What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Vincent Lepetit focuses on Artificial intelligence, Computer vision, Pattern recognition, Object detection and Pose.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Robustness, Feature extraction, Convolutional neural network, Deep learning and Pixel.
His Computer vision research focuses on Computer graphics and how it connects with Robotics, Robotic arm and Visual servoing.
His Pattern recognition study combines topics from a wide range of disciplines, such as Cognitive neuroscience of visual object recognition, Filter, Contextual image classification, Boosting and 3D pose estimation.
Vincent Lepetit combines subjects such as Image processing, Object-class detection, Classifier and Viola–Jones object detection framework with his study of Object detection.
His studies deal with areas such as Mathematical optimization and Camera resectioning as well as Pose.
His most cited work include:
- BRIEF: binary robust independent elementary features (2306 citations)
- EPnP: An Accurate O(n) Solution to the PnP Problem (1399 citations)
- DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo (1043 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Pose and Object detection.
His study in Object, Image, Robustness, Segmentation and Convolutional neural network is carried out as part of his studies in Artificial intelligence.
His study in Augmented reality, 3D pose estimation, Image processing, RGB color model and Tracking falls within the category of Computer vision.
His research integrates issues of Contextual image classification, Pixel, Cognitive neuroscience of visual object recognition and Feature in his study of Pattern recognition.
His work deals with themes such as Real image and Training set, which intersect with Pose.
The concepts of his Object detection study are interwoven with issues in Video tracking and Classifier.
He most often published in these fields:
- Artificial intelligence (89.31%)
- Computer vision (59.92%)
- Pattern recognition (33.59%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (89.31%)
- Computer vision (59.92%)
- Pattern recognition (33.59%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Pattern recognition, Object and Pose are his primary areas of study.
His study connects Point and Computer vision.
When carried out as part of a general Pattern recognition research project, his work on Similarity is frequently linked to work in Matching, therefore connecting diverse disciplines of study.
In his research on the topic of Object, Homogeneous space is strongly related with Rotation.
His research in Pose intersects with topics in Object detection and Feature learning.
His work carried out in the field of Object detection brings together such families of science as Embedding and Discriminative model.
Between 2018 and 2021, his most popular works were:
- HOnnotate: A Method for 3D Annotation of Hand and Object Poses (42 citations)
- Generalized Feedback Loop for Joint Hand-Object Pose Estimation (27 citations)
- Systems and methods for normalizing an image (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Object, Pattern recognition and Pose.
Artificial intelligence is a component of his Image, RGB color model, Feature extraction, Segmentation and Monocular studies.
His work in the fields of Computer vision, such as Color image and Corner detection, overlaps with other areas such as Event based, High dynamic range and High temporal resolution.
The various areas that he examines in his Object study include Normalization and Rotation.
Vincent Lepetit interconnects Deep learning, Contrast and Translation in the investigation of issues within Pattern recognition.
His Pose research integrates issues from Object detection, Link and Viewpoints.
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