What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Speckle pattern.
His Artificial intelligence research includes themes of Machine learning, Data mining and Remote sensing.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Liveness and Image.
His Feature, Image resolution, Pixel and Image texture study, which is part of a larger body of work in Computer vision, is frequently linked to Detector, bridging the gap between disciplines.
His Pixel research focuses on subjects like Data compression, which are linked to Multispectral image.
His research integrates issues of Synthetic aperture radar and Algorithm in his study of Speckle pattern.
His most cited work include:
- Land Use Classification in Remote Sensing Images by Convolutional Neural Networks (269 citations)
- Compression of multispectral images by three-dimensional SPIHT algorithm (245 citations)
- Efficient Dense-Field Copy–Move Forgery Detection (147 citations)
What are the main themes of his work throughout his whole career to date?
Giovanni Poggi spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Image segmentation and Segmentation.
His is doing research in Scale-space segmentation, Synthetic aperture radar, Image, Image texture and Multispectral image, both of which are found in Artificial intelligence.
Computer vision is closely attributed to Algorithm in his work.
His studies deal with areas such as Contextual image classification and Transform coding as well as Pattern recognition.
The Image segmentation study combines topics in areas such as Tree, Remote sensing and Image processing.
The various areas that Giovanni Poggi examines in his Segmentation study include Algorithm design, Watershed and Panchromatic film.
He most often published in these fields:
- Artificial intelligence (78.62%)
- Computer vision (48.97%)
- Pattern recognition (43.45%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (78.62%)
- Computer vision (48.97%)
- Pattern recognition (43.45%)
In recent papers he was focusing on the following fields of study:
Giovanni Poggi mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Image and Synthetic aperture radar.
His Artificial intelligence study frequently intersects with other fields, such as Machine learning.
His Pattern recognition study combines topics in areas such as Liveness and Feature.
His Feature study incorporates themes from Histogram and Data mining.
His Image research integrates issues from Discriminative model, Reliability and Feature vector.
Giovanni Poggi interconnects Wavelet transform and Geographic information system in the investigation of issues within Synthetic aperture radar.
Between 2013 and 2021, his most popular works were:
- Land Use Classification in Remote Sensing Images by Convolutional Neural Networks (269 citations)
- Efficient Dense-Field Copy–Move Forgery Detection (147 citations)
- An Investigation of Local Descriptors for Biometric Spoofing Detection (124 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Artificial intelligence, Pattern recognition, Computer vision, Feature and Synthetic aperture radar are his primary areas of study.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Data mining.
Giovanni Poggi has included themes like Fingerprint, Liveness and Noise in his Pattern recognition study.
His work on Filter, Image resolution and Segmentation as part of general Computer vision study is frequently connected to Detector and Block, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Giovanni Poggi interconnects Watershed, Image segmentation, Multispectral image, Morphological gradient and Image fusion in the investigation of issues within Image resolution.
His Segmentation research includes elements of Panchromatic film and Remote sensing.
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