What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Jean-François Lalonde focuses on Artificial intelligence, Computer vision, Computer graphics, Classifier and Image.
His Artificial intelligence research incorporates elements of High dynamic range and Identification.
Jean-François Lalonde undertakes interdisciplinary study in the fields of Computer vision and Probability distribution through his research.
His Computer graphics research includes elements of Method, Object, Pose and 3D single-object recognition.
His research investigates the link between Point cloud and topics such as Unmanned ground vehicle that cross with problems in Segmentation and Tree.
Jean-François Lalonde has included themes like Dynamic range, Leverage, Virtual image, Panorama and Reflection mapping in his Standard test image study.
His most cited work include:
- Natural terrain classification using three‐dimensional ladar data for ground robot mobility (334 citations)
- Photo clip art (258 citations)
- Detecting ground shadows in outdoor consumer photographs (152 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Deep learning, Augmented reality and Ground truth.
His biological study spans a wide range of topics, including Computer graphics and Pattern recognition.
His Computer vision research integrates issues from Reflection mapping and Robustness.
His Deep learning research is multidisciplinary, incorporating perspectives in Lens and Artificial neural network.
His work in Augmented reality addresses issues such as Image formation, which are connected to fields such as Observer.
His Ground truth research is multidisciplinary, relying on both Compositing and Camera resectioning.
He most often published in these fields:
- Artificial intelligence (88.78%)
- Computer vision (72.45%)
- Deep learning (20.41%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (88.78%)
- Computer vision (72.45%)
- Deep learning (20.41%)
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, Augmented reality and Object detection.
Jean-François Lalonde connects Artificial intelligence with Context in his research.
As part of the same scientific family, he usually focuses on Computer vision, concentrating on Robustness and intersecting with Video tracking.
He combines subjects such as Light stage and Image formation with his study of Deep learning.
His study in Augmented reality is interdisciplinary in nature, drawing from both Minimum bounding box and Template matching.
Jean-François Lalonde works mostly in the field of Object detection, limiting it down to topics relating to Convolutional neural network and, in certain cases, Hyperparameter optimization, Image processing, Computer engineering and Black box.
Between 2018 and 2021, his most popular works were:
- Deep Sky Modeling for Single Image Outdoor Lighting Estimation (33 citations)
- Fast Spatially-Varying Indoor Lighting Estimation (29 citations)
- Deep Parametric Indoor Lighting Estimation (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Computer vision, Computational photography, Overcast and Panorama.
His Artificial intelligence study frequently links to adjacent areas such as Pattern recognition.
His Computer vision study combines topics from a wide range of disciplines, such as Representation and Reflection mapping.
His Representation research includes elements of Object and Compositing.
His work focuses on many connections between Computational photography and other disciplines, such as Virtual image, that overlap with his field of interest in Graphics and Standard test image.
His Object detection research incorporates themes from Segmentation, Rendering and Robustness.
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