What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Computer vision, Motion estimation, Image segmentation and Motion field.
Yiannis Aloimonos has included themes like Machine learning and Natural language processing in his Artificial intelligence study.
The various areas that he examines in his Natural language processing study include Image, Invariant and Key frame.
His Computer vision and Pixel, Segmentation, Object detection, Video tracking and Projection investigations all form part of his Computer vision research activities.
His studies deal with areas such as Translation and Rotation as well as Motion estimation.
His Motion field research also works with subjects such as
- Structure from motion that connect with fields like Image plane,
- Optical flow together with Match moving.
His most cited work include:
- Corpus-Guided Sentence Generation of Natural Images (288 citations)
- Attribute-based transfer learning for object categorization with zero/one training example (123 citations)
- Affordance detection of tool parts from geometric features (123 citations)
What are the main themes of his work throughout his whole career to date?
Yiannis Aloimonos mainly investigates Artificial intelligence, Computer vision, Motion estimation, Motion field and Structure from motion.
Yiannis Aloimonos has researched Artificial intelligence in several fields, including Pattern recognition and Natural language processing.
His study in Computer vision is interdisciplinary in nature, drawing from both Deep learning and Robotics.
His research integrates issues of Motion, Geometry, Translation, Rotation and Computer graphics in his study of Motion estimation.
The various areas that Yiannis Aloimonos examines in his Motion field study include Match moving, Camera resectioning, Image plane, Stereo camera and Observer.
While the research belongs to areas of Structure from motion, he spends his time largely on the problem of Epipolar geometry, intersecting his research to questions surrounding Algorithm.
He most often published in these fields:
- Artificial intelligence (79.11%)
- Computer vision (50.00%)
- Motion estimation (23.29%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (79.11%)
- Computer vision (50.00%)
- Deep learning (6.51%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Deep learning, Artificial neural network and Event.
His Artificial intelligence study incorporates themes from Perception and Natural language processing.
His work deals with themes such as Robot and Tactile sensor, which intersect with Computer vision.
His Deep learning research includes themes of Odometry and Obstacle avoidance.
His Artificial neural network research includes elements of Wavelet and Pattern recognition.
His Optical flow study combines topics in areas such as Epipolar geometry, Algorithm, Flow and Structure from motion.
Between 2017 and 2021, his most popular works were:
- Revisiting active perception. (102 citations)
- Event-Based Moving Object Detection and Tracking (70 citations)
- GapFlyt: Active Vision Based Minimalist Structure-Less Gap Detection For Quadrotor Flight (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Yiannis Aloimonos spends much of his time researching Artificial intelligence, Computer vision, Event, Deep learning and Robotics.
Yiannis Aloimonos combines topics linked to Perception with his work on Artificial intelligence.
The study incorporates disciplines such as Active vision, Structure and Representation in addition to Perception.
He has included themes like Robot, Simultaneous localization and mapping, Robustness and Visualization in his Computer vision study.
Yiannis Aloimonos interconnects Preprocessor, Odometry, Obstacle avoidance, Visual odometry and Feature selection in the investigation of issues within Deep learning.
His Robotics research is multidisciplinary, incorporating elements of Optical flow, Monocular, Unsupervised learning and Task.
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