What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Change detection, Artificial intelligence, Pattern recognition, Computer vision and Pixel are her primary areas of study.
The study incorporates disciplines such as Image resolution, Context and Synthetic aperture radar in addition to Change detection.
Her Multispectral image and Feature extraction investigations are all subjects of Artificial intelligence research.
Her Pattern recognition research includes elements of Contextual image classification and Image.
Her study looks at the relationship between Computer vision and fields such as Resolution, as well as how they intersect with chemical problems.
Within one scientific family, she focuses on topics pertaining to Representation under Pixel, and may sometimes address concerns connected to Context model and Statistical model.
Her most cited work include:
- A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain (304 citations)
- A detail-preserving scale-driven approach to change detection in multitemporal SAR images (301 citations)
- A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment (177 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Artificial intelligence, Change detection, Pattern recognition, Remote sensing and Computer vision.
Her study in Artificial intelligence concentrates on Pixel, Multispectral image, Image resolution, Feature extraction and Image.
Her work in Change detection addresses issues such as Synthetic aperture radar, which are connected to fields such as Backscatter and Similarity measure.
The concepts of her Pattern recognition study are interwoven with issues in Contextual image classification and Multispectral pattern recognition.
Her Remote sensing research integrates issues from Data set and Sensor fusion.
Her Computer vision study incorporates themes from Residual and Identification.
She most often published in these fields:
- Artificial intelligence (65.55%)
- Change detection (51.20%)
- Pattern recognition (43.06%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (65.55%)
- Pattern recognition (43.06%)
- Change detection (51.20%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Artificial intelligence, Pattern recognition, Change detection, Remote sensing and Pixel.
Her work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Remote sensing application.
Francesca Bovolo studied Pattern recognition and Deep learning that intersect with Anomaly detection.
Her Change detection research also works with subjects such as
- Feature together with Multispectral image,
- Synthetic aperture radar which connect with Representation.
The Remote sensing study combines topics in areas such as Bathymetry and Robustness.
Her Pixel research includes themes of Spectral bands and Time series.
Between 2019 and 2021, her most popular works were:
- Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data (14 citations)
- Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding (12 citations)
- Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon (11 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Francesca Bovolo focuses on Artificial intelligence, Change detection, Pattern recognition, Feature extraction and Pixel.
When carried out as part of a general Artificial intelligence research project, her work on Support vector machine is frequently linked to work in Sequence, therefore connecting diverse disciplines of study.
Her Change detection research incorporates elements of Satellite imagery, Image and Machine learning, Leverage.
Her work on Hidden Markov model and Viterbi algorithm as part of general Pattern recognition study is frequently connected to Chart and Pleural line, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Her Feature extraction research is multidisciplinary, incorporating perspectives in Image resolution, Segmentation, Image segmentation, Multispectral image and Feature.
Her Pixel research is multidisciplinary, incorporating elements of Anomaly detection, Deep learning, Feature vector and Identification.
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