What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Operating system
Her primary areas of investigation include Artificial intelligence, Computer vision, Segmentation, Object detection and Image segmentation.
Her Artificial intelligence research integrates issues from Machine learning and Pattern recognition.
Her Pattern recognition study integrates concerns from other disciplines, such as Pixel, Optimal decision and Cluster analysis.
As part of her studies on Computer vision, Rita Cucchiara frequently links adjacent subjects like Robustness.
Her work on Scale-space segmentation as part of general Segmentation research is frequently linked to Research community, thereby connecting diverse disciplines of science.
Her biological study spans a wide range of topics, including Text mining, Speedup, Decision tree and Connected component.
Her most cited work include:
- Detecting moving objects, ghosts, and shadows in video streams (1284 citations)
- Visual Tracking: An Experimental Survey (1202 citations)
- Performance Measures and a Data Set for Multi-target, Multi-camera Tracking (842 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Multimedia.
Her study involves Segmentation, Deep learning, Object detection, Feature extraction and Image segmentation, a branch of Artificial intelligence.
Her work in Computer vision is not limited to one particular discipline; it also encompasses Robustness.
Her Pattern recognition research includes themes of Contextual image classification, Histogram and Cluster analysis.
Her research brings together the fields of Closed captioning and Machine learning.
Multimedia is closely attributed to Annotation in her research.
She most often published in these fields:
- Artificial intelligence (67.72%)
- Computer vision (41.54%)
- Pattern recognition (16.93%)
What were the highlights of her more recent work (between 2015-2021)?
- Artificial intelligence (67.72%)
- Computer vision (41.54%)
- Deep learning (7.48%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Artificial intelligence, Computer vision, Deep learning, Machine learning and Convolutional neural network.
Her research links Pattern recognition with Artificial intelligence.
Computer graphics is closely connected to Hallucinating in her research, which is encompassed under the umbrella topic of Computer vision.
Her Deep learning study combines topics from a wide range of disciplines, such as Parametric statistics, Pixel, Image, Pipeline and Point.
Rita Cucchiara interconnects Visualization, Image segmentation and Set in the investigation of issues within Machine learning.
Rita Cucchiara combines subjects such as Contrast and Salience with her study of Convolutional neural network.
Between 2015 and 2021, her most popular works were:
- Performance Measures and a Data Set for Multi-target, Multi-camera Tracking (842 citations)
- Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model. (224 citations)
- Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking (193 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Rita Cucchiara mainly investigates Artificial intelligence, Computer vision, Machine learning, Convolutional neural network and Deep learning.
Her Artificial intelligence study frequently draws connections between related disciplines such as Pattern recognition.
The Computer vision study combines topics in areas such as State and Identification.
Her research integrates issues of Adversarial system, Resolution, Social group, Generative grammar and Set in her study of Machine learning.
Her research in Convolutional neural network intersects with topics in Exploit, Contrast and Salience.
The concepts of her Deep learning study are interwoven with issues in Probability distribution, Parametric statistics, Feature learning and Code.
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