His primary scientific interests are in Artificial intelligence, Computer vision, Deep learning, Video tracking and Pattern recognition. Artificial intelligence is a component of his Image segmentation, Feature extraction, Segmentation, Artificial neural network and Feature vector studies. His work on Motion compensation, Multiview Video Coding and Object detection as part of general Computer vision research is often related to DBSCAN, thus linking different fields of science.
Anastasios Doulamis combines subjects such as Support vector machine, Outlier, Real-time computing, Task and Convolutional neural network with his study of Deep learning. His study in Convolutional neural network is interdisciplinary in nature, drawing from both Activity recognition, Facial recognition system, Pose and Perceptron. In his research, Edge detection, Depth map and Image processing is intimately related to Automatic summarization, which falls under the overarching field of Video tracking.
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Deep learning and Artificial neural network. His Artificial intelligence study often links to related topics such as Machine learning. Computer vision and Robustness are commonly linked in his work.
His study looks at the intersection of Pattern recognition and topics like Image retrieval with Information retrieval. His work carried out in the field of Deep learning brings together such families of science as Pixel and Convolutional neural network. His Multiview Video Coding research incorporates elements of Motion compensation, Block-matching algorithm and Video compression picture types.
His main research concerns Artificial intelligence, Deep learning, Pattern recognition, Convolutional neural network and Dance. The study incorporates disciplines such as Machine learning, Layer and Computer vision in addition to Artificial intelligence. The concepts of his Deep learning study are interwoven with issues in Pixel, Segmentation and Identification.
His Pattern recognition research focuses on subjects like Image, which are linked to Cluster analysis. Anastasios Doulamis has researched Convolutional neural network in several fields, including Transfer of learning, Algorithm and Robustness. His Artificial neural network research is multidisciplinary, relying on both Data modeling and Hidden Markov model.
Anastasios Doulamis mainly investigates Artificial intelligence, Deep learning, Convolutional neural network, Energy and Machine learning. His Artificial intelligence research includes elements of Dance and Pattern recognition. His work on Segmentation, Contour segmentation and Binary classification is typically connected to Fusion as part of general Pattern recognition study, connecting several disciplines of science.
His Deep learning study combines topics in areas such as Real-time computing, Hidden Markov model and Sensor fusion. As part of one scientific family, he deals mainly with the area of Convolutional neural network, narrowing it down to issues related to the Robustness, and often AC power, Noise, Lidar, Support vector machine and Data modeling. Anastasios Doulamis studies Artificial neural network, a branch of Machine learning.
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.
Deep Learning for Computer Vision: A Brief Review.
Athanasios Voulodimos;Nikolaos Doulamis;Anastasios D. Doulamis;Eftychios Protopapadakis.
Computational Intelligence and Neuroscience (2018)
Deep supervised learning for hyperspectral data classification through convolutional neural networks
Konstantinos Makantasis;Konstantinos Karantzalos;Anastasios Doulamis;Nikolaos Doulamis.
international geoscience and remote sensing symposium (2015)
A fuzzy video content representation for video summarization and content-based retrieval
Anastasios D. Doulamis;Nikolaos D. Doulamis;Stefanos D. Kollias.
Signal Processing (2000)
On-line retrainable neural networks: improving the performance of neural networks in image analysis problems
A.D. Doulamis;N.D. Doulamis;S.D. Kollias.
IEEE Transactions on Neural Networks (2000)
Low bit-rate coding of image sequences using adaptive regions of interest
N. Doulamis;A. Doulamis;D. Kalogeras;S. Kollias.
IEEE Transactions on Circuits and Systems for Video Technology (1998)
Efficient summarization of stereoscopic video sequences
N.D. Doulamis;A.D. Doulamis;Y.S. Avrithis;K.S. Ntalianis.
IEEE Transactions on Circuits and Systems for Video Technology (2000)
Deep Convolutional Neural Networks for efficient vision based tunnel inspection
Konstantinos Makantasis;Eftychios Protopapadakis;Anastasios Doulamis;Nikolaos Doulamis.
international conference on intelligent computer communication and processing (2015)
An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources
A.D. Doulamis;N.D. Doulamis;S.D. Kollias.
IEEE Transactions on Neural Networks (2003)
Fair Scheduling Algorithms in Grids
N.D. Doulamis;A.D. Doulamis;E.A. Varvarigos;T.A. Varvarigou.
IEEE Transactions on Parallel and Distributed Systems (2007)
A Stochastic Framework for Optimal Key Frame Extraction from MPEG Video Databases
Yannis S Avrithis;Anastasios D Doulamis;Nikolaos D Doulamis;Stefanos D Kollias.
Computer Vision and Image Understanding (1999)
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