What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Computer vision, Pattern recognition, Face and Facial recognition system are his primary areas of study.
His study in Image texture, Image, Object detection, Deep learning and Point cloud is carried out as part of his studies in Artificial intelligence.
Dimitris Samaras combines subjects such as Representation and Robustness with his study of Computer vision.
His Pattern recognition study incorporates themes from Contextual image classification and Pose.
His Facial recognition system research is multidisciplinary, relying on both Spherical harmonic lighting and Pattern recognition.
His work carried out in the field of Convolutional neural network brings together such families of science as Cancer, Artificial neural network, Carcinoma, Visualization and Discriminative model.
His most cited work include:
- Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification (306 citations)
- Two-person interaction detection using body-pose features and multiple instance learning (292 citations)
- Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example (290 citations)
What are the main themes of his work throughout his whole career to date?
Dimitris Samaras mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Face.
Many of his studies involve connections with topics such as Machine learning and Artificial intelligence.
His Machine learning research incorporates themes from Functional magnetic resonance imaging and Benchmark.
The study incorporates disciplines such as Facial expression and Robustness in addition to Computer vision.
In most of his Pattern recognition studies, his work intersects topics such as Ground truth.
His Facial recognition system research is multidisciplinary, incorporating elements of Spherical harmonics and Image texture.
He most often published in these fields:
- Artificial intelligence (80.62%)
- Computer vision (42.73%)
- Pattern recognition (36.12%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (80.62%)
- Pattern recognition (36.12%)
- Computer vision (42.73%)
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, Deep learning and Segmentation.
His studies link Machine learning with Artificial intelligence.
His work on Classifier as part of his general Pattern recognition study is frequently connected to Pipeline, thereby bridging the divide between different branches of science.
His study looks at the relationship between Classifier and topics such as Support vector machine, which overlap with Speech recognition and Eye movement.
Dimitris Samaras has included themes like Unsupervised learning and Representation in his Computer vision study.
His research in Deep learning intersects with topics in Immunohistochemistry, Face, Digital pathology and Convolutional neural network.
Between 2018 and 2021, his most popular works were:
- Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images (64 citations)
- Topology-Preserving Deep Image Segmentation (37 citations)
- Shadow Removal via Shadow Image Decomposition (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Dimitris Samaras spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Image segmentation and Computer vision.
His Artificial intelligence research includes elements of Generator and Task analysis.
Dimitris Samaras has researched Pattern recognition in several fields, including Deep learning and Distribution.
His study in Segmentation is interdisciplinary in nature, drawing from both Digital pathology, Artificial neural network, Topology, Rand index and Topology.
He interconnects Cancer and Generalization in the investigation of issues within Image segmentation.
His Computer vision study which covers Unsupervised learning that intersects with Autoencoder, Semi-supervised learning and Representation.
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