What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- The Internet
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Image retrieval and Object.
Petros Daras incorporates Artificial intelligence and Invariant in his studies.
His work in Computer vision addresses issues such as Set, which are connected to fields such as Soft computing, Motion, Human body and Work.
His Pattern recognition study combines topics from a wide range of disciplines, such as Matching and Representation.
His Image retrieval research incorporates elements of Moment, Multimodal search, Search engine indexing and Information processing.
The study incorporates disciplines such as Similarity and Information retrieval in addition to Object.
His most cited work include:
- A 3D Shape Retrieval Framework Supporting Multimodal Queries (165 citations)
- Evaluating a dancer's performance using kinect-based skeleton tracking (138 citations)
- Real-Time, Full 3-D Reconstruction of Moving Foreground Objects From Multiple Consumer Depth Cameras (130 citations)
What are the main themes of his work throughout his whole career to date?
Petros Daras mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Multimedia and Deep learning.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning.
As part of his studies on Computer vision, Petros Daras frequently links adjacent subjects like Polygon mesh.
Petros Daras combines subjects such as Matching, Histogram and Data mining with his study of Pattern recognition.
His biological study spans a wide range of topics, including Virtual reality, Human–computer interaction, The Internet, World Wide Web and Visualization.
His work deals with themes such as Field and Convolutional neural network, which intersect with Deep learning.
He most often published in these fields:
- Artificial intelligence (51.16%)
- Computer vision (24.13%)
- Pattern recognition (17.15%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (51.16%)
- Computer vision (24.13%)
- Deep learning (11.05%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Computer vision, Deep learning, Pose and Artificial neural network.
His Artificial intelligence research includes themes of Natural language processing, Machine learning and Pattern recognition.
His work on Image segmentation as part of general Pattern recognition research is often related to Generalization, thus linking different fields of science.
His research brings together the fields of Code and Computer vision.
His Deep learning study also includes
- Field, which have a strong connection to Algorithm and Representation,
- Convolution, which have a strong connection to Pattern recognition, Convolutional neural network and Pixel.
His research integrates issues of RGB color model, Object, Silhouette and Rendering in his study of Pose.
Between 2019 and 2021, his most popular works were:
- Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space (9 citations)
- A Comprehensive Study on Sign Language Recognition Methods. (8 citations)
- Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos. (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- The Internet
His primary areas of study are Artificial intelligence, Pose, Computer vision, Artificial neural network and Sign language.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Graph classification and Graph.
His Pose research includes themes of RGB color model, Silhouette, Virtual reality and Rendering.
His work on Ground truth, Monocular and Mixed reality as part of general Computer vision study is frequently linked to Complement, therefore connecting diverse disciplines of science.
His Artificial neural network study integrates concerns from other disciplines, such as Video capture, Sentence, Natural language processing and Convolution.
His biological study spans a wide range of topics, including Speech recognition and Joint.
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