What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Andreas Uhl mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Biometrics and Iris recognition.
His Feature extraction, Wavelet transform, Image processing and Deep learning study in the realm of Artificial intelligence connects with subjects such as Colonic Polyp.
His work carried out in the field of Pattern recognition brings together such families of science as Ground truth, Local binary patterns and Invariant.
The study incorporates disciplines such as Cryptography, Data mining and Fuzzy logic in addition to Biometrics.
His studies in Iris recognition integrate themes in fields like IRIS, Segmentation, Key and Iris.
His Wavelet study incorporates themes from Algorithm, Filter and Digital watermarking.
His most cited work include:
- A survey on biometric cryptosystems and cancelable biometrics (443 citations)
- Survey of wavelet-domain watermarking algorithms (265 citations)
- SELECTIVE BITPLANE ENCRYPTION FOR SECURE TRANSMISSION OF IMAGE DATA IN MOBILE ENVIRONMENTS (147 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Biometrics and Feature extraction are his primary areas of study.
Iris recognition, Wavelet, Wavelet transform, Segmentation and Contextual image classification are the primary areas of interest in his Artificial intelligence study.
His Wavelet research focuses on subjects like Algorithm, which are linked to Theoretical computer science.
His research on Computer vision often connects related areas such as Robustness.
His Pattern recognition study incorporates themes from Local binary patterns and Image.
His work carried out in the field of Biometrics brings together such families of science as Data mining and Identification.
He most often published in these fields:
- Artificial intelligence (59.03%)
- Computer vision (33.02%)
- Pattern recognition (31.93%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (59.03%)
- Pattern recognition (31.93%)
- Biometrics (21.03%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Biometrics, Computer vision and Finger vein recognition.
Artificial intelligence is a component of his Feature extraction, Iris recognition, Convolutional neural network, Segmentation and IRIS studies.
His Feature extraction research is multidisciplinary, relying on both Data mining and Robustness.
Andreas Uhl studied Pattern recognition and Fingerprint recognition that intersect with Matching and Fingerprint.
His Biometrics study integrates concerns from other disciplines, such as Facial recognition system, Face, Image warping, Image and Pattern recognition.
His Computer vision study combines topics in areas such as Authentication, Dorsum, Scanner and Data set.
Between 2016 and 2021, his most popular works were:
- Depreciating Motivation and Empirical Security Analysis of Chaos-Based Image and Video Encryption (88 citations)
- Deep Learning with Topological Signatures (51 citations)
- Deep Learning with Topological Signatures (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Andreas Uhl mostly deals with Artificial intelligence, Pattern recognition, Biometrics, Feature extraction and Computer vision.
His Iris recognition, Finger vein recognition, Convolutional neural network, IRIS and Segmentation study are his primary interests in Artificial intelligence.
His Pattern recognition research includes themes of Persistent homology, Spoofing attack, Margin, Deep learning and Perspective.
His Biometrics study combines topics from a wide range of disciplines, such as Word error rate, Image processing, Multivariate statistics, Eeg recording and Autoregressive model.
His research in Feature extraction intersects with topics in Image segmentation, Data mining, Geometric shape, Rotation and Support vector machine.
The Computer vision study combines topics in areas such as Dorsum and Data set.
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