What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Synthetic aperture radar, Computer vision, Radar imaging and Pattern recognition.
Her study on Artificial intelligence is mostly dedicated to connecting different topics, such as Filter.
Her Synthetic aperture radar study combines topics in areas such as Feature extraction and Detector.
Florence Tupin has included themes like Regularization and Interferometry in her Computer vision study.
Her Radar imaging research integrates issues from Image resolution, Speckle noise, Speckle pattern, Contextual image classification and Orientation.
The Pattern recognition study combines topics in areas such as Algorithm and Variable-order Markov model.
Her most cited work include:
- Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights (515 citations)
- Detection of linear features in SAR images: application to road network extraction (379 citations)
- A new statistical model for Markovian classification of urban areas in high-resolution SAR images (328 citations)
What are the main themes of her work throughout her whole career to date?
Florence Tupin mostly deals with Artificial intelligence, Synthetic aperture radar, Computer vision, Pattern recognition and Radar imaging.
Her works in Speckle pattern, Noise reduction, Image processing, Image resolution and Pixel are all subjects of inquiry into Artificial intelligence.
Her Synthetic aperture radar research includes themes of Inverse synthetic aperture radar, Feature extraction, Speckle noise and Interferometry.
Her work on Interferometric synthetic aperture radar as part of general Interferometry research is frequently linked to Phase, thereby connecting diverse disciplines of science.
Her Pattern recognition study integrates concerns from other disciplines, such as Change detection and Image.
As part of her studies on Radar imaging, Florence Tupin frequently links adjacent subjects like Contextual image classification.
She most often published in these fields:
- Artificial intelligence (68.30%)
- Synthetic aperture radar (52.68%)
- Computer vision (46.43%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (68.30%)
- Speckle pattern (21.43%)
- Pattern recognition (29.46%)
In recent papers she was focusing on the following fields of study:
Florence Tupin mainly investigates Artificial intelligence, Speckle pattern, Pattern recognition, Synthetic aperture radar and Computer vision.
Her biological study spans a wide range of topics, including Estimator, Noise reduction, Filter and Image restoration.
Her Noise reduction study incorporates themes from Covariance matrix and Temporal mean.
Her work carried out in the field of Pattern recognition brings together such families of science as Similarity, Detector and Time series.
The study incorporates disciplines such as Algorithm, Tomography and Random field in addition to Synthetic aperture radar.
She interconnects Generalized likelihood ratio and Interferometric synthetic aperture radar in the investigation of issues within Algorithm.
Between 2018 and 2021, her most popular works were:
- Ratio-Based Multitemporal SAR Images Denoising: RABASAR (19 citations)
- A Contrario Comparison of Local Descriptors for Change Detection in Very High Spatial Resolution Satellite Images of Urban Areas (14 citations)
- SAR Image Despeckling Using Pre-trained Convolutional Neural Network Models (11 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Optics
Her primary scientific interests are in Artificial intelligence, Speckle pattern, Pattern recognition, Synthetic aperture radar and Noise reduction.
As part of the same scientific family, Florence Tupin usually focuses on Artificial intelligence, concentrating on Remote sensing application and intersecting with Real image.
Her studies deal with areas such as Algorithm and Estimator as well as Speckle pattern.
The concepts of her Pattern recognition study are interwoven with issues in Image and Filter.
Her Synthetic aperture radar study is concerned with Remote sensing in general.
Her research integrates issues of Logarithm, Image restoration and Convolutional neural network in her study of Noise reduction.
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