What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Gian Luca Foresti mainly investigates Artificial intelligence, Computer vision, Feature extraction, Object detection and Video tracking.
His research in Artificial intelligence intersects with topics in Real-time computing, Data mining and Pattern recognition.
His study in Data mining is interdisciplinary in nature, drawing from both Data stream clustering, Pattern clustering, Cluster analysis and k-medians clustering.
Gian Luca Foresti interconnects Smart camera, Machine learning, Image fusion and Biometrics in the investigation of issues within Feature extraction.
The various areas that Gian Luca Foresti examines in his Object detection study include Database index, Edge detection and Information retrieval.
His work in Video tracking covers topics such as Tracking system which are related to areas like 3D single-object recognition, Morphological skeleton and Trajectory clustering.
His most cited work include:
- Trajectory-Based Anomalous Event Detection (364 citations)
- On-line trajectory clustering for anomalous events detection (203 citations)
- Ambient Intelligence: A New Multidisciplinary Paradigm (187 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Object detection are his primary areas of study.
His Artificial intelligence study focuses mostly on Feature extraction, Video tracking, Image processing, Feature and Sensor fusion.
His Video tracking research is multidisciplinary, incorporating perspectives in Tracking system and Real-time computing.
He works mostly in the field of Computer vision, limiting it down to concerns involving Robustness and, occasionally, Algorithm.
His research brings together the fields of Perceptron and Pattern recognition.
Gian Luca Foresti has researched Machine learning in several fields, including Representation, Data mining and Benchmark.
He most often published in these fields:
- Artificial intelligence (66.11%)
- Computer vision (31.88%)
- Pattern recognition (20.81%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (66.11%)
- Pattern recognition (20.81%)
- Computer vision (31.88%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Machine learning.
Artificial intelligence and Residual are commonly linked in his work.
His Pattern recognition research integrates issues from Image and Spatial analysis.
His research on Computer vision frequently connects to adjacent areas such as Baseline.
His studies deal with areas such as Speech recognition, Noise measurement, Direction of arrival and Reverberation as well as Convolutional neural network.
His biological study deals with issues like Feature extraction, which deal with fields such as Discriminative model.
Between 2017 and 2021, his most popular works were:
- Wide-Slice Residual Networks for Food Recognition (75 citations)
- Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition. (33 citations)
- A UAV Video Dataset for Mosaicking and Change Detection From Low-Altitude Flights (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Machine learning, Visualization, Pattern recognition and Algorithm.
His Artificial intelligence research includes themes of Residual and Computer vision.
His biological study spans a wide range of topics, including Mobile agent, Wireless sensor network and Acoustic emission.
When carried out as part of a general Machine learning research project, his work on Support vector machine is frequently linked to work in Association, therefore connecting diverse disciplines of study.
His work carried out in the field of Visualization brings together such families of science as Visual inspection, Feature extraction, Task analysis and Vision based.
His Pattern recognition study integrates concerns from other disciplines, such as Artificial neural network, Image, Narrowband and Reverberation.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
