What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Image segmentation and Image retrieval.
As a part of the same scientific family, he mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Fuzzy set.
His study on Pixel, Standard illuminant, Edge detection and Thresholding is often connected to Pigmented skin as part of broader study in Computer vision.
Gerald Schaefer has researched Pattern recognition in several fields, including Local binary patterns, Skin cancer and Mean-shift.
The various areas that he examines in his Image retrieval study include Visualization and Database.
His Database study incorporates themes from Quantization, Quantization, Color image, Uncompressed video and Ground truth.
His most cited work include:
- UCID: an uncompressed color image database (848 citations)
- Lesion border detection in dermoscopy images. (305 citations)
- Cost-sensitive decision tree ensembles for effective imbalanced classification (178 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Image retrieval and Feature extraction.
Artificial intelligence and Machine learning are commonly linked in his work.
His Machine learning research incorporates themes from Fuzzy classification and Fuzzy logic.
In his study, Palette is inextricably linked to Cluster analysis, which falls within the broad field of Pattern recognition.
His work carried out in the field of Image retrieval brings together such families of science as JPEG and Information retrieval.
His Image segmentation research is multidisciplinary, incorporating elements of Edge detection, Thresholding and Medical imaging.
He most often published in these fields:
- Artificial intelligence (72.32%)
- Computer vision (42.49%)
- Pattern recognition (32.19%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (72.32%)
- Pattern recognition (32.19%)
- Computer vision (42.49%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Image and Artificial neural network.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
His Pattern recognition research integrates issues from Pixel, Histogram, Local binary patterns and Image quality.
His Computer vision research focuses on Image retrieval and JPEG 2000.
Gerald Schaefer combines subjects such as Visualization and Information retrieval with his study of Image.
His studies in Artificial neural network integrate themes in fields like Fuzzy logic and Pattern recognition.
Between 2014 and 2021, his most popular works were:
- Fusing fine-tuned deep features for skin lesion classification. (49 citations)
- Skin Lesion Classification Using Hybrid Deep Neural Networks (44 citations)
- A Multi-Organ Nucleus Segmentation Challenge (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Pattern recognition, Metaheuristic, Classifier and Image segmentation.
His Machine learning research extends to Artificial intelligence, which is thematically connected.
His studies deal with areas such as Feature and Medical imaging as well as Pattern recognition.
His Metaheuristic study also includes
- Differential evolution which is related to area like Particle swarm optimization and Thresholding,
- Benchmark that intertwine with fields like Mathematical optimization, k-means clustering and Computational intelligence.
His research integrates issues of Decision tree, Breast cancer, Mammography and Computational biology in his study of Classifier.
His Feature extraction research entails a greater understanding of Computer vision.
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