What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Luis M. Bergasa mainly investigates Artificial intelligence, Computer vision, Robustness, Object detection and Global Positioning System.
His work on Machine learning expands to the thematically related Artificial intelligence.
His study looks at the intersection of Computer vision and topics like Eye movement with Mobile robot.
Luis M. Bergasa has researched Robustness in several fields, including Road texture, Simultaneous localization and mapping, Tracking system and Color vision.
He focuses mostly in the field of Object detection, narrowing it down to topics relating to Feature extraction and, in certain cases, Optical imaging.
His Global Positioning System research incorporates themes from Inattentive Driving and Simulation.
His most cited work include:
- Real-time system for monitoring driver vigilance (539 citations)
- ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation (411 citations)
- Combination of Feature Extraction Methods for SVM Pedestrian Detection (156 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Robot, Simulation and Segmentation.
His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
Luis M. Bergasa combines topics linked to Simultaneous localization and mapping with his work on Computer vision.
His Simultaneous localization and mapping study integrates concerns from other disciplines, such as Sensor fusion, Global Positioning System and Extended Kalman filter.
His work deals with themes such as Partially observable Markov decision process, Navigation system, Human–computer interaction and Probabilistic method, which intersect with Robot.
Luis M. Bergasa works mostly in the field of Segmentation, limiting it down to topics relating to Convolutional neural network and, in certain cases, Image.
He most often published in these fields:
- Artificial intelligence (75.39%)
- Computer vision (59.16%)
- Robot (19.37%)
What were the highlights of his more recent work (between 2017-2020)?
- Artificial intelligence (75.39%)
- Segmentation (13.61%)
- Computer vision (59.16%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Segmentation, Computer vision, Convolutional neural network and Robustness.
Much of his study explores Artificial intelligence relationship to Machine learning.
His work on Image segmentation as part of general Segmentation research is often related to Field, thus linking different fields of science.
His research investigates the connection between Computer vision and topics such as Wearable computer that intersect with problems in Monocular.
The concepts of his Convolutional neural network study are interwoven with issues in Domain, Image, Grayscale, State and Line fitting.
His research investigates the connection between Robustness and topics such as Parsing that intersect with issues in Panorama.
Between 2017 and 2020, his most popular works were:
- ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation (411 citations)
- Unifying Terrain Awareness for the Visually Impaired through Real-Time Semantic Segmentation. (55 citations)
- Bridging the Day and Night Domain Gap for Semantic Segmentation (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Luis M. Bergasa spends much of his time researching Segmentation, Artificial intelligence, Computer vision, Monocular and Image segmentation.
His Segmentation study incorporates themes from Pixel, Wearable computer, Data mining and Code.
As a member of one scientific family, he mostly works in the field of Wearable computer, focusing on RGB color model and, on occasion, Image processing and Humanoid robot.
His Artificial intelligence research includes themes of Machine learning and Residual.
His specific area of interest is Computer vision, where he studies Field of view.
His biological study spans a wide range of topics, including Robotics, Navigation system and Human–computer interaction.
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