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.
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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
Le Hou;Dimitris Samaras;Tahsin M. Kurc;Yi Gao.
computer vision and pattern recognition (2016)
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.
Joel Saltz;Rajarsi Gupta;Le Hou;Tahsin Kurc.
Cell Reports (2018)
Two-person interaction detection using body-pose features and multiple instance learning
Kiwon Yun;Jean Honorio;Debaleena Chattopadhyay;Tamara L. Berg.
computer vision and pattern recognition (2012)
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham;Michael P. Milham;Adriana Di Martino;R. Cameron Craddock.
Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics
Lei Zhang;D. Samaras.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)
Is decreased prefrontal cortical sensitivity to monetary reward associated with impaired motivation and self-control in cocaine addiction?
Rita Z. Goldstein;Nelly Alia-Klein;Dardo Tomasi;Lei Zhang.
American Journal of Psychiatry (2007)
Neural Face Editing with Intrinsic Image Disentangling
Zhixin Shu;Ersin Yumer;Sunil Hadap;Kalyan Sunkavalli.
computer vision and pattern recognition (2017)
Face recognition under variable lighting using harmonic image exemplars
Lie Zhang;D. Samaras.
computer vision and pattern recognition (2003)
Dense non-rigid surface registration using high-order graph matching
Yun Zeng;Chaohui Wang;Yang Wang;Xianfeng Gu.
computer vision and pattern recognition (2010)
High Resolution Acquisition, Learning and Transfer of Dynamic 3‐D Facial Expressions
Yang Wang;Xiaolei Huang;Chan-Su Lee;Song Zhang.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: