computer graphics.
He focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Pattern recognition and, in certain cases, Matching pursuit.
His studies deal with areas such as Visual processing, Robustness and Eye movement as well as Computer vision.
His study in the field of Deep learning, Regularization and Recurrent neural network also crosses realms of Color normalization and Jaccard index.
His Computer graphics research incorporates themes from Mixed reality and Gesture recognition.
His Eye tracking research integrates issues from Facial recognition system, Dot product, Gaze and Low contrast.
His most cited work include:
- Variability of eye movements when viewing dynamic natural scenes. (303 citations)
- ACCURATE EYE CENTRE LOCALISATION BY MEANS OF GRADIENTS (211 citations)
- How honeybees make grazing landings on flat surfaces (189 citations)
What are the main themes of his work throughout his whole career to date?
Erhardt Barth mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Gaze and Eye movement.
His Artificial intelligence study focuses mostly on Image processing, Neural coding, Feature extraction, Structure tensor and Convolutional neural network.
His Computer vision course of study focuses on Curse of dimensionality and Cognitive neuroscience of visual object recognition.
In Pattern recognition, he works on issues like MNIST database, which are connected to Unsupervised learning.
Erhardt Barth interconnects Human–computer interaction, Temporal resolution, Perception and Driving simulator in the investigation of issues within Gaze.
His Eye movement research is multidisciplinary, relying on both Visual perception and Salience.
He most often published in these fields:
- Artificial intelligence (73.33%)
- Computer vision (47.69%)
- Pattern recognition (29.23%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (73.33%)
- Pattern recognition (29.23%)
- Convolutional neural network (6.67%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Convolutional neural network, Machine learning and Deep learning.
The Artificial intelligence study which covers Computer vision that intersects with Self-similarity.
His Pattern recognition study incorporates themes from Transfer of learning and Sensitivity.
His Convolutional neural network research incorporates elements of Feature, Discriminative model, Ear recognition and Speech recognition.
Many of his research projects under Machine learning are closely connected to Jaccard index with Jaccard index, tying the diverse disciplines of science together.
The various areas that he examines in his Deep learning study include Watershed and Algorithm.
Between 2015 and 2021, his most popular works were:
- A Hybrid Convolutional Variational Autoencoder for Text Generation (115 citations)
- Recurrent Dropout without Memory Loss (89 citations)
- A Hybrid Convolutional Variational Autoencoder for Text Generation (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Regularization, Recurrent neural network and Contrast.
His Deep learning study in the realm of Artificial intelligence connects with subjects such as Property.
His Pattern recognition study integrates concerns from other disciplines, such as Gaze, Social psychology, Feature and Brain mapping.
His Feature research is within the category of Computer vision.
Erhardt Barth has included themes like Language model, Speech recognition, Autoencoder and Text generation in his Contrast study.
His studies in Machine learning integrate themes in fields like Image segmentation and Digital pathology.
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